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Statistics claim there are 8,945 programming languages, yet we bet neither developers nor businesses need all of them to get market traction. As we enter Q1 of 2025, the programming world hasn't changed much; almost every programming languages rating is still shaped by a mix of established languages and modernized classics like Java and Python. However, it has shifted to include innovative tools like AI, blockchain, data science, and IoT. 

To cut through the clutter, we've analyzed the latest trends and compiled a list of the top 10 programming languages for 2025, an overview of different rankings, and the best ideas on choosing a programming language for a project. Read on for success.

Programming Languages Ratings

As of early 2025, some programming language rankings are still based on 2024 data. However, developers, businesses, and educators can leverage PYPL, TIOBE, and GitHub insights to guide learning efforts, hiring strategies, and project development. 

Read all the ranking lists and find the most desired programming language in 2025.

PYPL Ranking 2025

The PopularitY of Programming Language (PYPL) index is a Google-powered snapshot of programming language popularity. It tracks tutorial searches, giving a clear view of which skills are in demand—a crucial tool for tech trend analysis.

As per the PYPL report, 5 five most popular programming languages are: 

The TIOBE Ranking 2025

The TIOBE Index provides a monthly snapshot of programming language popularity. It ranks languages by global developer presence, educational material availability, and industry adoption based on search trends from platforms like Google, YouTube, and Wikipedia. Amazon and Baidu.

According to TIOBE, the five most popular programming languages are:

The GitHub Ranking 2025

GitHub is a global code hub shaped by Fortune 50 companies. While GitHub doesn't provide rankings, tools like GitRanking organize repositories, and DevRank measures developer influence. Here are the dynamics of popularity from the latest GitHub report:

IEEE Spectrum Ranking 2025

From GitHub commits to Stack Overflow threads, this annual roundup analysis cuts through the noise of top programming languages with three crystal-clear metrics: established giants, breakthrough performers, and market demand. Here is the latest technical ranking IEEE report:

RedMonk Ranking 2025

RedMonk ranks programming languages twice a year, blending GitHub code usage with Stack Overflow engagement. They track adoption trends and developer preferences by combining code usage with developer discussions. Over 45% of software companies use these quarterly rankings to guide their technology decisions.

Here are the latest insights from their programming language RedMonk rankings:

Stack Overflow Developer Survey 2025

The Stack Overflow Developer Survey collects data from over 30,000 developers to explore their opinions and habits. It highlights trends in technology usage and preferences, illustrating how new tools and frameworks rise in popularity each year while others fade away.

According to this survey, the five most popular programming languages here are:

What Affects Programming Language Rankings?

Language rankings depend on GitHub stats, Stack Overflow activity, and job demand. Tech giants can elevate a language overnight. Community support ensures good documentation, while technical strengths drive relevance in AI, cloud, and mobile. Overall, enterprise adoption hinges on commercial investment in tools and frameworks.

Why Do Developers Switch Languages?

Market demand and salary potential often trigger language switches. New projects at work may require learning unfamiliar stacks. Some developers migrate when they hit technical walls with their current language. Others switch purely out of curiosity to explore fresh programming concepts. Better developer experience—from cleaner syntax to powerful debugging tools — can seal the deal.

How Do Trends Impact Programming Language Rating?

Tech trends reshape the programming landscape constantly. Cloud computing favors languages that handle concurrency well. Mobile development boosts cross-platform-friendly languages. AI and ML create demand for specialized numerical computing capabilities. Modern software architecture—like microservices and serverless — influences language choice based on performance needs. Open source momentum can make or break a language's trajectory. Universities follow these shifts, molding new developers' skills accordingly.

Why Do Some Languages Rise or Fall in Popularity?

Languages surge when they solve pressing problems—like Rust addressing memory safety. Corporate champions (Google with Go, Microsoft with TypeScript) accelerate adoption through resources and integration. Growing ecosystems create network effects, attracting more developers and tools. Languages decline when they stagnate or lose their edge to newer alternatives. Yet some persist through enterprise inertia, particularly in legacy systems. Success hinges on balancing innovation with stability while nurturing a vibrant developer community.

Top 10 Most Popular Programming Languages in 2025 

From crunching data and financial management apps to renewable energy systems, optimizing solar panel efficiency, and reducing carbon footprints—programming spans different fields of humankind. 

But here's the million-dollar question: with so many programming languages out there, which ones are worth learning? We turn to the database of the most desired and loved programming languages from Stack Over Flow. Here is the list of the top 10 below:

1. Python

With clean syntax and broad applications, Python is a go-to language for web development, AI, and data science. Supports procedural, object-oriented, and functional programming, consistently placing it at the top of programming language rankings.

Pros:

• Easy to Learn: Simple syntax makes it beginner-friendly.
• Extensive Libraries: Large collection of standards and toolkits available.
• Integration: Works well with languages like C and C++.
• High Demand: Especially among startups.

‍Cons:

• Slower Speed: Slower compared to compiled languages.
• Limited Concurrency: Global Interpreter Lock (GIL) limits true parallelism.
• High Memory Use: Consumes more memory than some alternatives.
• Complex Debugging: Dynamic typing can complicate the debugging process.
• Poor for Certain Applications: Less suitable for mobile, embedded, and real-time systems due to efficiency concerns.

Common Use Cases of Python:

Thanks to TensorFlow, Python excels in data science and web development, particularly in AI and ML. From Instagram to Dropbox, it powers a wide range of applications. We bet that in years ahead, we'll still see Python as the number №1 solution, ranked in most ratings.

2. JavaScript

Among all the most popular programming languages, JavaScript remains the "default" one for web developers. Born for the browser, JavaScript now dominates both frontend and backend, thanks to Node.js and a massive npm ecosystem. In 2025, it will continue to dominate as a leader on GitHub in terms of code volume.

Pros:

• Versatile & Rich Ecosystem: Works for front-end, back-end (Node.js), and mobile, with a vast array of frameworks and libraries (React, Angular, Vue).
• Client-Side Responsiveness: Executes directly in the browser, reducing server load and improving user experience.
• Interactive Web Pages: Enables dynamic features, animations, and real-time updates.
• Large, Active, and Supportive Community: Ensures ample resources and solutions.

Cons:

• Security Vulnerabilities: Client-side code is visible and susceptible to attacks (XSS).
• Inconsistent Browser Behavior: Code may render differently across browsers.
• Performance Limitations: Heavy client-side JavaScript can impact loading times.
• Debugging Complexity: Client-side debugging can be challenging.
• Single-Threaded Constraints: Limits performance for complex, concurrent operations.

Common Use Cases of JavaScript:

JavaScript powers web development with React, Angular, and Vue.js for interactive interfaces and single-page apps. Node.js extends its reach to server-side development, real-time apps, and microservices.

3. TypeScript

TypeScript is a Microsoft-backed superset of JavaScript that adds static typing and class-based object-oriented programming. It compiles to JavaScript and runs anywhere JavaScript does. Strong type safety and advanced tooling make it a top choice for large-scale development. In 2025, it will remain in the first position of top programming languages.

Pros:

• Type Safety: Static typing helps catch errors during development rather than runtime.
• Enhanced IDE Support: Better code completion, refactoring, and error detection in development tools.
• JavaScript Compatibility: Can use all JavaScript libraries while adding type safety.
• Enterprise-Ready: Perfect for large codebases and team collaboration.
• Improved Code Quality: By catching errors early, TypeScript reduces the likelihood of bugs in production.
• Better Collaboration: Type annotations make code more self-documenting, making it easier for teams to understand and work on the same codebase.
• Scalability: TypeScript is particularly well-suited for large and complex applications.

Cons:

• Type Definition Files: Some libraries may lack or have outdated type definitions.
• Complexity: Initial installation and setup might be a bit tricky.
• Development Time: Writing types can slow down initial development speed.
• Compilation Step: TypeScript code needs to be compiled into JavaScript before it can be executed.
• Increased Code Size: Type annotations can increase the size of your code.
• Runtime vs Compile time: while Typescript catches many errors at compile time, it does not guarantee that there will be no errors at runtime.

Common Use Cases of TypeScript:

It's the default for Angular and powers tools like Visual Studio Code and Slack. Its compatibility with Node.js makes it a top choice for full-stack development, prioritizing type safety.

4. Rust 

Rust, developed by Mozilla Research, delivers memory safety, high speed, and a growing ecosystem. Its ownership model eliminates memory vulnerabilities without a garbage collector. Adopted by startups and major tech firms, Rust is a rising star that won't stop raising its popularity for performance-critical development.

Pros:

• Memory Safety: Catches errors before runtime, not during production crashes.
• Performance: Matches C/C++ speeds with higher-level abstractions.
• Cargo: A modern package manager that just works. 
• Cross-Platform: Write once, deploy anywhere approach.
• WebAssembly Ready: First-class support for the web's low-level powerhouse.

Cons:

• Learning Cliff: Ownership and lifetimes concepts demand serious study.
• Unforgiving Compiler: The "fighting the borrow checker" experience is real.
• Build Times: Complex codebases can test your patience during compilation.
• Young Ecosystem: Some specialized tools are missing in the standard of mature languages.

‍Common Use Cases of Rust:

Rust shines in critical infrastructure: Firefox's core engine, Discord's real-time systems, and cutting-edge blockchain platforms. It's the go-to choice for projects where failure isn't an option—embedded systems, operating system kernels, and game engines pushing performance boundaries.

5. Go (Golang)

Go, developed by Google, is a high-performance language known for simplicity, efficiency, and scalability. In 2025, Go continues to rise with native scaling and a lightweight yet productive design, often chosen over Java or C# for backend and DevOps. While Rust challenges C++, Go is a streamlined alternative for modern software teams. Consequently, in recent years, it frequently tops each rating of programming languages.

Pros: 

• Simplicity: Clean and straightforward syntax, easy to learn. 
• Concurrency: Excellent built-in support for goroutines and channels. 
• Performance: Fast compilation and execution speeds, comparable to C++. 
• Garbage Collection: Automatic memory management, reducing the risk of memory leaks. 
• Static Typing: Catches type-related errors at compile time. 
• Standard Library: Comprehensive and well-documented standard library. 
• Cross-Compilation: Easy to compile for different platforms. 

Cons: 

• Lack of Generics: Limited code reuse for different data types (now addressed but still evolving). 
• Verbose Error Handling: Requires explicit error checking, which can lead to verbose code. 
• Limited Expressiveness: Some developers find the language less expressive than others. Dependency Management: Older dependency management was considered cumbersome (now improved with modules). 
• Smaller Ecosystem: While growing, the ecosystem is smaller than more established languages.

‍Common Use Cases of Go:

Go is widely used in cloud-native development, web development (server-side), and network programming. It's popular for building microservices, distributed systems, and command-line tools. Companies like Google, Docker, and Kubernetes utilize Go for their infrastructure and tooling.

6. C#

C# is a modern, object-oriented coding language developed by Microsoft as part of the .NET platform. It blends the robustness of C++ with the simplicity of Visual Basic, featuring strong type safety, versioning, and scalability support. In 2025, C# will remain the strongest language in the Microsoft ecosystem and seems not to lose its popularity.

Pros:

• Type Safety: A strong static typing system prevents common runtime errors and improves code reliability.
• Rich Ecosystem: Extensive standard library and NuGet package ecosystem for nearly any functionality.
• Cross-Platform: .NET Core/.NET 5+ enables true cross-platform development and deployment.
• Performance: High-performance capabilities, especially with recent memory management and compilation improvements.
• Modern Features: Pattern matching, nullable reference types, LINQ, and continuous language evolution.

Cons:

• Platform Dependencies: While cross-platform, some libraries still have Windows dependencies.
• Resource Usage: Higher memory footprint compared to lower-level languages.
• Compilation Required: Must be compiled before execution, adding a build step.
• Learning Curve: Complex features and patterns can be challenging for beginners.
• Verbose Syntax: More ceremonial code compared to dynamic languages.

Common Use Cases of C#:

C# is widely used in enterprise software development, particularly in the Microsoft ecosystem. It powers everything from ASP.NET web applications to Unity game development, Windows desktop applications, and cloud services on Azure. 

7. C/C++

C and C++ pioneers of modern computing remain unmatched in hardware control. C provides procedural efficiency while C++ adds object-oriented features; both deliver raw performance critical for systems development.  In 2025, it remains first among the 10 top programming languages.

Pros:

• Zero-Overhead Abstraction: Efficient execution with direct hardware access.
• Precise Memory Control: Fine-grained resource management without a garbage collector.
• Cross-Platform Compatibility: Runs on diverse architectures and operating systems.
• Extensive Legacy Codebase: Decades of existing libraries and software.
• Deep System Integration: Ideal for OS development, embedded systems, and performance-critical applications.

Cons:

• Manual Memory Management: Prone to leaks and undefined behavior.
• Complex Syntax: Challenging constructs like C++ templates.
• Slow Compilation: Long build times for large projects.
• Security Risks: More exposure to vulnerabilities like buffer overflows.
• Steep Learning Curve: Requires deep understanding of low-level concepts.

Common Use Cases of C/C++:

These languages drive OS development, game engines, embedded systems, real-time applications, and high-performance computing, including Chrome's V8 and major databases.

8. R

R is an open-source language for predictive analytics and data visualization, built with C, C++, Fortran, and R. It features S3 and S4 frameworks for flexible data modeling and method dispatching, which allows for flexible data modeling and method dispatching.

Pros:

• Statistical Analysis: Built-in capabilities.
• Visualizations: Publication-ready.
• Packages: Extensive scientific libraries.
• Math Operations: Advanced support.
• Community Support: Favorite among researchers.

Cons:

• Big Data Performance: Struggles with big data performance.
• Memory Usage: Can spiral quickly.
• Syntax Complexity: Unique syntax challenges newcomers.
• Package Quality Variance: Package quality varies wildly.
• Limited Use Cases: Beyond data analysis.

Common Use Cases of R: 

R dominates biotech labs and research institutions. Companies like Pfizer and Merck crunch clinical trial data while hedge funds model risk. Academia relies heavily on R for peer-reviewed research and statistical validation.

9. Java

Java is a high-level, object-oriented language known for its platform independence. With ongoing improvements, including syntactic enhancements and advancements in GraalVM and WebAssembly, Java is expanding beyond traditional backend development to stay competitive.

In 2025, Java will steadily retain its leadership in the top 10 programming languages as a workhorse for enterprises and large high-load systems. If you plan to work with large corporations, the banking sector, or any system that requires stability, Java is an excellent choice.

Pros:

• Platform Independence: Code runs anywhere via the JVM.
• Rich Ecosystem: Extensive libraries and frameworks like Spring and Hibernate.
• Concurrency Support: Built-in multithreading enhances performance.
• Security Features: Strong protections make it ideal for web and enterprise applications.
• Community Support: A large user base ensures abundant resources and tools.

‍Cons:

• Verbose Syntax: Requires more code compared to modern languages like Python.
• Performance Overhead: Slower than lower-level languages due to JVM interpretation.
• Memory Intensive: Consumes more memory than C or C++.

Common Use Cases of Java: 

Java is everywhere—from Google's backend to Fortune 500 banks. It's vital for Android, Spring, and high-load systems, and with cloud innovations, it continues shaping enterprise tech, big data, and finance.

10. PHP

Once a simple scripting language, PHP now drives much of the web, including WordPress and Facebook. Modern frameworks like Laravel have refined it, making PHP more efficient and scalable than ever.

‍Pros:

• Web Integration: Excellent integration with HTML and web servers.
• Easy Deployment: Simple setup and hosting requirements.
• Large Community: Extensive documentation and community support.
• Framework Ecosystem: Modern frameworks like Laravel provide robust development tools.
• Cost-Effective: Wide availability of hosting and developers.

Cons:

• Inconsistent Design: Historical inconsistencies in function naming and parameter ordering.
• Security Concerns: Easier to write insecure code if not following best practices.
• Type System: Weak typing can lead to subtle bugs (though improving with recent versions).
• Performance: Not as performant as compiled languages for complex applications.
• Legacy Code: Many older codebases use outdated practices.

Common Use Cases of PHP: 

PHP continues to dominate CMS, e-commerce, and traditional web development. Ideal for small to mid-sized projects, it remains the backbone of WordPress, Wikipedia, and countless content-driven platforms.

How to Choose a Programming Language for a Project?

While language wars rage on Twitter, programming languages ratings "hustle-and-bustle with numbers," successful tech leads know it's not about which language is "best" – it's about which one serves your specific mission. Let's delve into tips on how to figure out what programming language suits your business best.

Project Type

• Identify the target platform (e.g., web, mobile, desktop, or multi-platform).
• Define the application's purpose and user environment.

Language Features

• Evaluate performance, functionality, and built-in capabilities.
• Assess support for modern coding paradigms (e.g., functional, object-oriented).

Ecosystem

• Explore the availability of tools, plugins, and development environments.
• Investigate the breadth of open-source contributions and ecosystem maturity.

Scalability

• Analyze how well the language supports large-scale applications.
• Consider its ability to handle increased user loads or data growth efficiently.

Learning Curve

• Determine how intuitive the language is for new developers.
• Assess the time required to become proficient compared to alternatives.

Compatibility

• Verify how seamlessly it integrates with existing systems and APIs.
• Check for cross-platform support and interoperability with other technologies.

Industry Demand

• Research hiring trends and industries actively using the language.
• Examine its prevalence in job postings and developer surveys.

Support

• Review the quality and availability of official documentation.
• Look into active communities, forums, and professional support channels.

Future Trends

• Monitor the language's adoption of cutting-edge technologies (e.g., AI, blockchain).
• Analyze its trajectory in developer rankings and long-term viability.

Here is a detailed overview of the area domain and programming language features and connections:

Don't Fall Behind With the Wrong Programming Language

In most cases, when it comes to real development projects—it's not just about code but performance, scalability, and future growth. Checking the most popular programming language ratings ensures a wise, future-proof decision. Choose a language that fits your needs, leverages your team's expertise, and keeps you ahead of the curve. With Darly Solutions, you're always one step ahead.

Backed by deep industry knowledge and hands-on experience, we help businesses navigate the ever-changing tech landscape. Whether launching a startup or scaling an enterprise, don't let the wrong decisions hold you back—let's build for the future together.

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.

Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.

The mobile apps market is rapidly growing. According to Grand View Research, the global mobile app market was evaluated at approximately USD 253 billion in 2023 and is going to grow with a 14.3% CAGR by 2030. That’s why the need for qualified mobile development specialists is so relevant today. 

At the same time, one of the key features of a competent specialist is knowledge of the software development life cycle (SDLC) and which of the existing models is best to use in a specific project. The following article will overview the most popular SDLC models and explain where to apply them for the best results. 

What is the Software Development Life Cycle (SDLC)?

The software development lifecycle is a methodology that guides the development team during the app creation process. Each of the development life cycle phases has its aims, timeframes, and challenges. Yet, a systemized approach toward software development ensures that the final product is of the highest quality. 

In comparison, there are other approaches to software development. For example, development teams can choose from the following options: 

• Application lifecycle management. It has a broader scope outside of the actual development process. It can include several SLDC development life cycle stages inside. 
• Software testing lifecycle. This method emphasizes debugging and elimination of possible errors in the product. 
• Product development lifecycle. This strategy includes market and product promotion after its development is complete. 

What Is the Purpose of the Software Development Life Cycle (SDLC)?

The SDLC’s clear division into development cycle stages ensures that software is developed in a controlled environment. The systematic approach helps to avoid such challenges as: 

• Budget breaches.
• Project overruns and crunches 
• Failure to meet the requirements 
• Expired deadlines

Creative freedom is important during the software development process. Yet, the SDLC approach ensures that all the creativity and revolutionary solutions are channeled constructively. 

While following the life cycle of software development, each team member knows what task they should do and its deadline. 

What are the 7 Phases of SDLC? 

There are seven common SDLC phases: 

1. Requirements analysis 
2. Planning
3. Design 
4. Development
5. Testing
6. Deployment
7. Maintenance

Let’s consider them in more detail.

Requirements Analysis 

It is the initial stage at which the developer's team evaluates the required features for the app. It defines how long the app will be in development, the budget’s size, and the deadlines. 

This step answers such questions as: 

• What is the objective of the app? 
• Is it an upgrade or a brand-new software? 
• What are the expected results? 

Planning 

The project can be planned after its goals and desired results are established. Planning in SDLC defines the technological stack and project roles. The project roadmap is also developed during the planning phase of SDLC. 

Design 

The software blueprint is created during the design stage. Ideas turn into features, architecture, and UI/UX design. Designing also includes consideration of the software's platforms and security as a part of SDLC processes. 

Design can undergo several iterations before reaching its final look. During this phase, teams usually create a software prototype to see how all the features work together. This phase concludes with design document specification — a text description of the app’s functionality to share with stakeholders and within the team. 

Development 

Development is the long-lasting of the SDLC steps. During this stage, the team implements planned features and functionality into actual software. Tools for progress tracking and version control, such as Git, are common in the software development stage.

Testing 

Testing comes after the implementation phase in SDLC. It is a rigorous process during which all software elements are evaluated. Testing can include: 

• Performance testing
• Security testing
• Integration testing
• Alpha and beta testing, and so on 

The result of the testing and debugging software development cycle phases should be a product meets its specifications and is ready for the commercial environment. 

Deployment 

Usually, the apps undergo testing and development in a secluded, special environment. Deployment implies transferring the software to the commercial space. The deployment phase includes final testing of the working product and gathering feedback from a limited audience. 

The product is ready for full-fledged commercial use when the software development stages are over. 

Maintenance 

Maintenance is the final stage of the SDLC cycle. It includes software updates, security, and performance optimization. An upgrade or upscale of the software may be required in the future. In this case, the SDLC begins from the first stage, and the major update undergoes all the steps mentioned. 

Most Common SDLC Models 

The SDLC standards are common across the industry and development teams. However, there are different SDLC models. Each model serves its own purpose and offers a different order in which the different phases are performed.

SDLC Agile Model 

The Agile SDLC model is the hallmark of modern industry. This model implies dividing the bigger tasks into smaller chunks and working on their implementation. The Agile method emphasizes communication between cross-functional teams and stakeholders. 

Agile also implies frequent feedback gathering and analysis. Rigorous quality control is also an integral part of the Agile model. 

SDLC Big Bang Model

The Big Bang model is an almost informal approach to the SDLC cycle. It ignores any planning or assessment and jumps straight into the coding. Such an approach is more risky than others. However, the model works best if delivery time is critical. 

The Big Bang model can work for you if you wish to experiment or test waters in new markets. 

SDLC DevOps Model

DevOps is a combination of development (Dev) and operations (Ops) teams to streamline the delivery of software products. The combined practices require service providers of highly skilled specialists. The main DevOps principles are continuous integration and delivery at the enterprise software development level. These methods help automate the process and streamline management tasks. 

The DevOps model can strain the budget. However, the result is worth the cost. The DevOps SDLC cycle ensures that the product is in a deployable state and can be swiftly modified. 

SDLC Iterative Model

An Iterative model is another representative SDLC cycle. Unlike the Agile model, iterative SDLC does not require continuous customer involvement. Besides, the iterative model has a more defined structure. 

The Iterative model implies iterating the software and making changes between the cycles. Such an approach works well for expanded teams and allows the desired results to be achieved without failing deadlines or breaching the budget. 

SDLC Lean Model

The Lean model is one of the oldest SDLC cycles that has persisted until today. It aims to achieve maximum efficiency in resource management. Unlike Agile, it focuses on the highest possible quality instead of close and consistent communication with the client. 

It also takes a broader than Iterative model approach and improves the overall development process rather than the product itself. Lean is more philosophy than a precise model. As an SDLC model, Lean will ensure the quality of the product and client satisfaction before everything else. 

SDLC Spiral Model

The Spiral model is a combination of Iterative and Waterfall models (this one will be discussed below), taking the best features from both approaches. The model consists of spirals, and each spiral has the following steps: 

• Planning 
• Risk analysis 
• Development 
• Testing

Such an approach allows risk mitigation at the early stage of the project. While being time-consuming, the Spiral model can be a fail-proof net for your project. 

SDLC Waterfall Model

If you are looking for something straightforward and easy to manage – the Waterfall model is your choice. It takes the SDLC structure at face value and follows each step consecutively. The Waterfall model has its risks. It is rigid, and if changes or feedback appear in later stages, it will be hard to implement them. At the same time, the Waterfall model struggles with scalability. 

On the other hand, it can spare the headache of complex management and task delegation. 

Conclusion 

Software development is a complex process that requires structure and precision. The Software Development Lifecycle provides such a structure. In seven steps, a software development team can clearly see goals and meet the client’s requirements. 

Various SDLC models fit different needs and project scopes, and we know how to work with all of them. If you need specialists for custom software solutions, we are ready to answer your call and create a high-quality product. Contact us to discuss your project in detail.

In the design and operation of digital products, it is essential to detect errors and ensure that these products fully comply with specifications (both generally accepted and defined by their owners). For example, in the field of healthcare, using such solutions directly impacts the health and life of patients and the level of efficiency of medical services. So, let’s delve into this topic. 

Objective of Quality Assurance in Healthcare App Development

So, let’s find out why QA is important. In general, it is one of the integral parts of software development, especially in the medical field. After all, in the field of medical services, the fate of the patient's life and health is decided every day. Digital solution that looks and operates as intended (of course, thanks to the quality assurance benefits) provides the ability to prevent all potential errors as efficiently as possible and ensures that applications meet modern medical standards. 

Regarding the importance of quality assurance in healthcare applications, the following points can be distinguished:

• Safety of patients: At the heart of QA in healthcare software development is patient safety. The timely testing of healthcare applications that could impact a patient's health is a critical aspect. This includes verifying medicine dosage, ensuring the accuracy of diagnostic algorithms, and the functionality of the patient monitoring system. Any malfunction, even for a brief period, can lead to serious errors, underscoring the importance of reliability assurance.
• Data privacy: The guarantee of absolute protection of patients' personal data from unauthorized access. Due to their specificity, medical applications regularly process a huge amount of information, so the introduction of modern encryption methods is critical.
• Compliance with regulatory requirements: A number of modern healthtech standards like HIPAA and GDPR are created for a reason. Therefore, absolutely all software should be developed to meet these requirements. Civilized countries actively implement official app development rules. If these laws are ignored, it is undoubtedly a direct path to legal accountability, with all the attendant consequences. The implementation of reliable digital solutions reduces the chances of getting a fine or a lawsuit until the loss of a license.
• Optimization of regular processes: The higher the efficiency of the application, the better the performance and accuracy of work within the medical professional team. In this way, medical staff can access the necessary information, thus minimizing the time spent on data processing. This guarantees both speed and high accuracy of decision-making regarding the further treatment of patients.

Risks Associated with Poor Software Quality in Healthcare

No one will argue that some medical institutions ignore the risks of using legacy software that doesn’t meet the modern healthtech regulations. However, this is a very serious and critical topic that can significantly impact patients' safety and lives and the effectiveness of daily processes.

Numerous errors, incorrect diagnoses, and, as a result, incorrectly prescribed treatment all directly threaten the patient's life and health. Also, do not exclude the occurrence of legal and reputational problems (fines or a prominent indicator of loss of confidence on the part of the public, both patients and employees of a particular medical institution). Unreliable digital solutions automatically complicates the interaction between employees. It leads to a decrease in the productivity of daily medical practice because it complicates the possibility of uninterrupted access to all necessary data and the exchange of information between departments and specialists.

Types of Testing in Healthcare Applications Development

There are different types of healthcare application testing, but the most common and popular among them are the following types:

• Performance testing: It includes analyzing the application's speed of action and navigation, taking into account different levels of load.‍
• Regression testing: It defines how much each of the module functions meets the requirements.‍
• Security testing: It implies checking the protection of each patient's data from illegal access by unauthorized users.‍
• Usability testing: This example of quality assurance in healthcare is designed to evaluate how user-friendly the application is, ensuring it is convenient for both healthcare industry employees and patients of all ages.

Healthcare app testing features are primarily critical, including the need for strict compliance with regulatory requirements and safety regulations.

The Future of Quality Assurance in Healthcare

Now, knowing the answer to the question: “What is quality assurance in healthcare?”, let’s talk about its future. Specifically, today, we can define three the most sustainable trends:

• Implementation of AI and ML-based solutions aimed at automating the testing process, independently identifying bugs, and preventing them in advance;
• Combination of DevOps and Agile, which ensures closer interaction between development and testing teams, ultimately reducing the number of bugs and speeding up their fixing;
• Using cloud-based testing platforms that provide scalability of QA healthcare scenarios without compromising the budget.

However, in the coming years we can expect something more revolutionary than the actualization of the above-mentioned directions since the healthtech sector is actively invested in by both governments and private institutions.

Conclusion

Now, you know the answer to the question: “What is quality assurance?” and can understand the role of QA in software testing – generally speaking, it is crucial regarding ensuring patient safety and the performance of the healthcare delivery process. Minimizing risks during the medical quality assurance process and creating reliable medical applications are possible only through the use of modern technologies and compliance with all official standards provided by a qualified service provider.

If you have a great idea, and it involves creating something innovative, aiming at solving standard problems using unconventional methods and tools, it’s probably worth bringing it to life gradually. This way, you will move from priority features to secondary ones, relying on real users’ opinions – both positive and negative ones. Actually, here, we described the essence of the MVP development process. Below, we will explain its definition and check its benefits and specifics of implementation. 

Understanding MVP in Software Development

If you are going to build an MVP, you should first understand exactly what it is.

In a nutshell, a minimum viable product (MVP) is a product that has basic functionality to satisfy some needs of the target audience. Its main goal is to attract first users, determine their impression of interacting with it, and think through a further strategy for its development.

Key advantages of an MVP approach

When you decide to build MVP, you get the following benefits:

• Ability to confirm or refute hypotheses about the product
• Attracting investors through accelerated confirmation of the idea’s viability
• Help in analyzing the target audience
• Reduction of financial risks
• Development costs reduction thanks to proper prioritization of the product’s features
• Faster engagement of the target audience, long before the launch of a finalized product version
• Early bug fixing
• Reduced time to market

Thus, by resorting to MVP building, you can start to earn faster. At the same time, the development of a minimum marketable product is especially relevant for startups as it allows you to find out the real needs and pain points of your target audience in practice.

Developing Your MVP: Focus on Core Functionality

Now, let's find out what the MVP process looks like.

Identify your target audience

No matter how universal your solution is, its audience still has something in common, be it goals, pain points, demographics, or something else. Therefore, before you hone your product concept, you will have to define who it will be intended for – that is, conduct user research. To do this, you need to draw up a detailed portrait of the client – gender, age, place of work, hobbies, income level, desires, needs, problems, etc.

Come up with core functionalities

Surely, you have already seen a picture on the Internet that reflects the essence of MVP – a skateboard gradually turning into a car. Formally, this means that you need to understand which functions will be fundamental, that is, without which no user will be able to implement the task assigned to your product. These will be your MVP requirements. Then, as you receive reviews from real users, you can add features of less priority that make your solution more usable than the previous version.

Prioritize correctly

Before you start to develop MVP, think about which stages of your project are the most resource-intensive – they will need to be implemented first. As resources become available, you can assign lower-priority tasks to individual members of your team. Actually, this prioritization will determine your product roadmap. Thanks to this approach, you will eliminate downtime in your work processes and will be able to bring the MVP software launch date closer.

Define tools and resources

Finally, you should determine what tools you will use in development and what resources will be needed for this. We are talking about programming languages, wireframing and software development tools, platforms for project management, business messengers, etc.

Iterating Based on User Feedback

Any MVP strategy consists of iterations – that is, constant repetition of the development cycle based on user feedback received after testing its previous version. Let's look at the nuances of implementing these iterations.

Data collection methods

Depending on the resources available, you can collect data from real users by applying different methods. This could be live interviews, questionnaires, A/B product testing, supervised and unsupervised testing, and so on.

Feedback analysis and prioritization

Once the data has been collected, you will need to identify which reviews should be processed first and which ones can be postponed. In fact, everything is simple: the highest priority belongs to those that indicate that the user cannot perform the target action in your solution at all. Second in priority are reviews related to the complexity of the functionality – that is, those in which users claim that they did not immediately figure out what to click, or that they had to spend too much time to perform a simple operation. Everything else that does not affect the accessibility and simplicity of functionality can be optimized later, in subsequent updates.

The importance of iteration

Since building MVP for startups involves creating a primitive version of a full-scale solution, most likely, you will have to deal with a lot of negative feedback. Some will not like the typography, some will find the navigation inconvenient, and some will not even understand what actions need to be performed to achieve the goal. This is okay. At the same time, those who leave you reviews now are likely to use your solution after its optimization, too – therefore, in order not to lose their loyalty, you should constantly improve it. This will show your audience that you appreciate their opinion and that they also contribute to your project.

Launching Your MVP: Getting It Out There

Each iteration that brings your MVP closer to its finalized version must fulfill a specific business task – this could be confirming or refuting hypotheses, testing functionality, attracting users, choosing the best version from several proposed ones, identifying competitive advantage, etc.

This means that with each new iteration, you may have to take different approaches to each stage of the development cycle, from MVP planning, as well as choosing development tools and startup methodology, to testing and determining methods for collecting user feedback. Ultimately, this flexibility will open up further development prospects for your minimum viable prototype and reduce the risks of its possible failure.

Conclusion

Generally speaking, the benefits of creating an MVP from a business point of view cannot be overestimated. This approach helps reduce the risks of failure, attract investors, ensure a more moderate expenditure of the project budget, evaluate its competitiveness, and much more. In addition, it allows you to quickly enter the market because instead of the time-consuming development, you can spend less time creating its primitive version and, thus, more quickly test its viability and prospects in practice.

If you are just considering the possibility of implementing your business idea in MVP format, feel free to contact us. We will take on all stages of its implementation, from concept approval to usability testing and launch, and will repeat iterations until it transforms into a full-fledged product.

With more than 2.7 billion smartphone users across the world in 2020, it’s no surprise that the mobile app industry is expended with no signs of stopping. Nowadays, smartphones have become a useful and convenient instrument that offers multiple benefits such as communication through emails and social networking, web surfing, entertainment, GPS and various other productivity apps.

In fact, studies show that the average American checks their phone every 12 minutes. 10% of people check their phones every four minutes.

We use our phones every day at work, at home, on the street, in bed, while we are eating, in our cars. You might be reading this article from a mobile device right now.

What are people doing on their phones? Well, 90% of mobile time is spent on apps.

According to these facts, many companies considering mobile apps are facing a common challenge — which app development approach to choose? Should we build a native, mobile web, or hybrid app? More importantly, which is the most appropriate app type for you and your business?

Let’s find out what are native, mobile web, and hybrid apps, analyze the differences between them, as well as the advantages and disadvantages of each. After reading, you will be able to understand which approach will be the right one for your business idea.

Web Apps

A web app is a software program that uses web technologies like HTML5, CSS, JavaScript. It exists entirely within browsers such as Safari and Chrome and is essentially a mobile website. It is therefore not possible to download them from an app store.

Also, the web app is not connected with the device’s operating system and does not use its storage. Unlike an iOS or Android app, there is no software development kit (SDK) for a developer to work with. There are templates and frameworks for developing web apps like Angular, React, and Vue.js that you can use to get a quick start.

So, here are some pros of web application

• Developing a web app can be simple and quick. So, web apps are a cost-effective way to put your product in the hands of a lot of users.
• Web apps can be built for all platforms as long as they can run in an appropriate web browser.
• The user doesn’t have to manage updates manually. The newest version always loads when a user opens a web app.

And some cons:

• Searching for an app might be difficult because the user doesn’t find it in any app store.
• A browser is required to run a web app. It means that users have to take more steps to use a web app, whether that’s searching for the page or typing in a URL.
• Web apps can be slower and lack a lot of benefits. They only work online and they don’t have access to as many phone features like push notifications, contacts, etc.

However, as browsers and web apps become more advanced, PWAs (progressive web apps) offer a wider functionality like:

• Sending push messages
• Using some touch screen elements
• Using some of the device’s hardware

So, If your startup is on a budget and doesn’t require complex functionalities or access to operating system features, then building a web app can be the most suitable solution.

Native Apps

A native app is a software application that is built for specific platforms and is written in languages that the platform accepts. For example, Swift and Objective-C for native iOS apps, Java or Kotlin for native Android apps and C# for the most part for Windows Phone apps. Native apps are also built using the specific Integrated Development Environment (IDE) for the selected operating systems.

Advantages of the native application:

• Native apps offer the fastest, most reliable and most responsive experience to users.
• Good integration with the hardware of the device (e.g. camera, GPS, phone, touch screen)
• It comes with the push notification functionality.
• Great design and user experience possibilities. For example, the implementation of sophisticated UI/UX design and animation.
• These apps can be downloaded from app stores and installed on mobile devices, where they can be placed as a designated icon on the home screen.
• Native apps are also more suitable for working offline.

Disadvantages:

• Need more than one codebase. iOS apps will not run on Android and the opposite, so you will have to work with different codebases for every platform you choose to build in.
• Native apps are usually more expensive in developing, especially for companies that need apps on multiple OS platforms. For both platforms, it will require two separate developers or teams.
• A lot of time is spent on development. Also, it takes time to update the app for different platforms.
• Require constant updates to keep with the current OS versions.
• Expensive to keep your native app updated
• Both Apple App Store and Google Play get a 30% commission from every in-app payment.

To sum up, a bigger budget is required if you want to build your app for multiple platforms (i.e. iPhones and Android) and to keep your native app updated. Native apps are not the best option for simple applications.

By offering a great user experience, better performance and accessibility, native apps are able to offer users a more personalized product.

Hybrid apps

Hybrid apps combine the power of native and web apps to reap the benefits of both. Actually they are made, so they look and feel like native applications, but like web apps, are built with JavaScript, HTML, and CSS. Also, there is a shell that is downloadable and loads the code using a WebView.

Advantages of the hybrid apps:

• Hybrid apps don’t need a web browser like web apps.
• With a hybrid app, you only have one codebase to manage. So, instead of building two apps like with native apps, you’re building one app, and it works on both platforms.
• Hybrid app developers are often less expensive than native application developers. You will probably require half the number of developers two native apps would have required. Or, with the same number of developers, a hybrid app could be published in half the time.
• Hybrid is a great option for developers that create visually intense applications, for instance, games
• Hybrid apps let you retain the same ability to access device features as with native apps.
• Easy to work with, maintain and upgrade
• Because of their native infrastructure, hybrid apps can work offline.
• Hybrid apps are easier to scale to another platform. Once you’ve built for one platform, you can launch on another, like Windows Mobile.

The downside:

• Getting your hybrid app to run appropriately on each platform generally takes substantial work. Sometimes, the total cost might become comparable to that of fully native apps. It all depends on how close you want to get to the “native user experience” or how simple your app is.
• Hybrid apps can be slower than native apps.
• Need to customize to a large number of exciting browsers
• Inability to implement complex features (e.g. in-app analytics, charts, diagrams)
• These hybrid systems make bug fixing more difficult.
• Hybrid apps load in a WebView, so performance can be worse than native applications.

Say you have an idea for an app, and you don’t know if people will like it or not. If you understand that a web app doesn’t really allow you to test this app, the simple version of the hybrid app will be a good solution for you.

You give to people to download and use the app on their device, although you are short on resources. Usually, a startup project is decided to create a hybrid app that in the startup world, this is called an MVP, or minimum viable product.

Conclusion

There are many different directions in which you can take your app, all of which have their pros and cons. The choice between web, native, and hybrid development is dependent on a number of factors, including business needs, app requirements, developer skill, budget and timelines. What’s important is to spend enough time thinking and analyzing before you start building because the apps are expensive enough. So, you may only have one go at getting it right. Find out the information as much as you can about the different kinds of apps and the development stages.

‍

Single-page applications (SPAs) have earned a strong position in demand long ago. Such apps have one page where most information remains unchanged, and only its parts are updated when a user interacts with them. 

Maybe, you don’t know precisely what they are. However, you probably use them daily. Most social media, Gmail, and other programs are single-page apps. They are quickly adopted thanks to their fast load times and user-friendliness. 

As for React, it is quite a favored instrument for SPA web development. Programmers can build SPA’s components with React to ensure their scalability, reuse them with few configurations, and refresh them instantly without manually reloading a page. Besides, React solutions are optimized for SEO while delivering high flexibility thanks to the Virtual DOM. 

What Are React Single Page Applications (SPAs)? 

SPAs consist of one HTML page. To access it, you make a one-time connection and then – load and modify data without refreshing an entire page. Only the blocks engaged in the interaction process are updated while other page elements remain unaffected.

A clear illustration of a one-page application is Google Docs. While reading a text, you scroll through sheets, but the top row with the menu and the left panel with captions stay put. Whatever changes you make, certain things stay untouched.

React is very common for SPAs because of its convenience — it speeds up the programming process significantly. Also, note that it is not a framework, it’s a library. The main thing in it is the components as they represent particular items on a page. Each one of them contains the entire set of data and methods needed for work.

React allows you to save the state of the elements. This makes them isolated and self-contained. This feature, in turn, provides developers with the ability to reuse the elements as needed. Specifically, the same element can be applied to another page or parts of the website as many times as you want. This way, a developer doesn't need to re-write a significant part of the code. Therefore, the absence of complex dependencies between elements simplifies troubleshooting.

Pros of React SPAs

Why do giant companies like Meta choose React SPAs? It becomes clear after looking at its main advantages: 

• Versatility. SPAs can be used from any device that has access to the Internet. 
• Accessibility. There are no problems with сompatibility, as well as memory limitations, capacity, or installation time.
• Enhanced usability. These apps are straightforward and enjoyable to operate.
• Better responsiveness. It can be ensured thanks to the high speed of data load.
• Ability to process large volumes of data. The device's memory does not limit the processing power of an app and the volumes of data it operates.
• Code reuse. React allows developers to reuse parts of code as often as needed in the same app. 

Let's look closely at the main merits of React SPA development. 

Enhanced User Experience (UX)

The primary advantage of a single web page application is immediate data update, resulting in a smooth sensation from using and improved responsiveness. Since SPA doesn't have to implement full-page reloads during use, the contents of a page refresh very quickly. The program only needs to connect to the server at the start, and then simply loads the necessary segments as a visitor needs them. In a single-page app, everything works quickly after the first load and doesn’t keep users waiting. 

Improved Developer Efficiency

Programmers who use React receive a lot of tools that simplify the assembling of the single-page application architecture. Moreover, React allows reusing code in software architecture to speed up the work. For instance, if you decide later to evolve a one-page app into a full-featured multi-page application, you’ll be able to reuse about 30% of the code instead of writing everything from scratch. Plus, it will help your startup save time and reduce costs.

Rich Interactivity

React is a freely accessible framework library that has a JSX (JavaScript XML) compiler with a focus on the users’ comfort as it provides prompt rendering. Thus, by utilizing React, you can create a UI-centric product which is common to SPAs that should have fluid interfaces, rapidly responding to the actions of users.

Cons of React SPAs

Surely, React SPA development has its drawbacks. Otherwise, every app would be written using this framework. 

The most important cons of these apps are:

• Lack of SEO optimization. The whole point is in the structure of such applications, which consist of just one page.
• Longer initial loading time. Since all user-side data of the application must be loaded during initialization, this can cause unpleasant delays for users.
• The client-side routing challenges. React may not be the best choice for creating applications with complex business logic and data routing.

So, let’s check each of them in more detail.

SEO Challenges

Single-page applications are quite problematic to index because not much text can be recognized by search robots. At the same time, you can’t put all the keywords on one page because it will look weird and lead to spam problems. What's more, the page will have only one URL.

You can eliminate this issue by resorting to server-side rendering. It's a way to render a one-page application on the server side. The app is still one-paged, but the main work is done on the server. The application receives a finished page with the required SEO elements from the server immediately after its initialization. However, due to the complex introduction of this technique, the cost of SPA development might be higher.

Complexity for Large Applications

Managing complex states and data traffic can be a problem in large SPAs. That’s why using React SPAs relates mostly to projects with rather limited functionality. At the same time, if applied skillfully, this library can still be useful when creating large projects – evidence of this is the digital giants that actively use it in the development of their services and applications. 

Initial Load Time

With React’s ability to load all content during initialization, people can wait a lot when opening a SPA first (in comparison to other websites). However, after initial loading, all other data will be pulled up automatically, and there will be no issues with delays in provisioning page content. 

Code splitting solves the problem of slow app loading by reducing the volume of data sent on the first session. Rather than uploading the entire code at once, users do it only with the parts that they need right now. This, in turn, facilitates the coding procedure.

React SPAs vs Traditional Multi-Page Applications (MPAs)

These kinds of apps are paralleled and characterized as competitors. However, multi-page applications (MPAs) are unlikely to lose the race because each alternative works for certain products and commercial pursuits. 

Let's look at the core strengths of each application type:

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As you can see, each application type has its drawbacks and advantages. So, when choosing between them, come from the specifics and tasks of your company. 

What kind of companies should pay attention to one-page solutions?

• Products that run on a captivating platform with low data volumes. SPA is commonly applied to SaaS platforms, social media, and private communities. Some examples include Grammarly, Netflix, and Slack.
• Startups focused on usability. Single-page apps allow you to create an easy-to-use and intuitive interface that attracts the eye and encourages customers to explore the product. 
• Startups planning to go big. Later you can reuse some of the code of your single web page application. This helps reduce the time and expense involved in developing new, full-fledged digital solutions.
• Businesses for whom SEO is not a key metric. If your company doesn't require to be ranked high on Google from its first launch, to successfully implement your idea, SPA is right for you.
• Companies with their own APIs. You can build a one-page application based on a pre-built API.

Conclusion 

Today, we see that one-page applications gradually replace classic applications and begin to play an important role in the construction of large-scale web systems. Now, these apps are everywhere, and we use them every day without noticing it. Moreover, the big players in the app market already use SPAs.

What are the rewards of single-page applications? They are pretty speedy, don't keep visitors on hold, and generally improve their experience when dealing with the solution. in turn, overall consumer comfort has a direct impact on conversion rates and profits.

Still, SPAs are not ideal for all startups. But for those seeking a dynamic, high-speed, and lightweight digital solution, SPAs are an excellent choice.

The healthcare industry is an area that strongly needs innovations at all times. Diseases constantly evolve, and the care methods should go one step ahead. Infrastructure in healthcare and streamlined communication are not the last points in fostering positive swifts in this industry. These two elements, in conjunction, create favorable conditions for bringing innovations or merely positive changes. The more automation and advancements are introduced, the more effort can be devoted to curing and preventing diseases. That is why the Medical Internet of Things is so emphasized nowadays. Its role is impossible to overestimate, indeed.

Essence and Aim of the Internet of Medical Things (IoMT)

IoMT stands for a tech advancement that envisages the mutual coordination of various elements inside the health system, such as medtools, apps, and other arrays that facilitate steady and ongoing data circulation. The pillar objective of the IoMT is to foster and ease the non-stop collection, handling, transmitting, and sharing of the medinfo among the interested subjects. Most typically the latter ones are state authorities, hospitals, and other members of the healthcare industry. 

Among the main deliverables that IoMT ensures are multiple options to track and manage the state of patients on a real-time basis. It also makes possible timely cure and disease prevention. Variable IoMT devices are already utilized for these purposes – health monitors, intelligent implants, remote diagnostic tools, and patient tracking systems, to mention a few.

How IoMT Works at a Glance

This tech approach is aimed at connecting multiple physical devices, including sensors and implants, through the Internet networks, most typically Wi-Fi. This endeavor is accomplished in order to grasp and transfer the most up-to-date info regarding the patient’s state. 

IoMT also makes it feasible to collect a large scope of data and ascertain the outcomes of treatment as well as the tendencies of disease evolution. At a glance, the said technology is about multiple devices, software, informational systems, algorithms, and storage facilities. All of these elements are related to each other instantaneously and deliver error-free info about the things that fall under the survey. 

Based on that info, medical personnel and other interested subjects highlight measures that have to be taken for the effectual care of the patients. IoMT is also in charge of elaborating on the list of actions that are needed to be accomplished to prevent similar cases from appearing in the future. Any information obtained during such an operation is extremely valuable and derives numerous benefits. 

Benefits of IoMT

This tech advancement is definitely under the stage of its next development and upgrade. Therefore, the number of benefits may be higher in the short-term perspective. As of now, it is proven that IoMT guarantees these positive swifts across the industry:

• Bolstered efficacy of performance within a healthcare institution – the work of the medical personnel becomes more effective and coherent enabling quick decision-making which is especially crucial in emergency cases. 
• Better and easier identification, including across multiple institutions – people don't need to input the same data many times. Once they have done that, it is transmitted through the internal channels of communication between the medinstitutions.
• Enhanced surveyance – IoMT software ensures timely and correct treatment as well as further post-medical support. The info is collected and processed steadily allowing the interested parties to make relevant conclusions regarding the efficacy of the chosen methods.
• Tackling diseases – collecting the info regarding the reasons why patients suffer from certain diseases enables scientists to elaborate on findings that are very precious for disease prevention. Most typically this is made by eliminating the conditions for such diseases to occur. 

IoMT issues and challenges

On balance, the adoption of the considered tech advancement is associated with specific threats and risks:

• Coherent interaction of various medsystems – frequently, such medsystems have multiple peculiarities of operation since different independent producers develop them. Making all the integrated systems not only joint but also coherent may be quite challenging sometimes.
• Effective information management – this covers clinical data monitoring, storage, and processing. The outputs have to be accurate, relevant, and up-to-date.
• Secure data handling – since the info processed is very sensitive in nature, it requires more advanced measures to be implemented. Fairly saying, this type of data is very nourishing for cybercriminals. Still, encryption and specialized IoMT security software can substantially decrease, even eliminate at all, the risk of data leaks. The goal of ensuring robust data protection is required not from the ethical perspective solely – this is also a compulsory rule among the norms applicable within the industry.
• Alignment with the regulatory standards – the applicable legal rules are quite strict, taking into account the essence of services. To this end, a failure to comply with such often leads to severe fines and other sanctions, up to the liquidation of a medical institution. In addition, the applicable regulatory framework is frequently updated, resulting in the need to take other measures allowing to remain compliant.

What Prospects Does the IoMT Have?

IoMT in healthcare definitely opens wider perspectives, among others, thanks to the following tech advancements:

• AI&ML implementation – these two elements are equally good at bolstering analytics and personal care approaches. This can be achieved through automation in decision-making. Most common cases will not require excessive time to ensure correct treatment – these technological advancements, like AI platform for clinical diagnostics, will handle them quickly, efficiently, and at lower costs.
• Bolstering the quality of wearables and implants – upgraded serviceability and preciseness will deliver more faultless data regarding the patient’s state, particularly one’s life parameters. The advancement will also enable quick and more productive feedback in response to possible changes, especially adverse ones.
• Automated internal arrangements – monitoring the inventory and machines plays a pivotal role in ensuring the straightforward operation of any medinstitution. The less time medical personnel devote to routine assignments, the more time caregivers have for their visitors. Even more, optimized internal flows reduce associated costs drastically as well. The quality of medservices is not sacrificed but boosted under such terms.
• Utilizing big data benefits – IoMT ensures the effective and timely handling of large volumes of medical information for multiple purposes: tendencies, patterns, typical treatment outcomes, opportunities for innovations, etc.
• Quality and upgraded involvement methods – future IoMT will facilitate far more effectual involvement of patients and their awareness of the medcare applied. Informing, consent giving, and providing the updates will be made automatically and faultlessly, preventing any unreasonable delays or discrepancies.
• Effectual dealing across the healthcare sector and the remainder of the sectors concerned – IoMT architecture opens another room for diverse independent service providers, financial institutions, and state authorities to interact and exchange data with each other swiftly and straightforwardly. As a consequence, the interested subjects can drastically bolster their performance and the speed of decision-making.

Conclusion

The Internet of Things medical application is a powerful tool on the way to ensuring top-rated services within the industry and effective disease prevention in the future. It already offers multiple benefits, such as on-time and effective performance, multifunctional diagnostics, disease treatment and prevention, cost-effective operation, and, finally, more accurate forecasts. This tech advancement will undoubtedly bring even more opportunities in the short term since it is evolving steadily. 

So, it was definitely a wise decision to incorporate such tech upgrades in a business yesterday. Now, they have already become compulsory things to have in possession. Our company is a reliable service provider experienced in custom medical software development. We will facilitate you to take the next step ahead by rendering top-notch services swiftly and at a reasonable cost. Enjoy the condensed expertise gained through the years.

Specifically, according to the Exploding Topics resource, in 2024, Android has a 70.93% global market share while iOS only has about 25% of it. However, both platforms are undoubtedly popular, and each of them, with the right approach to development and promotion, can provide product owners with a stable income. Below, we will reveal the differences and similarities between iOS and Android development, paying special attention to security and customization options.

Android vs. iOS App Development: Key Differences

Let's first look at the fundamental differences in Android and iOS development.

Programming languages

When it comes to native development, the most popular Android development language can be considered Java or Kotlin (they are both officially supported by Android’s original IDE, but the last one is preferable for new projects). As for native development for iOS, here, developers will need knowledge of Objective-C or Swift (generally speaking, this is the best option for new projects, while Objective-C is mainly used to support existing solutions that were originally written in this language).

Development environment

As the primary development environment for the Android platform, Android developers use the open-source Android Studio IDE, although in some cases their choice may also fall on Visual Studio, Eclipse, and some other development environments. At the same time, iOS developers usually resort to the help of Xcode with the integrated iOS SDK. 

Differences in design

iOS development requires following the platform’s own principles for user interface design listed in the Human Interface Design Guidelines (the main aspects of which are color variety without shadows (Flat Design Depth), minimalism (Clarity), and content-first approach (Deference)). In the case of Android development, these are the recommendations for recreating Material Design (the main aspects of which are expressing the hierarchy of elements through their size, colors, and shadows (Metaphoric Nature), using bold typography to highlight important elements (Bold Graphical Design), visualizing reactions to user actions (Meaningful Animation), as well as adapting the design to devices of different form factors (Adaptive Design)). Also note that Android allows the development of applications for operation in Kiosk Mode – this will be useful for businesses presenting their services and/or products through software installed on corporate Android-based devices.

Hourly rates

According to Glassdoor, the average annual salary of an Android developer in the US ranges from $91K to $135K, while for an iOS developer this range is from $101K to $174K. From this, we can conclude that, on average, the iOS app development cost (if you stick to the hourly rate for specialists) will be somewhat higher compared to the Android app development cost, all other things being equal (in particular, the same number of supported device formats and OS versions).

Complexity of development

It is worth noting that native Android development tools and iOS mobile app development tools are quite easy to deal with and, in general, have a low entry threshold. That is why the complexity of implementing software for a particular platform should be considered primarily from the point of view of ensuring compatibility. The fact is that if you are planning Android development to cover as many user devices with different form factors as possible, your team will have to spend a lot of effort and time. Also, there is the need to provide compatibility with earlier versions of Android and, accordingly, adapt the software for devices that support them. Therefore, the development process for Android can be much longer than in the case of iOS, a platform that is characterized by a relatively small number of compatible user devices. On the other hand, for iOS development, the entire team must be equipped with macOS-based devices — only then will they be able to build the software and successfully test it in an emulator.

iOS vs. Android: Security and Privacy

The main difference between iOS and Android in terms of security lies in the fact that iOS security is more focused on providing strong software protection (which is facilitated by its closed nature and limited number of supported devices), while Android focuses on a combination of software and hardware protection (due to its initial open source nature, which can still be violated by individual vendors creating their own Android-based OSes, as well the variety of supported user devices). In the end, it is impossible to say that one of these operating systems is more secure or, on the contrary, more vulnerable, since by following the standards and generally accepted rules of secure development, you will be able to achieve optimal compliance for your software as well.

Which Platform Has a Bigger Market Share?

At the very beginning of this article, we indicated that Android is the undisputed leader in the mobile software market, with almost 71% market share. However, it is worth noting that the ratio of market shares between platforms may vary depending on the business niche for which the application is created. Moreover, there is rather old research that owners of iOS devices are more willing to download paid applications, while owners of Android devices prefer free software. As a result, this factor can determine your monetization opportunities for your app, too.

Which Platform Has More Customization Options?

In terms of customization options, Android gives developers more flexibility due to the open nature of the platform. Thus, they get more space for creativity, which allows them to implement non-trivial designs and functionality. In turn, the iOS platform is a closed ecosystem with its own strict compliance rules. This concept is aimed at maintaining uniformity of user interfaces, so it will not be possible to create something too non-standard on its basis.

Android vs. iOS App Development: Which One to Choose

Ultimately, we cannot say that development for any one of the platforms, be it iOS or Android, is more profitable. Everything depends on the niche for which the software solution is being implemented, its localization, and many other factors that determine the size of the target audience.

Additionally, it is important to be aware of app store fees for paid software and software with in-app purchases. In particular, iOS paid apps, in-app purchases of digital goods or services, and/or linking out to purchase digital goods and services on your website are subject to a 17% fee (or 10% If you're enrolled in the App Store Small Business Program/the Apple Video Partner Program/News Partner Program or renew a qualifying auto-renewal subscription beyond one year); also, if you use App Store payment processing and related commerce services for in-app purchases, you will be charged a 3% fee. As for Google Play, here, the fee is 15% on the first $1 million in annual revenue (or 30% on annual revenue exceeding $1 million), 15% on products with an auto-renewing subscription (regardless of annual revenue size), and 15% or less for developers who qualify for the Play Media Experience Program.

By the way, if you find that the ratio of potential users for each platform is approximately 1:1, you can consider the cross-platform app development option, which involves creating a single code base for iOS and Android platforms. In this case, you can save about 30% of the budget compared to developing two native applications, and also bring the launch date of your mobile solution closer. The only thing is that, like any other type of software development, this one also has some restrictions.

Regardless of whether you are considering cross-platform or native mobile app development, you can always choose us as a reliable service provider of custom mobile solutions. We will implement your business idea with the minimum possible budget without compromising its quality and select the best approaches and tech stack to ensure its competitiveness and profitability over the long term.

In today's world, social platforms have become an integral part of our everyday lives. They provide the opportunity to communicate, share information and create communities, making them a powerful tool for business. Their popularity and influence on modern society have led many companies to realize the potential of social platforms in the context of B2B solutions.

Let's break down the process of implementing such social platforms step by step:

• Defining goals and requirements

The first step in developing social platforms for B2B businesses is to define specific goals and requirements for your project. You need to be clear about what features and capabilities should be available on your social platform. These include user profiles, sharing features, instant messaging, community building, and more. Determine what specific goals you want to achieve, such as expanding your network, attracting potential customers, or strengthening customer relationships. Understanding your goals will further guide your overall strategy and implementation. Prioritize and develop an action plan to address all the requirements of your B2B business.

• Choosing the right technology platform

The next step is to choose the right technology platform for developing a social platform. Many frameworks and tools can help you in this process. For example, you can use popular open-source frameworks such as Ruby on Rails, Django, or Laravel or choose ready-made social platforms such as BuddyPress for WordPress or Sharetribe for developing marketplaces. The appropriate choice of technology platform depends on your needs and your company's capabilities.

• Designing the architecture and database

After choosing a technology platform, it is essential to design the architecture of your social platform. This includes creating the database structure, defining modules and system components, and establishing interactions. A modular approach is recommended to ensure the flexibility and scalability of your social platform. Divide functionality into modules such as authentication, user profiles, messaging, community management, and others. This approach will simplify your platform's development, testing, and support.

• Designing the user interface

The user interface is a key element of any social platform. Designing an attractive and intuitive user interface will help attract and retain users on your platform. Remember your company's brand identity when designing and ensure user-friendly navigation and clear and attractive interface elements.

• Implementing functionality

At this stage, you can start implementing the basic functionality of your social platform. Provide the ability to customize user profiles, set access rights, filter content, and other features necessary to ensure the platform's security and usability. Create modules for registering users, creating profiles, sending messages, forming communities, and other elements that meet your requirements.

• Integration with external systems

Integrating with external systems is a crucial step when implementing a social platform in a B2B business. Enable data exchange between the social platform and external systems to provide a unified flow of information and improve the efficiency of your business. These can be CRM systems, electronic payment systems, analytics tools, and other applications your company uses.

• Testing and launching

Conduct thorough testing before launching your social platform to detect and fix possible bugs and issues. Perform functional testing, compatibility testing with various browsers and devices, and security testing. Once testing is successfully completed, you can begin launching your social platform. Install the necessary server resources and configurations, and configure the security and scalability of the system. As you launch, monitor the process and ensure all components work correctly.

• Support and update

Developing and implementing a social platform is just the beginning. It's essential to continue to provide support and updates to your platform as needed. Regularly release updates, fix bugs, and add new features and enhancements to meet the needs of your audience. Provide timely support to users, answer their questions, and guide how to use the platform.

• Analysis and optimization

Integrating social platforms into B2B solutions provides an opportunity to collect valuable data and metrics. Analyze this data to understand which features are used most often, what issues arise, and what improvements can be made. Use analytics tools to measure the effectiveness of your social platform and make informed decisions to optimize it.

Summary

Incorporating social platforms into B2B solutions can significantly benefit your business. However, for this process to be successful, you need to properly plan, design, and implement a social platform, taking into account your company's technical aspects and requirements. By following the steps outlined above and paying attention to detail, you can create a powerful tool to help strengthen your business connections, expand your customer base, and make your company more competitive in your business domain.

The medical field has been developing for ages, but over the past twenty years, we’ve seen the flourishing of healthtech. And it’s not surprising: this technology is a game-changer for patient care, workflow, and life-saving. 

The healthcare industry includes producing portable devices to monitor vital signs and online medical consultations provided by modern digital health solutions. And, similarly, every private clinic should have a top-notch website where patients can book an appointment with a doctor. 

What are the roles of a medical site?

1. A communication tool with patients. A site can be easily googled. Also, it contains information about available services and specialists. Thus, it makes it much easier for clients to reach out to you.
2. A communicational channel. Internet marketing is one of the most effective tools for attracting customers, but you need a web resource to use it. Thanks to a site, you will be able to find customers among users of search engines and social networks.
3. An image-maker of your organization. You need to give a good impression to make others want to collaborate. 
4. A source of getting feedback and statistical data. Thanks to analytics systems, you can track changes in behavioral factors, perform A/B tests, and monitor the effectiveness of various traffic channels.

So, a medical website must be very comprehensive, secure, and accessible at all hours. To achieve this, it’s important to involve specialists responsible for different aspects of the work at each stage of the healthcare website development. 

Key Considerations When Hiring

Before you hire a health tech development company, it’s essential to ensure their team has the necessary hard and soft skills. The task of the dev team will be to make the site appealing to users and customize traffic channels. In turn, the site owner should provide the essential information and approve tasks in time to avoid excess of the final healthcare website development cost and pauses in the work process.

What should you bear in mind while searching web development companies for healthcare?

• Relevant experience. The more projects related to medicine they have, the better. 
• Technical knowledge. Find out if the developers know the required programming languages. 
• Communication skills. Website development is a team effort not just for the performers but also for the customers, so soft skills are vital here.
• Ability to comply with regulatory requirements. The company must know about HIPAA compliance and other legal provisions. 

Experience in HeathTech

The company you want to hire must understand your business. That’s why your healthtech development company must be experienced with medical data and technology. Look for the company that has previously worked on similar projects. They could have a background in developing electronic health record systems, medical visualization software, remote care platforms, or other products related to healthtech website development. Companies with extensive experience in this field are well prepared to meet the specific demands of healthcare software development.

Technical Expertise 

The company’s tech team should have certain skills required for medical website development. First of all, they need to ensure the security of the medical website. Secondly, it must be able to scale. This will allow you to upgrade a site following current changes like adding new services, functions, categories, or sections. It will also increase functionality and optimize it based on customer requests and company interests.

Another important aspect is defining the right tech stack. The developers must be able to choose a health tech website development programming language that meets business needs. The most popular ones are HTML/CSS, PHP, Python, JavaScript, Java, Ruby, and Swift. The final choice of one or another development tool must be made according to the goal of creating responsive web design and providing a brilliant user experience. The company should also have a skilled designer who can transfer your brand identity into the website’s UX/UI design. 

Regulatory Compliance 

A team working on a health tech site must guarantee its security as patients leave their personal data there – this can be ensured by its knowledge of how to create a secure website for healthcare providers. Specifically, such websites must meet HIPAA regulations and other confidentiality rules in health care, such as GDPR and PCI-DSS. Make sure the company is aware of these norms and can perform HIPAA-compliant website development. 

Communication and Collaboration

Healthcare website development requires ongoing communication between the client and the contractor. For example, in the beginning, you and the tech team will negotiate on choosing the right CMS for the website. Consider hiring only those who are accessible, transparent, and timely. If there are no meetings or any other forms of communication, move on to the next candidates.

Partner’s Reliability

Make sure the chosen company is trustworthy. It must be present on social media and have its website with a portfolio. Seek for clients' feedback on special resources, forums, and social networks. This way, you’ll quickly see what prior customers have said about a company's service delivery. And, if some field-specific media have published case studies about the company’s work, it’s a very good sign. 

Compliance with Your Request for Proposal 

Once you decide on a candidate, it’s time to write a project proposal. Health tech website development proposals include stating the project’s objective, target audience, budget, and other features specific to your project. This way, the potential company will understand your business needs better and be able to provide an individual proposal. 

Impression After Personal Interviewing

A key to successful collaboration lies in communication. Interviewing health tech web developers will allow you to test their understanding of your specific demands and the concept of the website. 

Crucial moments to clarify during interviews: 

1. Ask for details about their experience. Get candidates to talk about a project related to health tech they have participated in. Go into detail to assess their role and expertise.
2. Ask about the development process. Give them thorough questions to test their technical knowledge and figure out the workflow. You can also provide them with a test task to evaluate their skills firsthand.  
3. Uncover their management style. Ask if the team has worked on any cross-functional projects. Look for leadership qualities like guiding, empowering others to succeed, and handling conflicts constructively.
4. Find out the team membership. Alongside those who are usually present in the technical team, such as engineers, QA testers, and tech leads, there should also be a UX designer. 
5. Ask who will own the code. It’s much better for you if the web development companies for healthcare offer a total handover. Yet, the agreement may include a clause that allows the contractor to keep the code. The worst thing that can happen might be that the company charges you more money because of the constant glitches they fix. 
6. Question what happens if the team fails to meet the deadline. Find out how the company can repay for the lost time.

Portfolio 

Ask companies included in a shortlist to demonstrate their previous projects. A voluminous portfolio is usually a sign of a company that has extensive experience. This way, you’ll wind out what they’re capable of. The portfolio will show you if the team can manage challenging projects and meet high regulatory standards. Also, it will show how good and user-friendly their software is. And, naturally, pay strong attention to websites related to the medical field.

Conclusion

Ideally, a website solves several problems of a healthcare business at once. First, it is one of the most effective tools for attracting new clients. It also acts as a channel of communication with existing patients, forms the image of your organization, and allows you to get feedback and collect your customers’ data. All this is possible if the site is developed by the pros. That’s why you should be very picky when you want to hire a health tech development company. Follow the recommendations listed above to find the perfect fit.