First announced in 2014 by Ethereum co-founder Gavin Wood, today, the concept of the next-generation internet, called Web 3.0, opens up a whole niche for startups. Below, we will look at its basics, describe its advantages and disadvantages, and also find out what technologies can be used to implement Web 3.0 solutions.
What Is Web 3.0?
The Internet that we are used to is defined as Web 2.0. It allows all users to upload content themselves, making it publicly available. This became possible thanks to the emergence of social networks, which over time, instead of providing users with the desired freedom, began to limit them with all sorts of rules and censorship. This, in turn, has become the main reason for looking for new, alternative concepts that would go against these restrictions, giving users more control over their publications.
In this regard, instead of centralized networks, the operation of which was tied to central servers, decentralization was proposed. It had to provide a “smart” distribution of content to servers located throughout the world. Thanks to this approach, widespread availability of user content would be ensured. With this approach, to block specific publications, the consent of all network nodes is needed—instead of just one, as was the case with Web 2.0.
As for technologies capable of implementing a decentralized network architecture, these include, first of all, blockchain, which would transform the centralized Internet into a distributed content database. Actually, this radical transformation would give the world the Web 3.0 Internet.
Today the simplest Web 3.0 formula looks like this: "read + write + own".
Why Is Web 3.0 Important?
Due to its undeniable value for ordinary Internet users, Web 3.0 can be of particular interest to businesses that are constantly developing in the digital plane.
Specifically, by increasing content accessibility, reclaiming ownership of user-created content, and overall end-to-end user centricity, businesses can benefit from launching personalized Web3 websites, apps, and services that aim to increase trust and engagement among their users, as well as providing advertising based on their behavioral patterns.
This way, businesses can strengthen their relationships with consumers and, therefore, increase their profits.
How Web 3.0 Works
As you can already understand from the above, Web 3.0 is based on decentralized networks, but this description is not complete. In particular, Web 3.0 also implies that any Web3 website or application must be able to process information like the human brain, that is, as artificial intelligence and machine learning already do today. This is how an alternative name for the 3rd generation of the Internet has appeared—the semantic web.
This requirement for the “intelligence” of the Internet arose for a reason: since, due to decentralization, users and machines interact with data directly, this data must be recognized by the network not only using predefined algorithms—now, its context has become no less important.
Benefits of Web 3.0
With the right approach, virtually any company that continually grows its online presence can reap a number of benefits from Web 3.0 software development. These include the following:
Increasing customer loyalty achieved through the AI and smart chatbot introduction aimed at completely personalizing digital content and services;
Improved analytics thanks to more complete and accurate sets of user data obtained from disparate sources (and, as a result—the ability to create more accurate marketing strategies);
Automation of a wider range of tasks provided by blockchain, ML, and AI;
Increased security that only distributed registry technologies can guarantee;
New partnership opportunities without the involvement of third parties thanks to blockchain and smart contracts;
Supply chain optimization, also enabled by blockchain that provides full control and transparency of supply chain operations;
Copyright protection provided by giving users full control over their data (also thanks to blockchain).
Challenges of Web 3.0
Despite the existence of decentralized solutions, it is too early to say that the 3rd-gen Internet already exists. To make this thesis true, developers have to implement connectivity between Web3 solutions so that the data they collect and process become globally accessible.
For example, if we are talking about Web 2.0, in it, data exchange occurs via the HTTP protocol. In this case, there is an intermediary between the participants (namely, the server) on which information, databases, applications, etc. are stored. The concept of Web 3.0 implies the organization of a new level of network interaction, without a server. So far, such technologies do not exist—it can be explained by the limitations of physical equipment and infrastructure in general.
An equally serious challenge is to ensure proper speed of data transfer and processing in the Web 3.0 development solutions. In a more global sense, this means a scalability problem since the need to maintain a proper balance between decentralization and security is most often achieved by limiting throughput, as we can see in the examples of Bitcoin and Ethereum.
At the same time, options to circumvent these obstacles are already being considered—a potential way out of the situation could be the creation of a cross-chain infrastructure to integrate all blockchain networks. Similar solutions (though not so large-scale as to replace the whole Web 2.0) have already been implemented in the Polkadot and Cosmos projects.
Technologies that are powering Web 3.0
Earlier we talked about the need to use AI and ML in Web 3.0, but let's take a look at the full list of technologies that will be relevant for each Web 3.0 developer.
Artificial Intelligence and Machine Learning
Together with machine learning, artificial intelligence in Web 3.0 provides better analysis of user data and creates a personalized user experience based on it. Thanks to this, users get the content they need faster, without getting confused by colossal volumes of useless or only partially useful information. These technologies can also be responsible for automating resource-intensive processes such as data sorting and forecasting. Finally, we should not discount natural language processing—one of the areas of AI that ensures more accurate and context-sensitive recognition of user queries by machines.
Blockchain and other distributed ledger technologies (DLT)
In Web 3.0 application development, blockchain and other DLTs provide better security when storing user data by eliminating centralized control over it. These technologies also make it possible to create tokens (including non-fungible ones, NFTs), which are decentralized digital assets stored on the blockchain. They define ownership of real or virtual objects and, in the future, can be used to protect copyright.
3D graphics and animation
With the faster content delivery typical to Web3, 3D graphics and animation solutions will become commonplace. In particular, Web 3.0 opens up new opportunities for creating realistic three-dimensional objects to ensure an immersive experience that was previously unavailable with any other digital technologies. Supplemented with reality-like animation, these objects can act as avatars of living users and thereby make them full-fledged participants in meta universes.
The future of Web 3.0
Given all of the above, it is unlikely that Web 3.0 will be a single system—we are sure, it will be a comprehensive solution that goes beyond the traditional web. This approach will ensure the management of the Internet by the user community and exclude content monopolization.
As for specific areas of development, the most promising are considered to be meta universes that combine physical, augmented, and virtual realities with their own blockchain-based tokenomics. At the same time, it is important to understand that despite the many points of contact, currently, metaverses and Web 3.0 are different things since those that exist today are still products from centralized IT companies, with their typical problems. Therefore, the solutions that will mark the emergence of Web 3.0 will differ significantly both from the point of view of technical implementation and the very concept of interaction with users as well.
If you, like us, consider the Web 3.0 direction to be promising for your business and already have an idea that will bring this concept closer to reality, you can contact our Web 3.0 development company. We will advise you on the implementation of your project and also select specialists who will guide you from collecting project requirements to its launch.
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.
If you are looking for a suitable platform to implement your mobile solution in the most cost-efficient way and, thus, make it cross-platform, it can be a rather challenging task given the variety of different frameworks, libraries, and SDKs.
For example, according to Statista, Flutter and React Native (RN) were the world's two most popular frameworks for cross-platform development by the end of 2023. However, they cannot be called universal, and each is better suited to solving specific problems. Below, we will cover the difference between them in more detail and also conduct a comparative analysis of these two solutions.
What Is Flutter?
Flutter is an open-source framework from Google that first saw the light of day in 2018. It is designed for both front-end and full-stack development of cross-platform solutions with a single code base for iOS, Android, Web, Windows, MacOS, and Linux. Flutter uses the open-source Dart language, which is also a brainchild of Google. Now, it has 162k stars on GitHub.
One of the most significant benefits of Flutter is the ability to accelerate time-to-market: this is ensured by hot reload (which provides the ability to change the program code and check the results in real time), the built-in increased performance provided by direct compilation to native code, as well as the presence of a C++ engine for rendering elements and optimization. In general, according to the unspoken opinion of developers, about 95% of all development tasks can be covered by Flutter libraries and tools.
Benefits of Flutter
Flutter popularity is absolutely justified—it is loved by developers around the world for the following advantages:
Native-like performance. Due to the use of the Dart programming language, which is immediately compiled into machine code, applications created with this framework are unlikely to be inferior in performance to native analogs.
Uniformity of visual components. Flutter does not use platform-dependent visualization tools—instead, this framework refers to the Google Skia graphics library, which allows developers to provide a uniform look for UI components regardless of the platform.
Low entry threshold and a wide variety of available tools and libraries. Flutter is considered an easy-to-learn framework, and such built-in features as hot reloading, widget inspector, and a rich arsenal of tools for solving graphic problems (now, this framework already has graphics rendering at 120 fps) make the process of working on the frontend even more enjoyable and fast.
What Is React Native?
React Native is a cross-platform JavaScript-based framework created by the Facebook team in 2015 and introduced to the public as a solution for reusing the same code. Today, this platform can boast 116k stars on GitHub and the fact that it is at the heart of such world-famous services as Instagram, Facebook, and Skype.
An interesting feature of this framework is its basis on the Flux architecture, which is based on unidirectional flows. This approach allows data to be passed from one parent component to its child components, making it easier for developers to manage data flows in the application. Also, like in Flutter, there is the Hot Reload feature that simplifies the testing and debugging processes, as well as a rich set of built-in components to boost the development speed of high-performance and visually attractive mobile solutions.
Benefits of React Native
The main benefits of React Native are:
Ability to reuse code by up to 90% and reduced time to market. The reusability of code written in this framework can be up to 90%. In addition, the created assembly, for example, for Android, will require only minimal configurations in order to function properly on iOS. All these features of this framework speed up the launch of projects.
Fast update in app stores. Thanks to the CodePush function, applications can be updated within the repeated deployment process without an intermediate step involving uploading updates to the app store. In particular, when the update is ready, its version that has already been presented on the store uploads it itself, without the need for re-communication with Google Play or the App Store.
A wide variety of libraries and access to native components. While it provides access to native platform components, there are also internal libraries with well-developed documentation and, in some cases, even entire application templates, including Native Base, React Native Elements, and React Native Starter Kit.
Key Differences Between Flutter and React Native
Now it's time to start a more detailed comparison of React vs Flutter.
Entry threshold
Even though both mobile app frameworks are considered to be quite easy to learn, Flutter, due to its abundance of internal development tools, practically eliminates the need for developers to use third-party tools and libraries (unlike RN). This means that its entry threshold can formally be considered lower.
Performance
As for React Native vs Flutter performance, the second one has a lot of widgets based on its own high-performance rendering engine, which makes it suitable for developing applications with complex graphics. At the same time, despite the claims of RN creators, this solution can impose certain problems with the speed of work of resource-intensive projects.
Time to market
In our Flutter vs React Native comparison, RN gives Flutter a head start when it comes to developing applications with different designs for each of the platforms since the latter is aimed at building uniform interfaces.
Specifics for creating user interfaces
While Flutter offers developers a variety of ready-to-use widgets based on the Material (Android) and Cupertino (iOS) designs, React Native refers to the native components of the platforms.
Full list of supported platforms
Flutter supports development for Android, iOS, Windows, macOS, Linux, Google Fuchsia, Web, Tizen for Samsung Smart TV, LG webOS, as well as for embedded devices. As for RN, this framework is compatible with Android, iOS, Windows, MacOS, as well as Android and iOS TV platforms.
Conclusion
We hope that we have answered the essential question: “Flutter or React Native?” and it will be easier for you to choose the best of these two options. At the same time, we would like to note that the future of Flutter is uncertain, as Google has suspended its active development and significantly reduced its team. Therefore, we will keep our finger on the pulse and inform you about all upcoming changes.
Anyway, whichever framework you settled on, an equally important aspect will be the choice of an experienced team of developers. In particular, you can contact us for a consultation and also to find out the cost of development of your project.
According to a recent Deloitte study, 76% of American companies outsource their IT functions. What drives this trend? Does outsourcing truly win the in-house vs outsourcing development battle? In this article, we will explore both strategies, evaluate their advantages and disadvantages, and help you choose the approach that best suits your project.
What Is In-House and Outsourcing Software Development?
In-house development refers to the process in which a company independently creates its digital products. In this case, only the internal team is involved in development, without hiring external specialists.
Do you think this approach is ideal because all IT functions will be handled by a reliable development team that you assemble yourself? In some ways, you’re right. However, it also comes with significant responsibility and high costs. After all, you will need to hire and manage developers, designers, product and project managers, and other personnel. But we will discuss the pros and cons of this strategy in more detail later.
Outsourcing software development implies hiring an external team or individual remote specialists to work on a software solution. Depending on the budget size, required technology stack, and other project specifics, you can choose between nearshoring, offshoring, and onshoring. What do these terms mean?
Offshoring refers to delegating processes to a distant country, often on another continent. This is considered the most cost-effective outsourcing option, as service rates vary significantly across countries. However, such price differences do not always affect the quality of the final product.
Take a look at these impressive figures: In Switzerland, developers earn around $100,000 per year, whereas in Norway, Australia, and several other countries, this amount is nearly half as much. Does this mean that specialists in those regions are less qualified? Hardly.
Nearshoring—outsourcing to a neighboring country. Preferably one that borders yours or is at least located on the same continent. The goal is usually the same—to save on IT services.
Onshoring—hiring external specialists from your own country. What is the point? It’s simple. Even within the same state, the cost of creating an identical digital solution can vary significantly.
As an example, take a look at the salary range in major USA cities (data provided by Indeed):
Columbus, OH – $142,605 per year
Bellevue, WA – $137,206 per year
San Jose, CA – $120,751 per year
San Diego, CA – $118,932 per year
Houston, TX – $98,751 per year
So, we have figured out the definitions of in-house and outsourced software development and also discussed the types of the latter. What are the key differences between these strategies?
In-House vs Outsourcing: Key Differences
In-house vs outsourcing development is a choice that many teams face. The fact is that there are several fundamental differences between these approaches. They should be taken into account when organizing the development process in a company.
This comparison highlights the strengths and weaknesses of both approaches to software development. However, to make these differences even clearer, let's take a closer look at the pros and cons of outsourcing and in-house development.
Outsourcing Software Development: Pros and Cons
Let’s start by looking at outsourcing software development – the pros and cons of this approach allow the global outsourcing market to grow at an impressive rate. This suggests that the cons of this approach are not so significant, right?
If in 2024 it was valued at $611.8 billion, it is projected to grow to $1,345.5 billion in the next 10 years.
Outsourced Development Pros
Teams that opt for outsourcing digital solution development can expect the following benefits:
1. Cost Savings. We’ve already mentioned that companies can save by outsourcing to regions with cheaper labor.
However, that’s not all. Toptal has created a calculator to estimate the actual costs of hiring in-house developers and remote specialists. According to the calculations, even with the same hourly rate, outsourcing will cost the company 1.5 times less:
This is explained by additional expenses for office rent, employee training, providing necessary infrastructure, and so on.
2. Access to the global talent pool. Finding a developer with the necessary skills can be challenging, especially in regions where the number of specialists is not very large.
Take a look at how the number of IT specialists differs in various countries:
Access to the global talent pool allows you to use the latest technologies that may not be widely available in your region.
3. Flexibility and scalability. According to Indeed, the hiring process can take anywhere from a week to a month or more! Therefore, if you anticipate the need to expand or reduce your team, it is wiser to consider outsourcing.
It also makes more sense to consider a remotecandidate for short-term projects if there is no further collaboration planned after the project ends.
4. Accelerating Time to Market. Outsourcing allows you to reduce the time spent on recruiting and hiring specialists. Additionally, there is no need for training and onboarding the development team, which, as you’d agree, is also a lengthy process.
Moreover, you can engage several teams to work on the project. They will work on different aspects of the application simultaneously, bringing its release closer. An additional benefit is the ability to work around the clock due to time zone differences.
5. Optimization of company operations. Outsourcing IT functions will allow you to focus on other activities, such as marketing, customer service, implementing financial strategies, and more.
Furthermore, outsourcing makes sense even if you have an in-house development team. Engaging external specialists will reduce the load on your team and allow you to implement innovative technologies that your developers may not be proficient in.
Outsourced Development Cons
Along with the benefits for a company, turning to IT outsourcing can lead to certain challenges:
1. Communication problems. This issue is especially relevant with offshoring, when specialists are located in different time zones. Just imagine: you need to urgently address an issue, but it's outside your team's working hours. This can seriously affect the efficiency of development.
Another possible barrier to productive collaboration is different languages and cultural differences. Wouldn't it be difficult to understand your counterpart if their vocabulary is unfamiliar to you, or if they adhere to values and traditions that differ from yours?
2. Quality control challenges. When an in-house team is working on a software product, they are clearly aware of the standards they need to follow. This enables them to create solutions that meet the needs of the audience and business expectations.
In the case of outsourced development, there is a risk of discrepancies in quality standards between internal and external teams, especially when communication gaps exist.
The result is an unsatisfactory final product, which may lead to another outsourcing drawback—as we'll discuss next.
3. Hidden costs. If the final product quality is lacking, you’ll have to hire other specialists to fix defects. However, this problem can be avoided by working only with trusted providers.
Still, there is the risk of additional costs for managing remote specialists and the possibility of project expansion due to control issues.
4. Data and intellectual property (IP) security. Collaborating with third parties on development involves sharing confidential information about your business and clients.
Again, only work with reliable partners to ensure they won’t misuse your data or share it with unauthorized individuals.
We’ve discussed the pros and cons of outsourcing software development. How does the situation look with the advantages and disadvantages of creating an in-house team?
In-House Development: Pros and Cons
Despite the growing popularity of outsourcing, many companies still prefer to develop software in-house. This approach also has its own pros and cons.
In-house Development Pros
Developing software internally offers the following advantages:
1. Full control over the development process. The decision to hire an in-house team is a step towards complete control over the development process.
You can select the optimal tech stack, implement methodologies that you deem suitable, and track every stage of the SDLC and its duration.
2. Intellectual property security. According to ABI Research, U.S. companies lose between $180 billion and $540 billion annually due to intellectual property theft.
In-house development minimizes the likelihood of this issue, as the company will be the sole owner, and there is no need to share data with third parties.
3. Improved communication on the project. Teams working in a shared workspace experience fewer communication challenges. They are not hindered by cultural and language differences or time zone discrepancies.
This, in turn, helps avoid several problems. According to recent statistics, poor communication methods can lead to a number of losses for a company. Among these, 68% of work time is wasted, 42% of employees face stress and burnout, and 12% of customers leave for competitors:
4. Team engagement and a deep understanding of business nuances. An internal development team is likely to be better informed about the company's values, culture, and target audience. This enables them to create a tailored solution that aligns with the client’s vision.
Additionally, in-house developers are more invested in the success of the project, as they are part of the business.
In-house Development Cons
If you are an advocate of in-house development, be prepared for some challenges along with the benefits. Here are a few of them:
1. High costs. With outsourcing, the company only spends money on the developer’s rate. The situation is completely different with an in-house team.
We present a list of expenses to consider:
hiring costs;
total salary of the team;
provision of necessary infrastructure;
training fees for technologies;
bonuses and benefits;
software license purchases;
overhead costs.
Overall, the difference between in-house vs outsourcing development can amount to hundreds of thousands of dollars. And as you can see, it is not in favor of the in-house team.
2. Limited resources. In-house specialists may have limited experience and knowledge in specific areas needed for the project. There are two solutions to this issue: employee training, which again incurs additional costs and time, or opting for familiar technologies that are unlikely to yield the desired results.
Moreover, the lack of resources may become noticeable when scaling the project significantly. Hiring an in-house specialist is a lengthy and complicated process.
3. Hiring issues and employee turnover. According to Statista's analytics, in 2023, 54% of organizations experienced a shortage of tech talent. And this is not the peak—two years ago, this figure was at a record-high 70%.
However, even if you manage to hire the best talents, don’t rush to celebrate your victory. You still need to retain them. And here’s where the real challenge begins. To attract a developer to work specifically for your company, you will need to offer a high salary (higher than your competitors), social benefits, bonuses, etc. In other words, you will be committing yourself to unforeseen expenses. Whether this is justified is up to you to decide.
So, we’ve thoroughly discussed the advantages and disadvantages of in-house development and outsourcing. Now, it’s time to determine which strategy is best suited for you.
In-House vs Outsourcing: How to Make the Right Choice?
Long-term projects. If a project requires constant development, support, and close collaboration, it makes sense to build an internal team. This is especially relevant for complex systems that are continuously evolving and require in-depth knowledge of their architecture.
Projects involving confidential data. If you are dealing with confidential data, in-house development can guarantee its complete security. This is facilitated by better control over work processes and the absence of the need to share data with third parties. However, as an alternative, you can turn to a reliable outsourcing provider who guarantees the security of confidential data on par with an in-house team.
Projects that are critical for the business. These are projects that involve creating products that must fully reflect the vision, culture, and strategy of the company. If you are not sure that you can achieve this with external developers, it is better to opt for an in-house team.
For outsourced development:
Projects with a limited budget. Outsourcing allows savings on developer salaries and related expenses.
Short-term projects. If the team is only required to create a product without further updates and support, hiring in-house specialists is not advisable.
Projects where quick time-to-market is a priority. Time savings on hiring and adaptation, the ability for round-the-clock work, and involving multiple teams contribute to faster releases.
Projects with changing resource needs. Here, the ability of remote teams to scale quickly is key.
Innovative projects. Access to a global talent pool enables the use of cutting-edge technologies that may not be well-developed in your region.
Thus, if you are looking to save costs, use an innovative tech stack, and outpace competitors, outsourced development will be the best choice for your company.
Darly Solutions—Your Reliable Partner in Outsourced Software Development
The Darly Solutions team is ready to be your guide in the world of creating high-quality digital solutions. We provide comprehensive services, including web and mobile development, UI/UX design, MVP development, QA and testing, and much more.
By outsourcing your project to us, you can be confident in our full immersion in your product vision, data and intellectual property security, and guaranteed adherence to project deadlines.
At this stage, we get acquainted with your needs, outline the goals and desired results. We are always happy to take your project to the next level, and then beyond
We are a tech partner that delivers ingenious digital solutions, engineering and vertical services for industry leaders powered by vetted talents.
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