Both Tabnine and Copilot offer AI coding assistant solutions for enterprise R&D teams.
So which solution is better?
We’ve put together a comparison using the most common parameters that we’ve encountered in our own work as developers, whilst also serving the dev community for several years. From our POV, these parameters reflect the most important needs, pain points, and challenges that must be addressed by AI code assistants for enterprises:
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Let’s take a closer look at the parameters and how each solution compares.
Tabnine Enterprise offers full privacy for its customers’ code:
On the other hand, Copilot Business sends both code snippets and user engagement data to itself. Although the code snippets aren’t saved, user engagement data is collected and used to improve Copilot’s AI models.
The code on which a solution’s AI models are trained can have a serious impact on the companies that use the solutions.
Tabnine doesn’t train (and never has trained) its AI models on code with non-permissive licenses, and offers full transparency and attribution. This means that Tabnine isn’t restricted by the GPL license’s copyleft provisions, and protects users and customers from possible related consequences. In addition, it’s always been Tabnine’s goal to honor the intent of code authors and maintain good faith with the rest of the developer community.
On the other hand, Copilot for Business trains its models on OpenAI, which could result in legal exposure for its customers. This is because OpenAI is trained on non-permissive open source, such as GPL, and doesn’t disclose its training set or provide references.
While Copilot Business is trained only on OpenAI, Tabnine Enterprise allows customers to connect Tabnine’s AI models to their own code repositories. This means the models, which are only accessible to the customer, can learn the organization’s coding best practices, styles, naming conventions, and more, resulting in code suggestions that are way more context-sensitive and relevant. In addition, this allows for faster onboarding and training of new team members and junior developers.
Both Tabnine Enterprise and Copilot Business offer central management and configuration, however, the features offered are different.
Tabnine Enterprise’s centralized configuration allows customers to:
Copilot Business’s centralized configuration allows customers to:
On the surface, the price appears to be almost the same ($20 per Tabnine Enterprise user, $19 per Copilot Business user). However, Copilot Business is only available to companies using GitHub Enterprise, which charges $210 per seat per year. In effect, that’s a barrier for enterprise companies that don’t need or want GitHub enterprise.
Both companies allow enterprise customers to configure and manage user roles and permissions.
Whether you are choosing an AI coding assistant to make your life easier as an individual developer or choosing a tool to deploy out to your entire engineering team in order to improve the effectiveness and satisfaction of your team, it’s critical to evaluate potential vendors holistically:
Only Tabnine meets all of these requirements expected by enterprise engineering teams and has the history and scale of developer adoption to prove it.
Since launching our first AI coding assistant in 2018, Tabnine has pioneered generative AI for software development. Tabnine helps development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy & security. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. With more than one million monthly users, Tabnine typically automates 30-50% of code creation for each developer and has generated more than 1% of the world’s code.
Unlike generic coding assistants, Tabnine is the AI that you control:
Tabnine ensures the privacy of your code and your engineering team’s activities. Tabnine lives where and how you want it to — deployed as protected SaaS for convenience, on-premises for you to lock down the environment, or on Virtual Private Cloud for the balance of the two. Tabnine guarantees zero data retention, and we never use your code, data, or behaviors to feed our general models.
Personalized
Tabnine is personalized to your team for optimal impact on your business. Tabnine is built on best of breed large language models (with the flexibility to switch as new models emerge or improve), while giving you the ability to fine tune or deploy fully customized models. Tabnine is context-aware of your code and patterns, delivering recommendations based on your internal standards and engineering practices.
Tabnine works the way you want, in the tools you use. Tabnine supports a wide scope of IDEs and languages, improving and adding more all the time. Tabnine also provides engineering managers with visibility into how AI is used in their software development process and the impacts it is having on team performance.
Secured
Tabnine is secure and trusted. Tabnine believes in building trust through Algorithmic transparency, and thus shares how our models are built and trained with our customers. Furthermore, we are relentlessly focused on protecting our customers interests by only training on code with permissive licenses and only returning code recommendations that will not be subject to future questions of ownership and potential litigation. We respect open source code authors and their rights as well as the rights of each and every one of our customers.
As you should expect from any vendor, Tabnine offers proven, enterprise-grade security and meets key industry standards.
Get started with Tabnine for free today, or contact us to learn how we can help your engineering team be happier and more productive.
Tabnine is excited to announce that we’re expanding our offering beyond code completion into additional areas of the SDLC, with the release of our AI-powered Unit Test generation capabilities.
As the leading AI assistant for software development, Tabnine has quickly become a part of many developers’ daily process as the top AI tool for VS Code (5M since 2018) and products from the IntelliJ family, such as JetBrains (2M since 2016).
Last year, as part of our goal to double the productivity of R&D teams within organizations, we introduced Tabnine Enterprise. Building on our integration with GitLab, BitBucket, and GitHub, this offering addresses the needs of our enterprise customers, including enhanced security and privacy, self-hosting, AI model training on private code repositories, and central configuration.
Now, we’re leveraging our existing platform to broaden our offering. We all know how important it is to test our code thoroughly, but writing unit tests is often time-consuming, repetitive, and tedious. As a result, developers tend to neglect thorough testing, leading to less reliable software and production issues.
Our new unit test generation capability uses cutting-edge AI technology to generate unit tests for your code automatically, helping ensure that your code is rigorously tested, resulting in fewer bugs and better code stability – especially important for larger projects or projects with tight deadlines.
This new capability supports multiple programming languages, including Python, Java, and Javascript. It’s also easy to integrate with your existing development environment, currently supporting VSCode, with a user interface that’s simple and intuitive, requiring minimal setup.
However, the most unique feature of Tabnine’s unit test generation is that it learns from your code as you write it. This means that the more you use the tool, the better it gets at generating unit tests that match your coding style and patterns.
We believe that Tabnine’s unit test generation can make a real impact in ensuring your code is covered by thorough, effective automated tests, allowing your team to quickly and reliably ship top-quality software for your customers.
Want to get early access? Sign up now for the Beta version.
As we broaden our enterprise offering, this new capability is just of several more coming in 2023, including search for code similarity and code explanations – so be sure to follow us and stay updated!
The popularity of generative AI has broken out of tech circles and become a household term, with seemingly everybody talking about the amazing things that AI can do for them.
But for many software developers, generative AI has been an integral part of their lives for quite some time. Code assistants such as Tabnine and others have reached wide adoption among developers, and we’re now seeing more and more demand for an AI development assistant at the organization level, and not just at the individual developer level.
In the following sections, we’re going to describe Tabnine Enterprise and how it helps organizations create higher quality code faster with the help of AI.
Organizations can expect to gain significant benefits by deploying AI in their SDLC:
Tabnine Enterprise offers contextual code suggestions that automate repetitive coding, generating high-quality, best-practice code. Based on Large Language Models trained on billions of lines of code from credible open source licenses, Tabnine provides:
Tabnine generates ~30% of code, contributing to the following factors:
Smooth integration into the existing development workflow
Unlike AI chatbots like ChatGPT, Tabnine perfectly integrates into existing tools and processes. This means that no process change is required and you start getting value from day one. Tabnine functions as an extension of the development workflow directly within IDEs, with plugins available for all recent versions of Visual Studio Code, IntelliJ (and all JetBrains IDEs), Jupyter Labs, Visual Studio (full support for VS 2022 coming Q3), and Eclipse (full support coming Q3). Implementation is both fast and painless!
Battle-tested with millions of developers
Initially released in 2018, Tabnine isn’t only the most mature AI assistant for software development, but with millions of users worldwide, it’s also the most widely used product on the market. This is important because expertise matters. While many companies can train or serve Large Language Models for code prediction, the real trick is serving the day-to-day needs of the developers with the right suggestion at the right time with the correct scope and context. Tabnine is the result of countless iterations and improvements based on feedback from professional developers who use our product every single day.
Trained on code with permissive license only (no GPL etc., no ambiguity)
Tabnine is only trained on open-source code with a permissive license.. This decision has painful implications for Tabnine in terms of acquiring training data, but it helps ensure that developers can use the code that Tabnine generates in commercial projects without uncertainty about open-source licenses. Moreover, training our AI on code with permissive licenses only fully respects the intent of the developers who contributed code to open source.
Learn more about how we keep our users’ complete privacy.
Provides tailored guidance by learning private projects code and patterns
While AI that’s been trained on open-source code can definitely accelerate development, projects of significant size have an “internal language” comprised of internal services, frameworks, and libraries with their APIs and idiomatic patterns of how to accomplish certain tasks in the codebase. Tabnine Enterprise’s AI models provide fully secured, tailored guidance by learning private projects’ code and patterns, making the AI assistance especially relevant when working with internal APIs and patterns. This increases not only the speed of development but also the consistency of the code and the ease of onboarding onto a new codebase.
Enterprise-grade security
Source code is a core asset of companies, and as such, security of services touching code is critical and typically needs to meet certain standards to comply with corporate regulations. Tabnine prioritizes user security, implementing robust measures to keep your data safe:
Future-proof architecture
Tabnine’s architecture decouples the product from any specific AI model used as a basis, while also connecting to any additional foundational models as soon as they become available. This means that when you choose Tabnine, you get on a platform that’s continuously improving, not just thanks to Tabnine’s own innovation, but also thanks to other community efforts for training better and stronger foundational models.
Tabnine Enterprise is an AI code generation tool that helps software engineering teams write high-quality code faster and more efficiently, accelerating the entire SDLC. Designed for use in enterprise software development environments, Tabnine Enterprise offers a range of features and benefits, including the highest security and compliance standards and features, as well as support for a variety of programming languages and IDEs.
Code reviews have long been a staple in software development, and their importance has only grown over time. With the advent of new technology, the way code reviews are conducted is undergoing significant transformation. In this blog post, we will delve into the benefits of code reviews, the challenges faced by developers, and ways to make code reviews more efficient in your organization.
Code reviews offer several benefits to software development teams:
1. An excellent way to train and transfer knowledge among team members. Code reviews serve as an opportunity for team members to share their expertise and knowledge. This helps to upskill and train junior developers and fosters knowledge transfer between team members.
2. Increase the quality of code by catching bugs and improving code readability and code reuse. By reviewing code, bugs and issues can be detected and addressed before they reach production. This leads to improved code quality and reduced technical debt. The review process also helps to improve code readability and promotes code reuse.
3. Help to hold developers accountable by “defending” the code. Code reviews help to hold developers accountable for their work by allowing other team members to review and critique the code. This accountability encourages developers to take pride in their work and write better code.
4. Promotes team collaboration and communication, allowing developers to share ideas and knowledge. The code review process promotes collaboration and communication among team members. This allows for ideas and knowledge to be shared, and can lead to more efficient and effective solutions to problems.
5. Enhances the overall security of the code by detecting potential vulnerabilities. Through code reviews, potential security vulnerabilities can be identified and addressed. This helps to enhance the overall security of the code and reduce the risk of security breaches.
6. Increases code efficiency by improving performance and reducing duplication. By improving code performance and reducing duplication, code reviews can increase the efficiency of the code. This leads to improved software performance and a better user experience.
7. Helps to maintain a consistent coding style and standards. Code reviews help to maintain a consistent coding style and standards across the team. This consistency improves the readability and maintainability of the code.
8. Provides an opportunity for constructive feedback and personal growth for individual developers. Finally, code reviews provide developers with an opportunity for constructive feedback and personal growth. Developers can receive feedback on their work and use this to improve their skills and knowledge.
Code reviews can be challenging for several reasons:
To improve the efficiency of code reviews, there are several things you can do in your organization:
The AI revolution began with rapid advancements in machine learning and natural language processing, leading to the development of more advanced AI systems that could understand and generate human-like text. In November 2022, OpenAI introduced ChatGPT, a highly sophisticated language model trained on a diverse range of internet text, capable of having conversational and information-seeking interactions with users. ChatGPT marked a significant milestone in the AI revolution, offering a new level of interactivity and intelligence in language-based AI systems.
The introduction of AI-generated tools has revolutionized coding practices in recent years. AI-powered tools like Tabnine assist developers in their coding tasks, making the process quicker, more efficient, and less error-prone. These tools leverage machine learning algorithms to learn from code patterns and offer smart completions and suggestions that save developers time and effort. The integration of AI in coding tools is helping to enhance the development experience for developers and make coding more efficient and productive.
In 2023, the trend will continue, and we believe that the future of code reviews lies in AI-generated tools that can do part of the code review for you. These tools can reduce the annoying remarks on code readability, coding standards, and shift the initial review to your IDE with your AI assistant. This will reduce many of the remarks and interpersonal conflicts associated with code reviews. However, it is essential to leave the time of the expert to focus on the things that AI won’t be able to cover, such as business logic.
At the forefront of AI-powered code review tools, we advise organizations to stay up to date with the latest advancements in this field. As leaders in delivering AI solutions for developers, we continually add new tools to enhance workflows. If you’re interested in exploring how to automate code review and streamline other development team processes while maintaining privacy and security, please reach out to us.
Tabnine is an AI assistant tool used by over 1 million developers from thousands of companies worldwide. Tabnine Enterprise has been built to help software engineering teams write high-quality code faster and more efficiently, accelerating the entire SDLC. Designed for use in enterprise software development environments, Tabnine Enterprise offers a range of features and benefits, including the highest security and compliance standards and features, as well as support for a variety of programming languages and IDEs.
In conclusion, code reviews are an essential part of software development, and the way they are performed is evolving rapidly. With the help of AI-generated tools, the future of code reviews looks bright, and we’re excited to see how it will continue to shape the way we develop software in the future.
An AI coding assistant is a type of software tool that uses artificial intelligence (AI) to help developers write and debug code more efficiently. These tools can be used to provide suggestions and recommendations for code improvements, detect and fix errors, and offer real-time feedback as the developer is writing code.
AI code assistants can be integrated into an integrated development environment (IDE) or used as standalone tools. They use machine learning algorithms and natural language processing (NLP) to analyze code and provide intelligent recommendations based on best practices and coding standards. Some AI code assistants also have the ability to learn from the developer’s past coding habits and style, making their recommendations more personalized and relevant.
Increased productivity: Automate repetitive tasks, such as code completion and error checking, allowing developers to focus on more complex tasks.
Improved code quality: Identify and flag potential errors and bugs, reducing the likelihood of introducing new ones.
Streamlined collaboration: Help developers navigate large code bases, find relevant code snippets, and understand the intent of existing code.
Personalization: Some AI code assistants can be trained on specific code patterns and style, providing personalized recommendations and suggestions.
Here are some notable use cases for an AI-powered code assistant:
Web development: Assist with code completion, error checking, and debugging for web development languages such as JavaScript, HTML, and CSS.
Machine Learning (ML): Assist with code completion, error checking, and debugging for machine learning frameworks such as TensorFlow, Keras, and PyTorch.
Mobile development: Assist with code completion, error checking, and debugging for mobile development languages such as Swift and Kotlin.
Data Science: Assist with code completion, error checking, and debugging for data science languages such as Python and R.
An AI code completion tool provides recommendations to developers as they write code, helping them spot and correct issues without requiring manual checks. Fixing coding errors early in the code lifecycle is important to reduce the cost and technical burden of the software. Bugs become more difficult to address later in the development lifecycle and can negatively impact end-users once deployed to production.
Enforces coding best practices – code completions are more readable and consistent, improving code maintainability and ensuring the code is more reliable.
Reduces code review workload – coding assistants perform basic code reviews, leaving more time for human reviewers to identify deeper issues in the code, implement best practices and debug business logic issues.
Promotes use of safe libraries – coding assistants base their recommendations on common programming practice, so will typically recommend popular, well maintained third party APIs and libraries, which can reduce issues in production.
Promoting safe code reuse – some AI code assistants train on code previously created in an organization’s private code repositories. This means suggestions are based on trusted code that meets the organization’s quality standards – reducing the risk of low quality dependencies and bugs in third party libraries.
Here are some factors to consider when choosing an AI code assistant software:
Compatibility: Make sure the AI code assistant is compatible with the programming languages and technologies you are using.
Features: Consider the features that are most important to you, such as error detection, code completion, and real-time feedback.
Personalization: Evaluate if the solution is able to train on your organization’s codebase and integrate your individual best practices and quality standards into its code completions.
Integration: If you are using an integrated development environment (IDE), check to see if the AI code assistant integrates seamlessly with your IDE.
Ease of use: Look for an AI code assistant that is easy to use and has a user-friendly interface.
Cost: Consider your budget and compare the costs of different AI code assistants to find the one that fits your needs and price range.
Support: Look for an AI code assistant that offers good customer support, such as online documentation, tutorials, and community forums.
Reputation: Read reviews and ask other developers for their recommendations to get a sense of the reliability and effectiveness of different AI code assistants.
Minimizing production errors is of utmost importance in software development as it guarantees a dependable and stable application for end-users. Tabnine, an AI-based code completion tool, aids developers in reducing production bugs by offering suggestions for code snippets based on the current context and team-specific coding standards. With over a million developers from various companies utilizing Tabnine as their AI assistant, it has become a widely adopted tool in the software development community. Tabnine Enterprise is specifically designed for use in corporate software development environments, offering enhanced security and compliance features, as well as support for multiple programming languages and integrated development environments. By utilizing Tabnine, developers can focus on complex problem-solving while the tool handles routine tasks, resulting in fewer production bugs and a faster, more efficient software development life cycle. If you’re looking to enhance your software development practices while prioritizing privacy and security, please reach out to us.
