Advanced AI tools like GitHub Copilot and ChatGPT transform how developers write and understand code. However, there are several essential differences and distinct features that set these tools apart.
This guide compares GitHub Copilot and ChatGPT in depth, explaining their functionalities, use cases, benefits, limitations, and most importantly, concerns and considerations for organizations seeking to leverage these tools.
GitHub Copilot is an AI-powered code completion tool developed by GitHub in collaboration with OpenAI. Launched in 2021, it’s built on top of OpenAI’s Codex, a powerful language model trained on a vast corpus of code and text from the internet. Copilot is a programming assistant designed to help developers write code more efficiently.
By understanding the context and intent of the code being written, Copilot can suggest relevant code snippets, automating parts of the coding process. It supports various programming languages and frameworks, including JavaScript, Python, HTML, CSS, and more.
GitHub Copilot is trained on a vast corpus of code, creating the risk that some of the code it produces might not follow coding best practices or might contain security vulnerabilities. Organizations should exercise caution and carefully review GitHub Copilot code before using it in software projects.
ChatGPT is an advanced AI language model developed by OpenAI, based on the GPT-4 architecture. It’s designed to understand and generate human-like text and code, enabling it to engage in natural language conversations and provide informative responses. It’s able to accept nuanced instructions and produce code in any programming language, with natural language comments and explanations.
Trained on a diverse dataset from the internet, ChatGPT possesses extensive knowledge across various domains up to a cutoff date in 2021. OpenAI has recently added plugins that allow ChatGPT to browse the Internet and access more current data.
While ChatGPT can assist with answering questions, drafting content, and providing suggestions, its output may be inaccurate or biased due to its training data. Users should exercise critical thinking when using ChatGPT and verify any critical information obtained from it.
GitHub Copilot and ChatGPT are both AI-powered tools developed by OpenAI, but they have distinct purposes and features that cater to different user needs:
When comparing GitHub Copilot and ChatGPT for organizational use, several factors come into play:
When considering the integration of AI into your software development, it’s vital to take the following into account:
Tabnine is an AI assistant you can trust and that you control, built for your workflow and your environments. Using Tabnine, you get full control over your data, since Tabnine can be deployed in any way you choose: as SaaS, on-premises, or on VPC.
Unlike other AI coding assistants, Tabnine’s models are fully isolated without any third-party connectivity. Tabnine also doesn’t store or share user code. So whether it’s a SaaS, VPC, or on-premises deployment, your code is private and secured.
Tabnine’s generative AI is only trained on open source code with permissive licenses:
In addition to inline code completion in the IDE, we also offer Tabnine Chat, an AI assistant that sits in your IDE and is trained on your entire codebase, safe open source code, and every StackOverflow Q&A.
Tabnine Chat is always available right in the IDE, and can:
Tabnine’s code suggestions are based on large language models that are exclusively trained on credible open source licenses with permissive licensing. Tabnine’s world-class AI models are continually evolving and improving, so they remain at the forefront of technology.
Advantages for enterprises:
Tabnine is built on best-of-breed LLMs (with the flexibility to switch as new models emerge or improve) while offering 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 supports a wide scope of IDEs and languages, and we’re adding more all the time. Tabnine also provides engineering managers with algorithmic visibility into how AI is used in their software development process and the impact it has on your team’s performance.
Tabnine believes in building trust through algorithmic transparency. That’s why we provide our customers with full visibility into how our models are built and trained. We’re also dedicated to ensuring our customers’ interests are protected by only training on code with permissive licenses and only returning code recommendations that won’t be subject to future questions regarding ownership and potential litigation. At Tabnine, we respect open-source code authors and their rights as well as the rights of every one of our customers.
Get started with Tabnine for free today, or talk to an expert to learn how we can help your engineering team be happier and more productive.
In the world of coding, developers are always looking for tools and plugins that can streamline their workflows and make coding more efficient. Tabnine’s developer Amir Bilu recently discovered Tabnine Chat within VS Code, and he was completely blown away! Using Tabnine Chat, he could interact with his code using natural language and perform various tasks effortlessly.
Amir’s enthusiasm for Tabnine Chat led him to explore leveraging its power in other IDEs. Being an avid Neovim user, Amir took on the challenge of integrating Tabnine Chat into his beloved IDE, Neovim.
With determination and creativity, Amir successfully integrated Tabnine Chat into Neovim using Tauri, an innovative framework for creating cross-platform desktop applications. This integration opened up new possibilities for Neovim developers, empowering them with the same incredible features that Amir had come to rely on. Amir Bilu’s journey with Tabnine Chat is a testament to the transformative impact of innovative developer tools. This powerful plugin enabled Amir to streamline his coding process, improve code quality, and ultimately become a more productive developer. His successful integration of Tabnine Chat into Neovim is sure to inspire other developers to explore new horizons and embrace the power of natural language in their coding adventures.
To read more about it, check out the full article.
In an insightful webinar hosted by Tabnine’s CTO and co-founder, Eran Yahav, and VP of Ecosystems, Brandon Jung, they engaged in a comprehensive discussion about the advancements, challenges, and practical applications of leveraging language models. The webinar provided valuable insights into the current landscape of language models, and the advancements, challenges, and practical applications of leveraging language models for AI code assistance.
In this webinar, you’ll discover the latest developments in generative AI for code and beyond. Gain insights into how large language models (LLMs) work, their potential to solve complex problems, and their transformative impact on software development. The discussion also touches upon the trend of increasing model sizes and explores the implications of LLMs, including concerns related to bias, privacy, and security.
From diving into the underlying technologies to exploring the possibilities and limitations, this webinar provides an in-depth exploration of the trends driving AI machine learning with large language models.
Watch the full session below:
Tabnine’s code suggestions are powered by secured models that prioritize the confidentiality of your code. These models are designed to keep your code private while providing accurate and efficient suggestions. If you’re an enterprise looking to incorporate AI into your software development life cycle, Tabnine Enterprise is an exceptional option. With Tabnine Enterprise, you’ll not only benefit from contextual code suggestions that boost productivity and streamline coding tasks but also ensure the privacy and security of your code.
By leveraging Tabnine Enterprise, you can confidently enhance your software development process with AI-powered code assistance while maintaining the utmost security and privacy of your codebase.
Amazon CodeWhisperer is an AWS service that offers real-time, AI-driven code suggestions. Utilizing large language models (LLMs) and an extensive library of open-source code, it comprehends the context of your project and provides relevant recommendations as you type.
This is part of a series of articles about ChatGPT alternatives.
Amazon CodeWhisperer provides the following main features:
The pricing details below are subject to change. For up-to-date pricing information, see the official pricing page.
The individual tier is free and simple to set up, but it does not include the benefits of organizational license management.
If you are using CodeWhisperer at the individual tier, you can:
The professional tier incurs charges for additional features, with your employer covering the costs through their company AWS account.
Pricing for the CodeWhisperer Professional Tier is calculated on a “per user, per month” basis. Organizations are billed monthly based on the maximum number of users who have access to CodeWhisperer during a calendar month’s billing period. At the time of this writing, the professional tier costs $19 per user.
If you are using CodeWhisperer at the professional tier, you can:
GitHub Copilot, an AI-driven software development tool by Microsoft-owned GitHub, was introduced in 2021 and became generally available in 2022.
There are some notable differences between GitHub Copilot and Amazon CodeWhisperer:
Despite its numerous benefits, developers and organizations may face some challenges when implementing Amazon CodeWhisperer, including:
Tabnine is an AI code assistant used by over 1 million developers from thousands of companies worldwide. It provides contextual code suggestions that boost productivity, streamlining repetitive coding tasks and producing high-quality, industry-standard code. Tabnine’s code suggestions are based on Large Language Models that are exclusively trained on credible open-source licenses with permissive licensing. With Tabnine Enterprise, developers have the flexibility to run the model on-premises or in a Virtual Private Cloud (VPC), ensuring full control over their data and infrastructure while leveraging the power of Tabnine to comply with enterprise data security policies.
Advantages for enterprises:
Tabnine Chat
Tabnine has recently released Tabnine Chat which is an AI assistant trained on your entire codebase, safe open source code, and every StackOverflow Q&A, while ensuring all of your intellectual property remains protected and private.
Tabnine Chat is always available for you, right in the IDE, to:
Today is a very exciting day at Tabnine: I’m thrilled to announce the launch of Tabnine Chat in Beta!
Tabnine Chat is an enterprise-grade, code-centric chat application that allows developers to interact with Tabnine’s AI models in a flexible free-form way, using natural language.
While ChatGPT and other tools are useful for generating “first draft” code, Tabnine Chat aims to support the workflows of professional developers working on big projects, especially in enterprises, via several key attributes:
Our focus on developers was proven recently when Stack Overflow highlighted Tabnine as one of the two leading AI tools that developers are using.
Tabnine Chat is a huge leap, expanding the applicability of Tabnine beyond code generation. Using Tabnine Chat is easy and intuitive, and at the same time flexible and powerful.
Here are some of the notable things developers can do with Tabnine Chat:
1. Explain a piece of code, which is especially useful when reading a new codebase.
2. Search your code repos using natural language, giving you the ability to “talk with your codebase.”
3. Generate new code based on natural language specs (e.g., “Create an app that reads the weather in London”).
4. Extend code with some capabilities (e.g., “Add logging to this code”).
5. Refactor code using human language (e.g., “Add type specification,” “Change convention,” etc.).
6. Generate documentation (docstrings) for specific sections of code.
7. Find issues in the code and fix them.
8. Generate unit tests.
This is just the tip of the iceberg! With some experimentation, I’m sure you’ll find creative and useful ways to really enhance your workflow with this exciting product. Learn more about Tabnine Chat’s use cases.
Want to try out Tabnine Chat? Click below to fill out the form and get an invite to the Beta, and let us know if there are any additional use cases you’d like to explore. We look forward to a quick Beta with our trusted testers and then rolling it out broadly to Tabnine Enterprise and Pro users in the coming months.
Exciting news from the Stack Overflow 2023 Developers Survey!
According to the latest survey results, software development is undergoing a remarkable shift. The survey highlights that AI is becoming an integral part of the developer’s workflow. This shift is revolutionizing the way that developers innovate and create.
Tabnine is the only independent AI tool for software development being used by developers. We’re deeply grateful to all the developers and enterprises who have supported us throughout our incredible journey.
At Tabnine, our commitment remains unwavering. We’re dedicated to providing developers with innovative, ethical, and secure AI solutions everywhere. By leveraging AI’s immense potential, we aim to help developers reach new heights of productivity and creativity.
Join us on this remarkable journey as we continue to fulfill our promises and shape the future of software development.
In case you’re an enterprise looking to incorporate AI into your software development life cycle, Tabnine is an exceptional option.
By utilizing Tabnine Enterprise, you’ll have the opportunity to benefit from contextual code suggestions that can boost your productivity by streamlining repetitive coding tasks and producing high-quality, industry-standard code. Tabnine code suggestions are based on large language models that are exclusively trained on credible open-source licenses with permissive licensing.
GitHub Copilot for Business is an advanced AI-powered code completion tool specifically designed for enterprise developers and organizations. It automatically generates code by leveraging machine learning models trained on vast amounts of public code repositories. By integrating seamlessly with popular IDEs and code editors, Copilot for Business streamlines the development process and reduces repetitive tasks.
However, like the basic version of GitHub Copilot, GitHub Copilot Business creates the risk that some of the code it produces might not follow coding best practices, or might contain security vulnerabilities. Organizations should exercise caution and carefully review GitHub Copilot code before using it in software projects.
An image from a McKinsey report on developer productivity with generative AI, published in June 2023
GitHub Copilot offers several benefits for businesses, helping to improve efficiency, productivity, and overall code quality. Some of these benefits include:
Similar to GitHub Copilot for individuals, GitHub Copilot functions by transmitting code snippets from your IDE to GitHub.
There is limited control over code security, and the GitHub Copilot may not provide comprehensive protection against intellectual property leaks. GitHub Copilot’s model is based on open-source licenses, including some that are non-permissive, as well as GPL licenses with Copyleft clauses. GitHub Copilot’s training dataset includes a wide variety of public code, including licenses like GPL with non-permissive terms. This scenario could potentially expose companies to legal vulnerabilities.
Although GitHub Copilot brings numerous benefits to businesses, its implementation also poses certain challenges that require careful consideration. To successfully integrate the tool into existing workflows, organizations must thoroughly evaluate these challenges, strike a balance between the advantages of Copilot and the associated risks, and implement suitable measures to mitigate any potential issues.
Copilot Business is only available for companies with GitHub Enterprise, which costs $210 per user per year for the entire organization, which can add up to 100s of users.
If you want to use GitHub Copilot for Business in your organization or enterprise, you must first establish a policy for the use of GitHub Copilot. Once GitHub Copilot is enabled at the enterprise-level, you can configure GitHub Copilot settings for each organization in your enterprise.
Tabnine is an AI code assistant used by over 1 million developers from thousands of companies worldwide. It provides contextual code suggestions that boost productivity, streamlining repetitive coding tasks and producing high-quality, industry-standard code.
Unique enterprise features
Tabnine’s code suggestions are based on Large Language Models that are exclusively trained on credible open-source licenses with permissive licensing. With Tabnine Enterprise, developers have the flexibility to run the model on-premises or in a Virtual Private Cloud (VPC), ensuring full control over their data and infrastructure while leveraging the power of Tabnine to comply with enterprise data security policies.
Advantages for enterprises:
Customized for your organization
In essence, Tabnine is an AI code assistant that helps developers based on their unique codes and preferences, while protecting privacy of all users. Tabnine generates consistent and high-quality code suggestions across teams, reducing noice and helping prevent common errors.
Tabnine Chat
Tabnine has recently released Tabnine Chat, an AI assistant trained on your entire codebase, safe open-source code, and every StackOverflow Q&A, while ensuring all of your intellectual property remains protected and private.
Tabnine Chat is always available for you, right in the IDE, to:
Learn more about Tabnine Chat Beta here.
Discover the latest blog post on Google Cloud, where we delve into the pivotal role of AI in software development and its ability to accelerate the SDLC. Tabnine AI-powered assistant empowers developers by predicting and suggesting code lines based on context and syntax, thereby boosting their productivity and enabling the creation of new content. With its adaptability to different coding preferences, Tabnine enhances code quality and enables developers to produce up to 40% more code.
Tabnine’s commitment to ethical practices is exemplified by its utilization of fully permissive open-source code and training on specific company data, resulting in powerful and personalized outcomes. Tabnine harnesses Google Cloud’s advanced computing power and sophisticated data segmentation to support its advanced capabilities.
In this blog post, Brandon Jung, Tabnine’s Vice President of Ecosystems, provides insights into the use of customized and open source AI models, fostering faster innovation. Additionally, we showcase the success story of CI&T, a global IT company with over 7,000 employees. CI&T adopted Tabnine in 2022 to support its extensive developer team working with 18 different coding languages. Through Tabnine, CI&T achieved an impressive 11% increase in productivity, with developers accepting 90% of the tool’s single-line coding suggestions. Luis Ribeiro, Head of Engineering and Digital Solutions at CI&T, emphasizes the significance of AI in driving efficiency and innovation, particularly in regulated industries such as healthcare, life sciences, and financial services.
For the complete blog post, visit the link here.
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.
The world of software development is constantly evolving, and as developers, we want to stay up to date on the latest technological advancements. AI has emerged as a powerful tool that can help us write better code faster and more efficiently. To shed light on how to integrate AI into your coding workflow, we recently conducted a webinar with Dror Weiss, Tabnine’s CEO, and Brandon Jung, VP of Ecosystems. Here are some of the key insights from the webinar:
Selecting the right AI tools for your specific needs
The initial step in incorporating AI into your coding workflow is to carefully select an appropriate AI tool that caters to your specific needs. Tabnine, being a leading AI-assisted software development tool, is a popular choice among developers with over a million users relying on it for faster and more accurate coding. In fact, Tabnine produces about 30% of the code generated by its users. By utilizing deep learning algorithms to analyze the context of your code, it generates intelligent suggestions in real time, thereby saving time and minimizing the chances of errors. While AI can make developers more efficient and content, it’s crucial to assess your requirements, such as privacy regulations or company policies, before opting for an AI tool.
Impact of AI assistance on coding practices
Adding AI assistance to coding practices yields significant improvements in various aspects of software development, including code reuse, API identification, password encryption, natural language-to-code conversion, and code consistency. One major AI-powered tool in this domain is Tabnine, which offers suggestions for appropriate syntax and variable names, resulting in enhanced code quality and heightened productivity. The combination of human intelligence with AI empowers developers to automate repetitive code, maintain workflow momentum, and prevent errors, enabling them to devote more attention to creative tasks.
By adopting Tabnine Enterprise, developers can leverage contextual code suggestions that streamline repetitive coding tasks and generate high-quality, industry-standard code.
Tabnine’s code suggestions stem from large language models trained exclusively on reputable open source licenses with permissive licensing. This integration presents several advantages, including the generation of approximately 30% of the code, automation of repetitive coding tasks, consistent and high-quality code suggestions across teams, noise reduction to facilitate focused coding, and prevention of common errors.
As the AI layer for coding progresses, it’s expected to become an integral part of the development stack, playing a pivotal role in every stage of the software development lifecycle.
How to integrate AI into your organization
When integrating AI into your organization, it’s essential to evaluate options based on factors such as code suggestion quality, performance, security, and IP protection. Additionally, IDE support and the tool’s ability to learn your code are important considerations. To begin, evaluate the AI tool with a group of 15-25 developers for one month and choose an internal champion to lead the implementation. Provide quick training to ensure your team can make the most of the tool. After the pilot period, analyze the ROI and assess the subjective productivity gains. If successful, expand usage to other groups and specialize AI guidance by connecting your code and domain experts. By following these steps, you can effectively integrate AI into your organization and enjoy the benefits of improved code quality, increased productivity, and reduced errors.
About Tabnine Enterprise
Tabnine Enterprise is designed to help software engineering teams improve the quality and speed of their code development process. By using Tabnine Enterprise, teams can take advantage of various tailored features and benefits, including industry-leading security and compliance standards. Additionally, Tabnine Enterprise offers the flexibility of running the tool on-premises or in a virtual private cloud (VPC), allowing for greater control over data and infrastructure. This enables teams to fully leverage the capabilities of Tabnine while adhering to their organization’s data security policies. To learn more about how Tabnine Enterprise can benefit your organization, don’t hesitate to contact our team of enterprise experts.
Managing AI risks
When utilizing AI tools, it’s essential to be aware of the potential risks involved and take necessary precautions to manage them. These risks encompass concerns regarding privacy, security, open source usage, IP, and maintaining control over your code. Tabnine Enterprise addresses these risks by implementing robust security measures, including the avoidance of training on customer code, running the tool locally within the customer’s environment, and refraining from training on non-permissive code.
Tabnine AI code completion models can run locally, on self-hosted servers, within VPC, or completely offline, ensuring you have complete control and ensuring compliance with your organization’s policies.
Tabnine models are exclusively trained on repositories with permissive open source licenses. The platform follows strict protocols where customer code is used solely for model querying and is immediately discarded after the query. Your code is never stored, shared, or incorporated into Tabnine’s open source trained AI model, ensuring the confidentiality of your proprietary code.
By considering these factors you can effectively manage the risks associated with AI integration into your coding practices.
In conclusion, integrating AI into your coding workflow can be a game-changer for developers, enabling them to write better code faster and more efficiently. By selecting the right AI tool for your specific needs, managing the potential risks associated with AI use, and leveraging the full potential of AI for code generation, review, optimization, and project management, you can take your coding workflow to the next level. To learn more about Tabnine and how it can help you optimize your coding workflow, check out the video of our recent webinar.
AI models like GPT-4 are in high demand, but establishing the optimized infrastructure to support them can be expensive and complex. There are many organizations facing the challenge of balancing security and compliance requirements while maintaining the computational power needed to run generative AI at a massive scale. For this reason, some organizations choose to host their systems in their own data centers.
Tabnine understands the challenges of acquiring the hardware necessary for these AI advancements. While our cloud solution provides a convenient and expedient option, we acknowledge that it may not meet the stringent security prerequisites of all our customers. In an article for TechTarget Business Technology, our CEO, Dror Weiss, discussed the obstacles associated with implementing generative AI in enterprise environments, including security, infrastructure demands, and integration with existing systems.
As we continue to push the boundaries of AI, we must remain cognizant of these challenges. For a comprehensive understanding of this topic, we invite you to read the full article.
If you’re an enterprise looking to integrate AI into your software development lifecycle, Tabnine is a great option.
You can boost your productivity by streamlining repetitive coding tasks and producing high-quality, industry-standard code with Tabnine Enterprise. Tabnine code suggestions are based on Large Language Models that are exclusively trained on credible open-source licenses with permissive licensing. You can also run Tabnine Enterprise on-premises or in a virtual private cloud (VPC), ensuring that you maintain full control over your data and infrastructure. For more information on how Tabnine Enterprise can benefit your organization, feel free to contact our enterprise expert.