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.
What is GitHub Copilot?
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.
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.
What is ChatGPT?
ChatGPT is an advanced AI language model developed by OpenAI, based on the GPT-4 architecture. It is 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 vs. ChatGPT: 4 key differences
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:
1. Purpose and scope
- GitHub Copilot is designed for code generation and completion, making it the recommended option for developers working on code. It excels at understanding context and suggesting relevant code snippets across multiple programming languages and frameworks.
- ChatGPT is a more generalized AI language model that can engage in natural language conversations, answer questions, and draft content. While it can provide code explanations, it is better suited for beginners seeking assistance in understanding coding concepts.
2. Cost and availability
- GitHub Copilot offers a 60-day free trial, after which users must subscribe to a paid plan to continue using its services.
- ChatGPT can be used for free, making it more accessible to a broader audience who may need help with various topics, including beginner-level coding.
3. Learning and adaptation
- One of the key features of GitHub Copilot is its ability to continuously learn from user behavior and code, improving its suggestions over time. This personalization enables Copilot to better align with individual coding styles and preferences, enhancing its utility as a coding assistant.
- ChatGPT, while capable of generating contextually relevant responses, only remembers the code and context within a given conversation. It does not adapt to users’ preferences or learn from their input in the same way that Copilot does.
4. Target audience
- GitHub Copilot is primarily aimed at developers who need assistance in writing and completing code.
- ChatGPT caters to a more diverse audience, including non-programmers.
Github Copilot vs. ChatGPT for organizations
When comparing GitHub Copilot and ChatGPT for organizational use, several factors come into play:
- GitHub Copilot is a cloud-based service that doesn’t offer on-premise options. This may be a consideration for organizations with strict security or compliance requirements.
- ChatGPT is based on the GPT architecture, and might have the option of deploying a custom version of the model within an organization’s infrastructure, depending on OpenAI’s licensing and availability of the model. This could provide better control over data privacy and compliance. However, the ChatGPT tool is not available on-premises as-is.
- GitHub Copilot is designed to work with public and private repositories. However, the code suggestions it generates are based on a vast corpus of public code, and it is important to ensure that no proprietary or sensitive information leaks into the public domain through its usage.
- ChatGPT, being a more general language model, doesn’t directly interact with code repositories, but organizations should be cautious when discussing sensitive information within the chat environment. A recent data breach illustrates the risks involved when using ChatGPT or similar solutions.
- ChatGPT and GitHub Copilot’s machine learning models are trained on extensive datasets collected from public code repositories and users’ own code, incorporating this data into their model training process, meaning the users’ code is being shared.
- Organizations may need to comply with specific regulations, such as GDPR, HIPAA, or industry-specific standards. Both GitHub Copilot and ChatGPT, being AI-powered tools, may process or generate data that falls under these regulations. Organizations should ensure that they have the necessary agreements and policies in place with OpenAI and GitHub to remain compliant.
- At the time of writing, European Union (EU) regulators are starting work on the world’s first comprehensive AI legislation. Organizations should keep up to date with new legal developments and use them to evaluate tools like CoPilot and ChatGPT.
Integration with an organization’s code
- GitHub Copilot integrates with the popular code hosting platform GitHub, which is widely used by organizations for managing their code repositories. This makes it a convenient choice for developers working within these organizations.
- ChatGPT, however, doesn’t have direct integration with code repositories, as it primarily serves as an AI conversational partner. It’s important to note that ChatGPT-generated code lacks awareness of the code context.
Tabnine: A secure, enterprise alternative to Copilot and ChatGPT
When considering the integration of AI into your software development, it’s vital to take the following into account:
- Does the AI coding assistant provide a comprehensive platform with inline code completions and support via chat?
- Does the vendor support the IDEs and languages that are used by your team?
- Does the AI coding assistant use world-class models? Do the models evolve as technology improves?
- Is it possible to optimize the AI platform for your engineering team with tailored models and context awareness?
- Does the vendor offer complete privacy for your codebase and data around usage? Do they offer air-gapped deployments (on-premise or VPC) and/or guaranteed zero data retention?
- Was the AI coding assistant trained exclusively on code with permissive licenses?
- Does the vendor offer protection from legal risk by limiting the recommendations to software you have the rights to and not just promises of indemnification?
- Can the vendor meet your company’s expectations for security and compliance?
Tabnine: The AI coding assistant that you control
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-premise, or VPC cloud.
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-Prem deployment, your code is private and secured.
Tabnine’s generative AI is only trained on open-source code with permissive licenses:
- Tabnine ensures you control your code
- Best-in-class security &with SOC2 compliance
- Fully isolated AI models with zero data retention
- Trained exclusively on 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 for you, right in the IDE, to:
- Answer any of your questions regarding your code
- Generate new code from scratch
- Explain a piece of code
- Search your code repos for specific functions or pieces of code
- Refactor code
- Generate documentation (docstrings)
- Find and fix code issues
- Generate unit tests and more
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. Tabnine’s world-class AI models are continually evolving and improving, so they remain at the forefront of technology.
Advantages for enterprises:
- Trained exclusively on permissive open-source repositories
- Eliminates privacy, security, and compliance risks
- Avoid copyleft exposure and respect developers’ intent
- Can be locally adapted to your codebase and knowledge base without exposing your code
Personalized for your team
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, 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.