Enterprise vs. GitHub Copilot Enterprise: Сomparing AI Сoding Assistants for Companies

March 31, 2024
by Oleg Klimov

Many companies enlist the help of AI coding assistants to boost developers’ productivity. This is obvious, given the opportunities they are providing: make development faster, handle mundane routines, and improve the overall quality of code. Developers waste less time on repetitive tasks and save time for more important challenges, feeling more content with what they have accomplished at the end of the day. It is a power of AI code generation in action!

While the most widely recognized player in the market remains GitHub Copilot, its functionality does not always meet the requirements of software development in 2024. It’s a known fact that Copilot lacks security features, moreover, the quality of its suggestions is sometimes questionable. And it’s expensive! stands as a secure Copilot alternative with on-prem and LLM fine-tuning, which has a better accuracy and is affordable to any business! This makes a great coding tool for companies looking to enhance code delivery productivity.

This post compares Enterprise to Copilot for Enterprises, based on the key parameters important to developers, R&D teams, and software companies.

One last thing worth mentioning is that Copilot also offers a limited Business plan that costs $19. This is (spoiler) twice as expensive as and offers such limited functionality that we didn’t include it in our comparison. Enterprise vs. GitHub Copilot Enterprise Comparison

Price EnterpriseGitHub Copilot Enterprise
3 month free, then $10 per user/month>$39 per user/month*
* To use GitHub Copilot Enterprise, you need GitHub Enterprise Cloud, which costs $19/user/month. So, if you’re not already on this plan, the real cost you have to pay for GitHub Copilot Enterprise is $58/user/month ($39 + $19).

Code Completion EnterpriseGitHub Copilot Enterprise
In-line code autocompletions
Multi-line function suggestions
Comments to code
Codebase awareness
Context controlCan specify files, classes, repos, folders, or commands to add or retract certain files out of the analysisCan specify certain files, context retrieval is documented
Completion LLM✅ 14 models: Refact/1.6B, Starcoder base, Starcoder2, Wizardcoder, Codellama (full list)OpenAI’s

GitHub Copilot has a context awareness engine under the hood, which is generally described as “Okay” in terms of autocompletion quality as it has difficulties with some stacks and understanding the entire code base. This reduces its ability to assist you, and AI code suggestions by Copilot may not be that good.

Well, AI is not an all-seeing eye (hopefully), but there is a better technique that provides AI with a full repository knowledge. And has it! It is called RAG (Retrieval-Augmented Generation): LLMs become aware of your organization’s data and project context without needing to be pre-trained. Thanks to its deep knowledge, you can expect your code to be completed by AI with the utmost accuracy. also allows you to switch between LLMs to find the one that is more compatible for your specific dataset and coding needs.

Chat EnterpriseGitHub Copilot Enterprise
In-IDE Chat✅ ❌
Natural language question and search
Answers based on your organization code base
Question about your code
Custom prompts
Web search
Chat LLM✅ GPT-4, Anthropic Claude, Wizardlm, Llama2, Mistral, Mixtral, Deepseek-coder (full list)OpenAI’s

First and foremost: chat works right in your IDE’s workspace. You don’t need to switch between windows and create a mess — in chat, you can ask code questions, generate new code, make edits with easy code inputs and outputs, or even have a conversation with AI.

When completing your task, chat model can refer to an entire file or specific lines of code, using context analysis and/or models knowledge to provide you with the right answers.

Coding Tasks EnterpriseGitHub Copilot Enterprise
Bug detection
Code refactoring
Making code shorter
Code review
New code generation
Documentation generation
Writing unit tests

Both and Copilot are capable of helping you complete your coding tasks faster. The thing is how effectively it is executed. can detect bugs, refactor code, edit code components, and much more with the help of in-IDE chat and in-line AI Toolbox. The second feature is particularly useful and simple: select the code area to improve —> press F1 —> improve your code in a single click with /commands. Give it a try!

LLM Fine-Tuning EnterpriseGitHub Copilot Enterprise
Fine-tuning on code completion models
Fine-tuning on chat models
LLMs available for fine-tuning✅ 10 models (full list)

LLM fine-tuning is a key feature that adds speed and quality to your software development. In terms of numbers: our clients see an increase in code generated by a model from about 25% to 45% when they customize AI auto-completions around their data.

After training on your data, the model becomes an expert in your work domain, delivering AI code that is personalized to your coding domain. Plus, you can set up different fine-tunings for each team, so they can use a model that’s been pre-trained for their specific needs.

Fine-tuning is efficient and easy (the mechanics are described in this article), proving that your company can benefit from much more than from Copilot, as it doesn’t offer this feature at all.

Deployment EnterpriseGitHub Copilot Enterprise
As SaaS
On-Premise (GPU or VCP)

GitHub Copilot is a cloud-based service and can’t be deployed on-premise. This should be a consideration for companies with strict security requirements. provides various deployment options, including on-prem for your absolute control over your privacy and compliance. Self-hosted can be launched on a machine with an NVidia GPU, Runpod Cloud GPU, or AWS — you’re free to choose your favorite way to manage your data.

Security EnterpriseGitHub Copilot Enterprise
Training Data✅ Trained on permissively-licensed code❌ Datasets collected from public code repositories and users’ own code
Code privacy✅ Your data never leaves your control in on-premise mode. Telemetry from the plugins goes to your server and nowhere else, we never store it. You can choose the optimal level of privacy for your plugin.❌ Public code filter and compensations in case of data leakage

The code suggestions GitHub Copilot generates are based on a waste of public code, Copilot incorporates user data into the model training process and stores it in the cloud, meaning the user’s code is being shared. There are reports of data breaches and leakage of proprietary or sensitive information when using Copilot, illustrating that using Copilot always involves a risk for your company.

In contrast, is a secure AI coding assistant that prioritizes your company code privacy. We are open-source; our source code is available on GitHub, allowing everyone to verify how we treat data.

Statistics EnterpriseGitHub Copilot Enterprise
User access control
Per-User statistics

Both and GitHub Copilot provide you with user access control, but only gives you full statistics in Docker and plugins about how many completions writes for your developers every day. Measure productivity gains with real numbers!

Supported Environment EnterpriseGitHub Copilot Enterprise
Supported IDEs✅ VS Code, JetBrains, VS Classic, Sublime Text, Qt Creator✅ VS Code, JetBrains, VS Classic, Vim, Neovim, Azure Data Studio, CLI,
Supported languages✅ 25+ languages✅ All the popular languages


With, your company doesn’t have to choose between security, customization, AI coding accuracy, and a good price — you can have it all:

Privately deploy on-prem, fine-tune models, and make coding faster with the most precise code suggestions you’ve ever had as they are customized around your codebase and tech stack. Our clients have up to 45% of their code written by AI!