What Sets Them Apart
On paper both products do similar things: inline completion, chat-style assistance, repository-aware refactors, and an agent mode that can run multi-step tasks. Where they diverge is upstream of the editor. Copilot inherits OpenAI's frontier models and GitHub's deep telemetry on how millions of developers actually code, while Cody inherits Sourcegraph's structural index of your repositories and a multi-model backend that lets the same chat thread switch between Claude, GPT, and Sourcegraph's own models depending on the task.
GitHub Copilot and Cody at a Glance
GitHub Copilot is the default AI assistant for the GitHub ecosystem. It runs natively in VS Code, JetBrains IDEs, Neovim, the GitHub web UI, and a CLI, and it ships with inline completions, a chat sidebar, code review suggestions, and an agent mode that can plan and execute multi-step changes. Pricing is tiered against developer plans rather than usage: a free tier with 2,000 completions per month, a Pro tier at $10 per month, and a Business tier at $19 per user per month with organization controls and content exclusion settings.
Sourcegraph Cody comes at the assistant from the opposite direction. The product is built on top of Sourcegraph's code intelligence platform, which already indexes your entire codebase for symbol search and structural references. Cody uses that index to ground its answers in real code rather than relying purely on the language model's training data. It runs in VS Code, JetBrains, and the web, with a free tier for small teams, a Pro tier at around $9 per user per month, and an Enterprise tier with custom pricing that includes self-hosted deployment options.
The model story is also different. Copilot is essentially an OpenAI front end with periodic upgrades to whichever GPT-class model GitHub has rolled out, plus optional access to Anthropic and Google models on the Business and Enterprise tiers. Cody exposes a multi-model selector in the chat panel so individual developers can pick between Claude, GPT, Mixtral, and Sourcegraph's own models per question, which is useful for teams that want to compare outputs or route sensitive prompts to specific providers.
Codebase Context and Repository Awareness
Where Cody clearly leads is repository awareness. Because the assistant sits on top of Sourcegraph's index, it can answer questions like 'where is this function called across the monorepo' or 'which services use this Kafka topic' without needing the relevant files to be open in the editor. For large enterprise codebases with hundreds of services and millions of lines of code, that structural awareness is the entire reason teams adopt Cody.
Copilot has been closing this gap with codebase indexing and agent mode, but the underlying model is still 'whatever files we can fit in context plus signals from the GitHub workspace.' For most repositories that is enough — the 80% case is editing one file and looking at two adjacent ones — but it does not match Cody's ability to reason about a fifty-service monorepo as a structured graph rather than a folder tree.
On smaller projects this advantage flips. If your codebase fits comfortably in an LLM context window, Copilot's tighter integration and better editor latency tend to feel more useful than Cody's structural index. For a typical web app or microservice, the difference between the two assistants is mostly stylistic; for a 500-engineer enterprise monorepo, it is the difference between 'AI knows my project' and 'AI knows whatever I happened to open.'
Pricing, Privacy, and Enterprise Controls
Pricing structures push the two products toward different buyers. Copilot's per-developer pricing and tight bundling with GitHub Enterprise make it the easy default for organizations already standardized on GitHub — finance teams approve it on the same purchase order as their source control. Cody's pricing is competitive at the individual level but the real story is at Enterprise tier, where custom contracts include self-hosted deployment, model BYO, and integration with private code search indexes that customers may already pay Sourcegraph for.
Privacy and data handling also diverge. Copilot offers content exclusion on Business and Enterprise tiers and a 'do not train on my code' setting, but the assistant inherently routes prompts through GitHub and OpenAI infrastructure. Cody's enterprise plan supports air-gapped self-hosted deployment with the model running inside the customer's network, which is the deciding factor for teams in regulated industries that cannot send code outside their perimeter.
The Bottom Line
Pick GitHub Copilot if your team already lives in the GitHub ecosystem, your codebase fits comfortably inside a model context window, and you value the lowest-friction adoption path with the broadest IDE coverage. Pick Cody if you maintain a large multi-repo enterprise codebase, you want a multi-model assistant grounded in a structural code index, or you need self-hosted deployment for regulatory reasons. Both are credible choices; the right answer depends almost entirely on the size and shape of the codebase you are asking the assistant to understand.