Sourcegraph Cody and Cursor both address the same problem: making AI coding assistance work on real, large codebases — not just toy examples. Their approaches differ significantly.
The Context Problem in AI Coding
The challenge with AI coding tools on large codebases: LLMs have fixed context windows. You can’t fit a million-line codebase into a prompt. The solution matters — it determines how well the AI understands your code.
Cursor indexes your codebase locally and retrieves relevant code snippets for each query using vector similarity.
Cody is backed by Sourcegraph’s code intelligence infrastructure, which has understood code relationships at scale for years. It uses Sourcegraph’s AST parsing and cross-repository search to retrieve context.
Sourcegraph Cody
Cody is Sourcegraph’s AI coding assistant, available as a VS Code extension, JetBrains plugin, and other editors.
Enterprise Codebase Understanding
Cody’s differentiator is enterprise-grade codebase intelligence through Sourcegraph. If your organization already uses Sourcegraph for code search, Cody integrates directly with that index — meaning it can understand context across all your repositories, not just the one you have open.
Ask Cody a question and it can retrieve context from:
- The current file
- Related files in the same repo
- Cross-repo dependencies
- Internal libraries and shared components
For large engineering organizations with complex multi-repo dependencies, this is qualitatively different from Cursor’s single-repo local indexing.
Enterprise Security
Sourcegraph Enterprise is designed for organizations with strict security requirements:
- Self-hosted deployment
- Air-gapped environment support
- SSO/SAML
- Audit logs
- Custom model deployment (bring your own LLM)
For financial services, government, and other regulated industries, this matters.
IDE Flexibility
Cody works in VS Code, JetBrains IDEs, and Neovim. If your team can’t switch to Cursor (because they use IntelliJ, for example), Cody provides AI assistance across your existing editor investment.
Limitations
User experience is behind Cursor. Cursor’s inline autocomplete (Tab) is more sophisticated. The overall experience of coding with Cursor feels more seamless.
Agent mode is less capable. Cursor Composer for autonomous multi-file editing is more mature than Cody’s equivalent.
Requires Sourcegraph. Full cross-repo context requires deploying Sourcegraph Enterprise. This is a significant infrastructure investment.
Cursor
Cursor’s strengths are well-established: best-in-class Tab autocomplete, Composer for multi-file editing, and @codebase indexing for asking questions about your codebase.
For single-repo work, Cursor’s context handling is excellent. The experience of pair-programming with Cursor has no equivalent.
Where Cursor Falls Short vs. Cody
Cross-repository context. Cursor indexes one project at a time. If you need context from a shared internal library in a different repo, you have to open that repo separately.
Enterprise compliance. Cursor Business has privacy mode, but the full enterprise compliance infrastructure (self-hosting, custom model deployment, air-gap) isn’t as developed as Cody/Sourcegraph.
JetBrains support. Cursor is VS Code only. Cody supports JetBrains natively.
Use Case Decision
| Use Case | Winner |
|---|---|
| Individual developer, single repo | Cursor |
| Large org with multiple repos | Cody |
| JetBrains users | Cody |
| Autonomous multi-file edits | Cursor |
| Enterprise security requirements | Cody |
| Best autocomplete | Cursor |
| Deep codebase Q&A, cross-repo | Cody |
Pricing
Cody Free: Available with Sourcegraph.com account
Cody Pro: $9/mo — enhanced AI, more context
Cody Enterprise: Custom (requires Sourcegraph Enterprise)
Cursor Pro: $20/mo
Cursor Business: $40/user/mo
For individual use, Cody is cheaper. For enterprise deployment, Sourcegraph Enterprise adds significant cost but provides enterprise infrastructure Cursor doesn’t match.
Verdict
For most individual developers: Cursor. Better experience, better autocomplete, better agent mode.
For enterprise engineering teams with large multi-repo codebases, JetBrains users, or air-gapped environments: Cody with Sourcegraph Enterprise. The cross-repo codebase intelligence and enterprise security posture justify the infrastructure investment.