Sourcegraph Cody vs Tabnine
Both are built for Code — here's how Sourcegraph Cody and Tabnine actually differ on pricing, platform reach and where each one is strongest.
At a glance
| Attribute |  Sourcegraph Cody |  Tabnine |
|---|---|---|
| Pricing | Contact for pricing | From $39/mo |
| Platforms | Web, Self-hosted, macOS, Windows | macOS, Windows, Self-hosted |
| Categories | Code | Code |
| Integrations | Not listed | Not listed |
Where each stands
Sourcegraph Cody
Pros
- Context quality benefits directly from Sourcegraph's mature code search and indexing
- Strong option for very large, sprawling codebases where single-file context tools struggle
- Robust self-hosted and air-gapped deployment for regulated industries
Cons
- No longer available to individuals or small teams — free and Pro plans were discontinued in mid-2025
- Enterprise contracts start high, putting it out of reach for most teams under a few hundred seats
- Agentic, multi-file editing capability trails newer agent-first tools like Cursor or Copilot's agent mode
Tabnine
Pros
- Genuine self-hosted and air-gapped options, not just a marketing checkbox
- Broad IDE coverage from a single vendor and license
- Clear focus on IP protection and code privacy over flashy agentic features
Cons
- No free tier since 2025, and the entry price is higher than most per-seat competitors
- Agentic capabilities lag behind newer tools like Cursor or Copilot's agent mode
- Self-hosted deployment requires real infrastructure investment and Kubernetes-level setup
Which should you choose?
Sourcegraph Cody is the better fit for:
- Large organizations with monorepos where context across the whole codebase matters
- Regulated enterprises that need self-hosted or air-gapped AI coding tools
- Teams already using Sourcegraph code search who want AI chat layered on top
- Answering where is this used across hundreds of repositories
- Standardising common AI prompts across an engineering organisation
- Assessing the blast radius of a change before making it
Tabnine is the better fit for:
- Regulated or security-sensitive teams that can't send code to a third-party cloud
- Standardizing one AI coding assistant across many different IDEs
- Enterprises wanting contractual IP indemnification for AI-generated code
- Generating unit tests for existing code during a coverage push
- Tuning suggestions to a team's own patterns rather than public code
- Documenting a legacy service that nobody currently understands
Looking for more options? Browse every tool in the Code category.
Related comparisons
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