Cody by Sourcegraph vs Continue AI Coding Assistant
Detailed side-by-side comparison to help you choose the right tool
Cody by Sourcegraph
🔴DeveloperAI Development Assistants
Cody is Sourcegraph's AI coding assistant that uses the Sourcegraph code graph for repo-wide context, ships across VS Code/JetBrains/CLI, and is priced from $0 Free to $16/user/month Pro and Enterprise custom.
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FreeContinue AI Coding Assistant
🔴DeveloperAI Coding Assistant
Open-source AI code assistant for VS Code and JetBrains — bring your own models, curate a shared block library, ship as an internal platform.
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CustomFeature Comparison
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Cody by Sourcegraph - Pros & Cons
Pros
- ✓Deep codebase context via Sourcegraph's Code Search API, pulling relevant symbols and usage patterns across entire codebases for more accurate suggestions
- ✓Multi-LLM support lets users choose between Claude, GPT-4o, Gemini and other models, and enterprise customers can bring their own keys
- ✓Wide IDE coverage including VS Code, JetBrains, Visual Studio (experimental), a web interface in the Sourcegraph platform, and CLI access
- ✓Strong fit for large monorepos and polyrepo enterprise environments where cross-repository context is critical for accurate AI assistance
- ✓Customizable prompts and commands let teams encode standardized workflows (test generation, code review checklists, documentation) as reusable templates
- ✓Enterprise-grade governance with SSO, audit logs, repo permission-aware context, and guardrails for compliance-sensitive industries
Cons
- ✗Full enterprise context features require deploying and configuring Sourcegraph's code intelligence platform, which adds operational overhead
- ✗Free tier usage limits are more restrictive than some competitors like GitHub Copilot's free offering
- ✗Maximum value requires proper codebase indexing setup — context quality scales with indexing completeness
- ✗Smaller extension marketplace compared to GitHub Copilot's broader third-party integration ecosystem
- ✗Amp (the agentic evolution) is a separate product requiring additional onboarding and different workflows from the core Cody experience
- ✗Enterprise deployment complexity can be significant for smaller teams without dedicated DevOps resources
- ✗Learning curve to leverage advanced features like custom prompts, context filters, and @-mentions effectively
Continue AI Coding Assistant - Pros & Cons
Pros
- ✓Apache 2.0 core allows inspection and customization
- ✓Works in both VS Code and JetBrains with bring-your-own models
- ✓Continue Hub can distribute shared rules, prompts, documentation, and MCP blocks
Cons
- ✗A customizable setup takes more work than a fixed bundled assistant
- ✗Teams remain responsible for model security, cost, and output quality
- ✗Paid team seat pricing is not stated in the staging evidence
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