Cody by Sourcegraph vs Devin
Detailed side-by-side comparison to help you choose the right tool
Cody by Sourcegraph
🔴DeveloperAI Development Assistants
AI coding assistant powered by Sourcegraph's code intelligence platform, providing full codebase context awareness across repositories for chat, code completion, and agentic coding workflows.
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Starting Price
FreeDevin
🟡Low CodeAI Development Assistants
AI software engineer that codes, fixes bugs, and ships features autonomously. Builds full applications end-to-end with minimal supervision.
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Starting Price
$500/moFeature 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
Devin - Pros & Cons
Pros
- ✓Operates fully autonomously in a sandboxed VM with shell, browser, and editor access — handles end-to-end tasks that pair-programming tools cannot
- ✓Integrates directly into existing engineering workflows via Slack, GitHub, Linear, and Jira, so tickets can be assigned to Devin like a human teammate
- ✓Sessions are observable and interruptible — you can watch its plan, give mid-run feedback, edit files, or rewind to a checkpoint
- ✓Strong fit for parallelizable backlog work: small bug fixes, test writing, dependency upgrades, and codebase migrations across many files
- ✓Enterprise-ready with SOC 2 compliance, VPC/self-hosted deployment options, and a Devin API for programmatic dispatch from CI or internal tools
- ✓Maintains a custom knowledge base of repo conventions, runbooks, and prior decisions so it improves at your codebase over time
Cons
- ✗Significantly more expensive than IDE-based copilots, with usage-based ACU pricing that can escalate quickly on long or complex tasks
- ✗Quality drops sharply on ambiguous, novel, or architecturally complex work — best results require well-scoped tickets and good documentation
- ✗Async cloud-VM model means iteration latency is much slower than an inline assistant like Cursor or Copilot for quick edits
- ✗Requires real human review on every PR — unsupervised merging is risky, so it adds review load even as it removes implementation load
- ✗Onboarding to a new codebase takes time and tuning of the knowledge base before output quality becomes consistently useful
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