Moving from GitHub and GitLab to Sourcegraph AI code intelligence

Sourcegraph indexes repositories to provide universal code search and intelligence across multiple code hosts. I see teams leaving GitHub Code Search because they require the retrieval-first architecture Cody uses to populate context windows using embeddings and search-based retrieval. This architecture helps developers navigate large codebases by pulling context from files and symbols not open in the editor. Unlike GitLab Duo, which consumes 66% of its token limit on enterprise tooling like build configurations and CI definitions in 450,000-file environments, Cody uses semantic relevance to identify dependencies across the codebase. For teams managing complex migrations, Sourcegraph’s AI code intelligence drives automated migrations for organizations including Reddit and FactSet, which improves migration speed and reduces bugs by 60%. Teams must prepare for the reality that automated context selection occasionally fails in certain repositories.

Teams getting started can choose between cloud-hosted or self-hosted deployments. The Enterprise Starter plan supports growing teams of up to 50 developers and includes access to code search, multi-repo context, and a prompt library. This plan supports up to 100 GitHub repositories and provides 5GB of storage, with an additional 1GB included per seat up to a 10GB maximum.

Cody works inside VS Code and JetBrains. If you use JetBrains, the Cody plugin achieved general availability in June 2024. You can use the @ symbol to add specific files or symbols to your chat. Cody includes features like auto-edit, which suggests code changes based on cursor movements, and debug capabilities that identify errors in your code. You can also use context filters to ignore selected repositories from chat and autocomplete results. For larger organizations, annual contracts for enterprise deployments often exceed $150,000 and can reach $500,000 depending on the specific feature requirements and the negotiation approach used by the enterprise procurement team during the final contract stage. For those opting for self-hosted models, you must budget for compute and storage costs which can add $10,000 to $50,000 annually to the total cost of ownership. Additionally, Sourcegraph manages its development using a Fair Source license, which requires companies with more than 15 employees to pay for the software.

I find Cody’s multi-repository capability superior to GitLab Duo when refactoring services that span microservices. When I tested tracing data validation patterns across three services, Cody retrieved relevant implementations from repositories I did not explicitly name because the code graph identified the structural relationships. GitLab Duo struggles with this, as its conversation history only sends the last 25 messages to the LLM. In 450,000-file environments, GitLab Duo uses 66% of available context for build configurations and CI definitions before it can even begin code analysis.

Does the infrastructure overhead of maintaining embeddings offset the gains in retrieval accuracy?

Qualtrics reported a 28% reduction in leaving the IDE for web searches after deploying Cody to 1,000 developers. I recommend Cody for teams that prioritize cross-repository intelligence over unified pipeline automation. While GitHub Copilot remains a strong choice for teams staying within the GitHub ecosystem, Cody provides better context for developers who need to understand how code behaves across different services and repositories. Compared to alternatives like Bito’s AI Architect, which builds a live knowledge graph from Jira tickets and observability data, Cody focuses on the repository layer. While Bito provides a Team plan at $12 per seat per month, Cody’s pricing scales with the depth of code intelligence you require. I would skip Bito if your primary goal is project management integration, but I value Cody’s ability to perform semantic search across language boundaries. I find that Cody’s automated context selection can be unreliable in certain repositories, so you must manually specify files when automation fails.