Cursor vs Sourcegraph Cody: Embeddings and Monorepo Scale Compared

AI code assistants live or die by the context they can retrieve. At 50,000 lines of code, nearly anything works. At 2 million lines spread across a polyglot monorepo with generated Protobuf types, GraphQL schemas, and a shared component library, the architecture underneath the assistant starts to matter more than the model powering it.

Cursor and Sourcegraph Cody represent two fundamentally different bets on how to feed a codebase to an LLM. This comparison breaks down where each approach actually holds, and where it cracks.

How Cursor builds context: workspace embeddings and agent loops

Cursor operates as an AI-native fork of VS Code. When you open a project, it generates vector embeddings of your workspace files using a cloud-hosted embedding model. Those embeddings power its "codebase" retrieval: when you ask a question or request an edit, Cursor runs a similarity search against those vectors to pull relevant snippets into the LLM's context window.

For a single-service repository or a moderately sized monorepo (under roughly 500K lines of code), this works well. The embeddings update as you save files, the retrieval is fast, and the tight IDE integration means you rarely leave the editor. Cursor's agent mode can also shell out to grep and file-search tools, giving it a fallback when the embedding index misses something.

The limitation is architectural. Cursor indexes what is open in your workspace. If your monorepo's API contracts live in a separate repository, or your shared types are published as a package from another repo, Cursor has no knowledge of them unless you physically add those directories to your workspace. At enterprise scale, where teams own dozens of interconnected services, this blind spot compounds. You end up manually stuffing context by opening extra folders, which slows the editor and dilutes the embedding index with files you do not actually need.

Reports from teams exceeding 1 million lines of code also note occasional context drift, where the embedding model produces vectors that are too similar for structurally identical but semantically distinct generated code (think multiple Prisma schemas or duplicated Protobuf message types). The retrieval pulls in the wrong User type, and the suggestion breaks a service boundary you cannot see from the embedding alone.

For a deeper look at how Cursor stacks up against other AI editors on general coding workflows, see our Cursor vs Windsurf comparison.

How Sourcegraph Cody builds context: code graph indexing across repositories

Sourcegraph Cody takes a different approach entirely. It sits on top of Sourcegraph's code intelligence platform, which maintains a persistent index of every repository connected to your Sourcegraph instance. That index is not just vector embeddings. It includes a code graph built from precise code navigation (SCIP-based indexing), meaning Cody can resolve "go to definition" and "find all references" across repository boundaries without cloning anything locally.

When you ask Cody a question, it can search across every indexed repository using a combination of keyword search, code graph traversal, and (where configured) vector embeddings. This is the critical difference for monorepo and multi-repo setups: Cody's context window is not limited to what you have open. It can pull in the contract definition from the API repo, the shared validation logic from the platform library, and the deployment config from the infra repo, all in one retrieval pass.

Sourcegraph's cross-repository code search is what makes this work. The search layer handles regex, structural search (matching syntax patterns, not just strings), and symbol-aware queries. Cody's agentic context gathering can invoke these tools automatically, choosing the right search mode for the question.

The tradeoff is operational overhead. Running a Sourcegraph instance (self-hosted or cloud) requires configuration: connecting code hosts, setting up indexing jobs, managing SCIP indexers for each language in your stack. For a 10-person startup with one repo, this is overkill. For an organization with 200 repositories and compliance requirements around code access, the infrastructure pays for itself in context quality.

Where embeddings alone break down at scale

The pure-embedding approach (Cursor's default) has a well-documented failure mode in large codebases. Embeddings compress code into fixed-dimensional vectors. Two structurally similar but semantically different functions can land close together in vector space, especially when the code is generated (ORMs, type definitions, API stubs). At monorepo scale, where these duplicates multiply, retrieval precision drops.

Sourcegraph Cody mitigates this by layering code graph resolution on top of embeddings. If the embedding retrieval surfaces the wrong UserService, the code graph can disambiguate by checking import chains and call sites. Cursor has no equivalent mechanism: its fallback is grepping, which helps with exact matches but cannot resolve type hierarchies or cross-file references the way a code graph can.

It is worth noting that some tools (Claude Code, Devin) skip persistent indexing entirely, relying on agentic loops that drive ripgrep and file reads in real time. This avoids stale-index problems but trades off latency and token cost. For background on that tradeoff, our Claude Code vs Cursor breakdown covers the agentic-vs-indexed spectrum in detail.

Enterprise considerations: governance, deployment, compliance

Cursor is cloud-only SaaS. Your code snippets leave your machine to reach Cursor's embedding service and the upstream LLM provider. The Business plan adds admin controls and team management, but there is no self-hosted option.

Cody, backed by Sourcegraph's enterprise platform, supports self-hosted deployment. Organizations that cannot send code to external services (finance, defense, healthcare) can run the full stack on their own infrastructure. Sourcegraph Enterprise also provides audit logging, role-based access controls, and integration with existing CI/CD pipelines.

If your security posture requires that code never leaves your network, Cursor is off the table regardless of its coding UX. Cody is one of the few AI assistants where you can keep the entire pipeline (indexing, embedding, inference) on-premises, assuming you pair it with a self-hosted LLM or a VPC-deployed model endpoint.

For teams evaluating code intelligence alongside code quality tooling, our Sourcegraph Cody vs Qodo comparison covers that adjacent decision.

The monorepo tipping point

The practical dividing line comes down to repository count and size. If your team works in one repository under roughly 500K lines, Cursor's workspace embeddings provide excellent context with zero infrastructure. You open the project, the index builds, and you start coding.

Once you cross into multi-repo architectures or monorepos above a million lines, Cursor's single-workspace model becomes a bottleneck. You spend time managing which folders are open, fighting context drift on generated code, and manually pasting cross-repo references into chat. Cody's code graph eliminates that friction by design, at the cost of running the Sourcegraph platform.