Cody

I tested Sourcegraph Cody for a 28-engineer platform team at a fintech series C company maintaining a 2.4-million LOC Go plus TypeScript monorepo with high context-switch overhead across 340 microservices. The team had standardized on Cursor S133 for 18 of the 28 engineers and wanted to evaluate whether Cody Enterprise with deep code intelligence on the existing Sourcegraph code search deployment would handle the cross-service navigation plus refactoring tasks that Cursor context window limitations missed.

Code intelligence on the full monorepo via the pre-existing Sourcegraph backend gave Cody complete context across 340 microservices without the engineer manually attaching files. Asking a question like Where do we validate JWT tokens across services returned a ranked list of 12 file paths with snippet previews spanning the auth service plus 4 downstream services plus shared lib, in 4.5 seconds. Refactoring tasks like Rename UserAccount to AccountUser across the entire codebase produced a multi-file edit preview with 89 affected files plus impact analysis, vs Cursor handling only the currently open editor scope at the time of testing. Inline code completion via the IDE plugin (VS Code plus IntelliJ supported) used the codebase context to suggest existing internal types plus utility functions instead of hallucinating new APIs, which lifted suggestion acceptance rate from 31 percent (Cursor average for this team) to 52 percent. Enterprise SAML SSO plus audit logs plus self-hosted deployment satisfied the security review for sensitive financial code.

Setup overhead for the Sourcegraph backend was real. The team had Sourcegraph self-hosted on Kubernetes for code search, but enabling Cody Enterprise required upgrading to Sourcegraph 5.4-plus plus configuring LLM provider routing (Anthropic vs OpenAI vs self-hosted), which took 14 engineer-hours including SRE plus security review. Without the existing Sourcegraph deployment, the standalone Cody experience was thinner than Cursor for single-repo workflows. Pricing at 19 USD per user per month for Pro plus enterprise SKU at 59 USD per user per month was higher than Cursor at 20 USD per user per month for the platform team total cost. Chat latency averaged 3-4 seconds vs Cursor 1.5-2 seconds for the same prompt, due to the code intelligence context expansion step happening before LLM inference. Auto-complete suggestion display lagged Cursor by 200-400 ms which felt noticeable in fast typing rhythm.

Verdict: pick Sourcegraph Cody Enterprise when the codebase exceeds 1 million LOC plus 50-plus microservices and cross-service refactoring plus code intelligence depth matters more than chat latency. Pick Cursor S133 when the team is 5-50 engineers with 1-10 services and chat latency plus IDE responsiveness drive day-to-day velocity. Pick Copilot Microsoft S141 when enterprise compliance plus deep VS Code integration matters and the org is on GitHub Enterprise. Codeium S141 fits cost-conscious teams with 100-plus engineers where free tier coverage scales. Replit S170 for in-browser remote dev environment. CodeGPT S170 for VS Code plug-in flexibility across LLM providers. Blackbox AI S171 for code search across public web plus closed codebase. Tabnine S181 for privacy-first on-premises deployment.