Cursor vs Sourcegraph Cody: embeddings and monorepo scale

Once your codebase hits that million-line mark, the real problem isn't writing new features - it's remembering where anything lives. Teams often spend three days hunting down a function definition that should have taken thirty seconds to find.

This comparison examines Cursor, an AI-native VS Code fork, against Sourcegraph Cody, a cross-IDE assistant built on repository-scale embeddings. The architectural differences matter more than you'd think.

Cursor streams at most 8,000 lines into its GPT-4-style model, relying on quick searches for everything else. Cody pre-indexes your entire repository, stores vector embeddings for every file, and feeds roughly 100,000 lines of related code into each response. That's the difference between asking about your current file versus asking about your entire system.

These design choices cascade through everything: how well each tool spots distant dependencies, how smooth refactors feel, and whether you can stay in your current IDE setup. The sections ahead break down context handling, embeddings, IDE support, deployment models, and pricing so you can pick the tool that keeps you shipping code instead of searching for it.

Quick Snapshot

Testing both tools on production codebases reveals that Cursor feels instant when working locally, while Sourcegraph Cody shows its strength once your repository hits enterprise scale. Here's what the data shows:

Cursor vs Sourcegraph Cody: Overview

The reality:

• Cursor wins on immediacy: install, open a file, start refactoring within seconds
• Cody scales: 100k-line context and repo-wide embeddings handle million-line codebases without breaking
• Pick based on your setup: VS Code shop? Cursor feels native. Multi-IDE enterprise needing compliance? Cody's self-hosted option works

Model & Context Capabilities

The most fundamental difference between these tools lies in how they understand your code. Cursor can feed roughly 8,000 lines of code to its LLM per request. Cody can handle 100,000 lines. That 12x difference in context window size fundamentally changes what questions you can ask and what refactors become possible.

Cursor: Local-First Context Handling

Cursor treats context like a just-in-time compiler - it grabs the files you're editing, follows import statements, and streams those code slices directly to your chosen model. No pre-processing, no waiting for indexing to complete. You install the IDE, open a repository, and start getting autocompletes from GPT-4 within minutes.

The 8,000-line window works well for feature branches and localized changes. Autocompletes land instantly because there's no round-trip to a remote index. Multi-file refactors feel responsive when you're working within a bounded context.

The limitation hits when debugging spans multiple services. That authentication bug? If its root cause lives in a service outside your current working set, Cursor simply can't see it unless you manually open those files. You're essentially working with whatever fits in your editor's tab bar according to dev.to/sushrutkm.

Cody: Remote Embeddings & Extended Context

Cody indexes connected repositories and, when prompted, uses semantic search to pull the most relevant code fragments from across the codebase, feeding up to 100,000 lines to the LLM. When you prompt the assistant, it runs semantic search across the entire codebase, pulls the most relevant code fragments, and feeds up to 100,000 lines to the LLM.

This RAG (Retrieval-Augmented Generation) approach means you can ask architectural questions like "Where do we validate JWTs?" and get answers that span the entire monorepo. Cross-service refactors that touch hundreds of files become single-command operations. The assistant has genuine global awareness of your codebase's structure and conventions.

The trade-off is setup time. Initial indexing can take 20+ minutes on Google-scale codebases, and you'll likely need Sourcegraph's self-hosted infrastructure for enterprise deployments. But once the index is built, the assistant feels omniscient about your code according to Intuition Labs.

This fundamental difference cascades into everything else: Cursor dominates tight iteration loops where you're fixing bugs in code you just wrote, while Cody becomes essential when you're debugging problems that span multiple services or need to understand legacy systems built by teams that left years ago.

IDE & Workflow Integration

The tooling philosophy behind each assistant shapes how they fit into your daily workflow. Cursor forked VS Code and rebuilt it around AI. That single-IDE focus means the model integrates into every hotkey and panel. Hit Cmd + K for a refactor request - the agent edits files, runs tests in the embedded terminal, and updates a workflow_state.md file to continue the loop without leaving the editor. The team calls this pattern an "autonomous loop" that reads state, acts, then repeats inside the same UI. Context switches drop to near zero, and features like model switching or quota stats appear directly in the sidebar.

The downside: if your team isn't standardized on VS Code, adopting Cursor means migrating your entire workflow, extensions, and keybindings. For pure VS Code shops, migration cost is minimal. For teams using IntelliJ, Vim, or other editors, it's a non-starter. Early adopters describe the experience as "lightweight, fast, and very responsive" but acknowledge the single-IDE limitation.

Cody takes the opposite approach. It ships as extensions for VS Code, JetBrains IDEs, and Neovim, adding AI features to whatever editor you already use. Installation works like any plugin: install, authenticate, keep your existing keybindings. Since Cody runs the heavy processing in Sourcegraph's background service, every supported editor gets the same repository-scale search and 100k-line context window. The trade-off: Cody can't control your entire UI like Cursor does, limiting workflow automation to what each host IDE exposes.

Onboarding reflects these design choices. Cursor is a 200MB download - boot it up and you're refactoring code in minutes. Perfect for solo developers or small teams that want to try GPT-4 without permission meetings. Cody requires a Sourcegraph account and works best after indexing your repository. That setup overhead pays off when you need consistent AI assistance across JetBrains one day and Neovim the next.

Embeddings & Code Navigation

The different approaches to understanding your codebase become most apparent when you're trying to track down elusive dependencies or understand architectural patterns. Cursor processes context on-demand. When you highlight code or ask a question, it pulls relevant files into an 8,000-line context window and lets the LLM reason from there. While it dynamically loads relevant files, it also uses vector indexes and preprocessing (like embeddings) for project-wide code understanding and semantic search. This keeps the editor responsive but can limit global repository awareness depending on project size. In a 50,000-line service, Cursor feels instantaneous. Point it at a million-line monorepo and you'll hit blind spots unless you manually open the files you need.

Cody pre-indexes the entire repository using vector embeddings. Every function, class, and file gets embedded and stored in a searchable index. When you ask a question, the Retrieval-Augmented Generation pipeline retrieves the top-K vectors from up to 100,000 lines of context and feeds them to the LLM. This enables cross-package queries without knowing file paths, but requires initial indexing time that scales with repository size.

The difference becomes clear when tracking down dependencies. Say you need to find every usage of parseUserToken. With Cursor, you'd open the defining file, ask chat to "find every call site," then manually verify each import chain. The process works but requires you to guide it through the codebase structure. With Cody, you type "Where is parseUserToken used?" and the semantic index returns every reference immediately - including the one buried in a legacy CLI tool three directories away.

Embeddings & Code Navigation

For repositories under 100,000 lines, Cursor's immediacy often outweighs the navigation limitations. Once you cross into multi-million-line territory - where a single utility function might be referenced across five frameworks and two build systems - Cody's full-repository embeddings provide the global code awareness that Cursor's on-demand approach can't match.

Deployment, Privacy & Security

Deployment architecture differences between Cursor and Sourcegraph Cody become critical when security teams have non-negotiable requirements: code never leaves the network perimeter.

Cursor ships as a desktop application - a VS Code fork that installs locally and feels reassuringly offline during day-to-day coding. Your files stay on your machine, edits run at laptop speed, and there's no server infrastructure to maintain. But every completion request and chat interaction forwards code snippets to external LLM endpoints. Your proprietary code travels across the wire to Anthropic, OpenAI, or whichever model you've selected. Cursor doesn't publish enterprise certifications or offer air-gapped deployments, which can kill deals in financial services.

Cody takes the opposite approach. It installs as an extension inside VS Code, JetBrains, or Neovim, but the intelligence can run entirely behind your firewall. Sourcegraph offers self-hosted deployments where embeddings, search indexes, and LLM traffic stay on infrastructure you control. Enterprises can route Cody to private Claude or OpenAI endpoints, satisfying data-residency requirements while leveraging indexing and search to handle large monorepos.

Here's how the deployment models compare:

Deployment, Privacy & Security

For individual developers or startups shipping fast, Cursor's zero-ops install and snappy response time are hard to beat. But once security questionnaires start landing, Cody's self-host option, granular access controls, and audit logs save days of compliance paperwork. Regulated teams can keep every token behind the firewall, while solo developers can trade that belt-and-suspenders approach for Cursor's lighter footprint and speed.

Pricing & Value

Understanding the real cost of these tools requires looking beyond the sticker price. You'll also pay for tokens, infrastructure, and potentially developer hours to switch tools.

Cursor's public tiers range from a free Hobby plan with 50 "fast" GPT-4 calls per month to an Ultra tier at $200 per user that unlocks 5,000 priority calls and access to models like Claude Opus and Gemini 2.5 Pro. Mid-range plans sit at $20 (Pro) and $40 (Business) per user, each bundling 500 fast requests and unlimited slower queue-based calls according to Apidog's pricing guide. The company's 2025 reset on request quotas - after user backlash over "unlimited" language - makes real per-token cost transparent but means you must police your own usage as detailed by GetMonetizely.

Cody keeps dollar figures behind sales calls. Sourcegraph advertises a free individual tier and paid Team and Enterprise licenses, but you'll need to talk to sales for actual numbers. Enterprise agreements bundle self-hosting rights, SSO, RBAC, and priority support.

Pricing & Value

Total cost of ownership diverges at scale. Cody's full-repo embedding index means provisioning servers or managed Sourcegraph clusters - you'll pay for that infrastructure whether you host on-prem or in the cloud. Cursor is SaaS-only, but if your team lives in JetBrains or Vim, you'll pay in developer hours to migrate workflows.

Solo engineers get the most predictable deal from Cursor's Pro plan. Small teams weighing agility over governance often find Cursor's Business tier cheaper than standing up Sourcegraph. Enterprises needing air-gapped deployments and audit logs usually see Cody's bundle of self-hosting, RBAC, and large-scale indexing as the safer long-term bet - even if the final number comes on a custom quote.

Best-Fit Use Cases

After testing both assistants across different scenarios, the question becomes "which one matches how you actually ship code?" The answer shifts dramatically based on project size, IDE mix, and compliance requirements.

When Cursor fits like a glove: Solo developers already living in VS Code who want zero-friction setup get the most value here. Since Cursor is a VS Code fork, you install it, sign in, and get agentic editing within minutes - no infrastructure to maintain, no Sourcegraph server to configure. Small teams working with focused repos - services under ~100 kLoC - rarely hit the 8,000-line context bottleneck, and the speed advantage from local, on-demand lookup outweighs global code intelligence according to DevTools Academy's benchmarks.

Environments running offline or over unreliable VPN connections also benefit from Cursor's local-first architecture, which keeps most logic on your workstation and only hits the LLM when you explicitly request it. For rapid multi-file refactors during prototyping, you get GPT-4-powered edits across your current working set without waiting for repo-wide re-indexing.

Where Cody owns the field: Enterprises wrestling with monorepos stretching into millions of lines find Cody's repository-scale embeddings and 100,000-line context window essential for questions spanning the entire code graph, as demonstrated in Intuition Labs' deep dive. Teams split across JetBrains, VS Code, and terminal editors benefit from Cody's plugin strategy, which means everyone keeps their preferred tool while sharing one semantic search backend - no IDE culture wars.

Organizations governed by strict security or data-residency rules need Cody's self-hosted deployments with RBAC, audit logs, and air-gapped options. Codebases demanding cross-repo semantic search - think queries like "where do we serialize UserPayload across all services?" - get instant, reference-linked answers instead of grep-driven guesswork, as shown in Cody's demo.

Verdict & Recommendation

The choice between Cursor and Sourcegraph Cody ultimately hinges on three technical requirements: How often do you need answers that span the entire codebase? Does your team share one editor or many? What are the compliance guardrails around sending code to external LLMs?

If you're debugging across million-line codebases, Cody's architecture wins on pure scale. Its repo-wide embeddings and scalable semantic search handle cross-project queries beyond Cursor's 8,000-line context window. Add Sourcegraph's self-hosted deployment and granular RBAC, and you have an assistant that handles enterprise compliance and polyglot teams across JetBrains or Neovim.

Cursor optimizes for speed over scale. The VS Code fork loads in seconds, its agentic commands refactor multiple files in one shot, and everything runs locally unless you explicitly choose cloud models. If your workflow centers on VS Code and you prioritize offline privacy over monorepo queries, Cursor's 8,000-line context handles most single-service refactors.

Still evaluating? Run a one-week comparison: index your largest repo with Cody, then open the same project in Cursor for a typical refactor. Track prompt-to-shippable-code success rates across both tools. The performance difference becomes obvious within days, and you'll quickly discover whether your codebase benefits more from Cursor's immediacy or Cody's comprehensive understanding of your entire system.

Ready to Accelerate Your Development Workflow?

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