Cursor, Continue & JetBrains: Connect Your Whole Team to a Private LLM

Meet developers where they already are

The best AI platform in the world is useless if developers have to leave their workflow to use it. The win condition is simple: AI shows up inside the editor your engineers already use, pointed at a model you control.

The good news is that the major IDEs and AI extensions now support custom, self-hosted endpoints. If your inference server exposes an OpenAI-compatible API — which vLLM, Ollama, and TGI all do — connecting the whole team is mostly configuration.

Here’s how the pieces fit together.

VS Code with Continue

Continue is the most popular open-source AI extension for VS Code, and it’s built for exactly this. You point it at your server’s base URL and model name, and developers get inline completion and a chat panel — all served from your infrastructure.

Because Continue is configuration-driven, you can ship a standard config to the whole team so everyone gets the same models, the same retrieval, and the same behavior out of the box.

JetBrains IDEs

For teams on IntelliJ, PyCharm, GoLand, and the rest of the JetBrains family, Continue also ships a JetBrains plugin, and several other plugins support custom endpoints. The same private server backs them — your Java, Kotlin, Python, and Go developers all draw from one local brain.

Cursor

Cursor is a favorite for AI-first development. Teams that want to keep inference in-house can route Cursor’s model calls through a self-hosted, OpenAI-compatible endpoint, so the editor’s powerful UX runs against your own model rather than only external providers. The exact setup depends on your security posture, and it’s one of the things we configure as part of a rollout.

One server, every editor

The architecture that makes this clean:

        ┌─────────────┐   ┌─────────────┐   ┌──────────────┐
        │   Cursor    │   │  VS Code +  │   │  JetBrains + │
        │             │   │  Continue   │   │   plugin     │
        └──────┬──────┘   └──────┬──────┘   └──────┬───────┘
               │                 │                 │
               └─────────────────┼─────────────────┘
                                 ▼
                  OpenAI-compatible API (your network)
                                 │
                    ┌────────────┴────────────┐
                    │  Inference server +      │
                    │  private codebase RAG    │
                    └──────────────────────────┘

Every editor speaks the same protocol to the same server. Add a better model later, and every developer gets it at once. Improve retrieval, and every editor benefits. No per-developer setup drift.

Rollout is a people problem, not just a config problem

The technical connection is the easy 20%. Getting an entire engineering org to actually adopt the tool is the other 80%:

• Ship a standard config so no one has to fiddle with settings.
• Pick sensible defaults — the right model for the right task, retrieval on by default.
• Onboard in small groups with real examples from your own codebase.
• Measure adoption — completion acceptance rate, daily active users, time saved — so you can see what’s landing and fix what isn’t.

A rollout that ignores the human side ends with a great platform that nobody uses. One that takes adoption seriously turns AI into a team-wide habit.

The bottom line

Your developers don’t want another tab or another tool. They want AI in the editor they already love — and your business wants that AI to run somewhere safe. Connecting Cursor, VS Code, and JetBrains to a private model gives you both.

If you want every developer connected to one private, codebase-aware platform, book a discovery call.