How I Connected Ollama to Cursor and What It Taught Me About the Future of AI Coding Tools

One weekend, I spun up Ollama on my laptop, exposed the chat completions endpoint with ngrok, and plugged it into Cursor.

It was slow. My poor CPU wheezed like it was running a marathon in flip-flops. But it worked.

And that small experiment changed how I think about the future of AI coding assistants — because it showed me something obvious we keep forgetting: You don’t need to rely on big players like OpenAI or GitHub to build your own intelligent pair programmer. You just need a model, a GPU, and a bit of curiosity.

💡 Why This Matters: Cost, Control, and Code Privacy

Most developers love tools like Copilot, Cursor, or ChatGPT. They save time, boost productivity, and make coding feel… easier. But here’s the tradeoff we rarely talk about:

• Cost: usage-based pricing adds up fast.
• Privacy: your code (sometimes proprietary) leaves your system.
• Control: you can’t customize the AI’s behavior, data, or deployment.

We’ve quietly accepted that AI tools have to live in the cloud — tied to someone else’s API, someone else’s pricing, someone else’s decisions.

But what if that wasn’t true? What if you could run your own private AI backend — one that knows your codebase and your preferences — without leaking a single line to the internet?

That’s exactly what open-source models like Ollama, Mistral, and Qwen make possible.

🧠 The Big Idea: Running Your Own AI Coding Assistant

Think of it like self-hosting GitHub back in the day. It’s more setup at first — but you gain full control.

Open-source LLMs have matured fast. Today, you can run models locally that are good enough for code suggestions, documentation, and even architecture conversations. And if you’re in an organization with GPUs, you’re sitting on the infrastructure to make this real.

Here’s what makes this approach powerful:

• Creative control: fine-tune or prompt-tune for your codebase.
• Privacy by design: data never leaves your network.
• Cost control: no surprise bills.
• Customization: you choose the model and context strategy.

That’s why this idea excites me more than any new Copilot feature drop — it’s open, flexible, and puts developers back in charge.

🧩 How to Connect Ollama to Cursor (or VS Code)

Here’s the setup I tested — and how you can try it too.

⚠️ Note: My laptop didn’t have a high-end GPU, so performance was limited. But the concept works beautifully — and scales easily to real hardware.

Step 1: Install Ollama

Ollama is the simplest way to run open-source models locally. On macOS, installation takes one line:

brew install ollama

Then pull a model — for example, CodeLlama or Mistral:

ollama pull codellama

Once pulled, you can chat with it using:

ollama run codellama

Step 2: Expose Ollama’s API Endpoint

By default, Ollama runs on localhost:11434. You can access it at http://localhost:11434/api/chat.

To connect it to Cursor (which expects an external endpoint), use ngrok to tunnel your local server:

ngrok http 11434

Ngrok will generate a temporary public URL, something like:

https://your-random-id.ngrok.io

That’s your public API endpoint — your “mini OpenAI” for the day.

Step 3: Reconfigure Cursor or VS Code

Open Cursor’s backend configuration (or equivalent .env if you’re testing via API). Point it to your ngrok URL:

{
  "apiBaseUrl": "https://your-random-id.ngrok.io/api/chat"
}

Restart Cursor, and try a chat or completion.

The first time I saw Cursor respond from my own model, it felt weirdly empowering — like running my own little AI lab.

Step 4: Test, Tweak, and Imagine

Now, you’ll likely notice slow responses if you’re running on CPU. That’s fine — it’s proof-of-concept territory.

But here’s the mind shift: If you can run this on your personal machine, imagine what your company could do with GPU nodes.

• Host a code-specific LLM internally.
• Integrate it with private repositories.
• Audit every token and output.

That’s the kind of AI sovereignty every dev team should at least explore.

Step 5: Enterprise-Level Setup (Optional)

If you want to take this beyond local testing:

• Deploy Ollama or another inference server on GPU hardware (AWS, RunPod, Lambda Labs, etc.)
• Secure the endpoint with authentication and firewall rules.
• Optionally, fine-tune on your organization’s codebase.
• Integrate with tools like LangChain or LlamaIndex for memory and context.

At that point, you’re not just using an AI assistant — you’ve built one.

🔍 What I Learned from the Experiment

My first run was clunky. It took ages for Ollama to respond. But that didn’t matter. What mattered was the realization:

AI development doesn’t have to be centralized.

We’ve spent years assuming tools like Cursor, Copilot, or ChatGPT have to be black boxes. But this experiment flipped that assumption.

Once you run a model yourself — even for a weekend — you stop thinking of AI as a magic API and start seeing it as software you can control, shape, and improve.

That’s the mental shift developers need right now.

🏗️ Why This Matters for Developers and Enterprises

For developers:

• Experimenting with local models helps you learn faster.
• You understand inference, context limits, and latency in real terms.
• You gain confidence tinkering under the hood, not just prompting in the UI.

For enterprises:

• Your proprietary code never leaves your network.
• You can train domain-specific assistants that actually understand your stack.
• You reduce dependency on external APIs — and potentially save a ton.

The big picture? The next phase of AI coding tools won’t be about which model you use — it’ll be about where you run it.

⚙️ How to Start Experimenting This Week

If this excites you, here’s your starter plan:

1. Install Ollama and run a small model locally.
2. Use ngrok to expose the /api/chat endpoint.
3. Repoint Cursor or your editor’s LLM config to your ngrok URL.
4. Try a few completions. See how it feels.
5. Optional: Move it to a cloud GPU and scale from there.

🧰 Useful links:

• Ollama Docs – run open models locally
• Ngrok – create secure tunnels
• Cursor – AI-powered code editor

🔚 Wrapping Up

Here’s what we covered:

• How to connect Ollama to Cursor or VS Code using ngrok.
• Why open-source LLMs matter for privacy, cost, and creativity.
• How this small experiment points toward a decentralized AI future.

For you, this means you don’t need to wait for another paid feature or API key. You can build your own AI coding assistant — today — and actually own it.

So here’s my question to you: Would you trust your company’s AI pair programmer if it ran on your own GPU instead of someone else’s?

Because that’s not a futuristic dream anymore — it’s a weekend project away.