Private AI Coding Tools in 2026: Cursor, Continue, and Local Alternatives

AI coding tools went from novelty to default in 36 months. Cursor, Copilot, Anthropic's Claude, Windsurf, and Codeium are now installed on more developer laptops than linters are. The productivity gains are real. The privacy costs are also real and mostly undiscussed.

Every time you accept a cloud-based AI autocomplete, some fraction of your codebase — and sometimes all of it, depending on the tool and its "indexing" mode — crosses the network to a vendor. If you are writing hobby projects in public repos, who cares. If you are writing code that touches customer data, internal infrastructure, regulated industries, or anything subject to an NDA, it matters a lot.

This is the 2026 landscape of AI coding tools, ranked by what they actually do with your code, and a working local-only setup that matches cloud tool productivity for most real workloads.

What "private" actually means for coding tools

Privacy in AI coding tools is a spectrum, not a binary. The axes:

Where does inference happen? Local machine, vendor cloud, or third-party inference provider (OpenAI, Anthropic, Google) called by the vendor.
What context is sent? Just the current line, the surrounding function, the whole file, or the whole repository index?
Is code used for training? Vendor-dependent, sometimes configurable, sometimes not.
Is code retained? Logged temporarily, retained indefinitely, or never stored?
What jurisdiction applies? US, EU, or "wherever the model provider's servers happen to be today."

A tool can be "private" on axis 3 and wildly non-private on axis 1. Most vendor marketing conflates these deliberately.

The 2026 tool landscape

Cursor

Cursor is the market leader among AI-first editors. It forked VS Code, wired in multi-model routing (Claude, GPT, Gemini), and added whole-repo indexing.

Where inference happens: Vendor cloud, which proxies to Anthropic/OpenAI/Google.
Context sent: By default, files you open and "Codebase" queries index your entire repo via embeddings sent to the vendor.
Training: Cursor has a "Privacy Mode" that disables training and is enterprise-default. Off by default for free users until recently.
Retention: Prompts and completions logged by the upstream model vendor per their terms.
Verdict: The most capable AI editor in 2026 and the least private of the popular options. Fine for open-source work, disqualified for anything under an NDA without Privacy Mode + an enterprise contract.

GitHub Copilot (and Anthropic's Claude)

Copilot was the first and is still the most widely deployed. Anthropic's Claude is an agentic coding assistant that competes with it directly.

Where inference happens: Microsoft/OpenAI cloud (Copilot) or Anthropic cloud.
Context sent: File-level for autocomplete, broader for chat/agent modes.
Training: Business and Enterprise tiers contractually exclude training. Free and individual tiers historically did not.
Retention: Microsoft retains prompts for 28 days on Business. Anthropic retains for 30 days by default.
Verdict: Acceptable with enterprise contracts, not acceptable for genuinely sensitive code.

Codeium / Windsurf

Codeium (now Windsurf) positioned itself as the "enterprise-friendly" option. They offer self-hosted enterprise deployments.

Where inference happens: Vendor cloud by default, self-hosted option for enterprise.
Context sent: File and workspace level.
Training: Contractually excluded on enterprise.
Retention: Configurable.
Verdict: The best cloud option for compliance-sensitive teams. Still cloud.

Cody (Sourcegraph)

Cody integrates with Sourcegraph's code intelligence platform. Strong on large codebases because it leverages Sourcegraph indexing.

Where inference happens: Sourcegraph cloud or self-hosted Sourcegraph Enterprise.
Context sent: Indexed by Sourcegraph. Self-hosted keeps indexing on-prem.
Training: Excluded.
Retention: Configurable.
Verdict: Excellent for enterprises already running Sourcegraph. Overkill for solo developers.

Continue.dev

Continue is the open-source, bring-your-own-model coding assistant. It is the tool that makes local-first AI coding actually practical.

Where inference happens: Anywhere you point it. Ollama on localhost, vLLM on your own server, OpenAI, Anthropic, anywhere.
Context sent: Whatever you configure. Everything stays local if your model is local.
Training: N/A when local.
Retention: N/A when local.
Verdict: The clear winner for privacy. Slightly rougher UX than Cursor, but not by much in 2026.

Aider

Aider is a terminal-based AI pair programmer that works over git. It is minimalist, scriptable, and pairs beautifully with local models.

Where inference happens: Any OpenAI-compatible endpoint, including Ollama.
Context sent: Only files you explicitly add to the chat.
Training / retention: Depends on model provider. Zero if local.
Verdict: The power-user local choice. Works from the terminal, commits with proper messages, and gets out of your way.

Side by side

| ------------- | --------------- | ----------- | ------------- | ----------------- | ---------------------- |

| Cursor | No | No | Cloud | No | Excellent |

| Continue.dev | Yes | Yes | Local | Yes | Very good |

| Aider | Yes | Yes | Git-aware | Yes | Good |

The local-only setup that actually works

Here is the stack I run for all sensitive coding work. It gets about 85-90% of Cursor's productivity, costs nothing per token, and sends nothing across the network.

1. Ollama as the inference backend

Install Ollama (see the laptop setup guide) and pull two models:

```bash

ollama pull qwen2.5-coder:32b # for chat/edits/reasoning

ollama pull qwen2.5-coder:1.5b # for autocomplete

```

The small model handles FIM (fill-in-the-middle) autocomplete where latency matters. The big model handles chat, edits, multi-file changes, and reasoning where quality matters.

On a 64GB Mac Mini M4 Pro, both can stay resident at the same time.

2. Continue.dev as the VS Code integration

Install the Continue extension from the VS Code marketplace. Open ~/.continue/config.json and configure it to point at Ollama:

```json

{

"models": [

{

"title": "Qwen 2.5 Coder 32B",

"provider": "ollama",

"model": "qwen2.5-coder:32b",

"apiBase": "http://localhost:11434"

}

"tabAutocompleteModel": {

"title": "Qwen 2.5 Coder 1.5B",

"provider": "ollama",

"model": "qwen2.5-coder:1.5b",

"apiBase": "http://localhost:11434"

"embeddingsProvider": {

"provider": "ollama",

"model": "nomic-embed-text"

"contextProviders": [

{ "name": "code", "params": {} },

{ "name": "diff", "params": {} },

{ "name": "folder", "params": {} },

{ "name": "codebase", "params": {} }

]

}

```

This gives you:

Chat with your codebase, fully local.
Tab autocomplete, fully local.
Embeddings-based code search, fully local (via nomic-embed-text).
Diff context for code review prompts.

Restart VS Code. Open a project. Everything works like Cursor, minus the cloud round-trip.

3. Aider for terminal workflows

For agentic tasks — "add a test for this function, run it, fix whatever fails" — Aider beats IDE integrations. Install it and point it at Ollama:

```bash

pip install aider-chat

aider --model ollama/qwen2.5-coder:32b \

--editor-model ollama/qwen2.5-coder:32b

```

Add files with /add, ask for changes, and Aider writes them to disk and commits through git with a sensible message. All local.

The best local models for coding in 2026

Coding models are where the open-weight gap to frontier is narrowest. Pick one:

| ---------------------- | ------ | ------ | ---------------------------------------- | --------------------------------- |

For most people: Qwen 2.5 Coder 32B. It runs on anything with 32GB+ of RAM, handles fill-in-the-middle autocomplete natively, and matches the quality of cloud mid-tier models for everyday coding. For autocomplete specifically, pair it with Qwen 2.5 Coder 1.5B.

For workstations: DeepSeek-V3. It is the first open-weight model that genuinely competes with frontier closed coders on held-out benchmarks. Requires serious hardware — Mac Studio M3 Ultra or similar — but it is worth it if your daily work justifies the investment.

Performance: local vs cloud for coding

Against Cursor (which typically routes to Claude Sonnet or GPT-class models), here is roughly what I see with the local setup above on real codebases:

| Task | Local (Qwen Coder 32B) | Cloud (Cursor default) |

| ----------------------------- | ---------------------- | ---------------------- |

| Single-function completion | ~95% as good | Reference |

| File-level refactoring | ~90% as good | Reference |

| "Explain this code" | Equivalent | Reference |

| Multi-file agentic task | ~70% as good | Reference |

| Novel algorithm from scratch | ~75% as good | Reference |

| Autocomplete latency | ~80ms | ~200-500ms |

| Cost per day (heavy use) | $0 | $1-5 for API, $20/mo+ for subscription |

| Offline | Works | Doesn't |

The gap is real on the two categories where frontier closed models still dominate — long-horizon agentic tasks and novel algorithmic reasoning. The rest is close enough that the privacy and cost wins dominate for sensitive work.

The hybrid workflow I actually recommend

Most professionals should not pick one tool. They should run two, with a clear rule about which touches what.

Local stack (Continue.dev + Ollama + Qwen Coder):

Client code
Internal repos
Anything under an NDA
Anything touching customer data
Security-sensitive work
Personal projects you plan to monetize

Cloud stack (Cursor, Anthropic's Claude, Copilot):

Open-source contributions
Public tutorials and blog post code
Learning / throwaway experiments
Side projects you do not care about

The discipline is not "always local" or "always cloud" — it is "never route sensitive work through tools that log it." Local is the tool that lets you enforce that rule without sacrificing the productivity gains entirely.

What to watch for when evaluating any coding tool

If you are evaluating a new AI coding tool, these are the questions that actually matter:

Where does inference happen? Get a specific answer, not a marketing euphemism.
What is the training data policy, in writing, for the tier I would actually use? Not the enterprise tier you cannot afford.
What is the retention window? And what happens if there is a subpoena?
Does "codebase indexing" upload my repo to your servers? If yes, does deletion actually delete the embeddings?
Can I run this against my own model endpoint? If yes, the tool is inherently more trustworthy because you can verify with a network inspector.
Is there an enterprise SSO + zero-retention option? Good tools have one. Check whether it actually changes the data path.

Any vendor that cannot answer those questions clearly is not a serious option for sensitive work.

Lock down the rest of your developer stack

A local AI coding setup protects your source code from the AI layer. It does not protect the rest of the stack — your email, your calendar invites with engineering meeting notes, your browser history full of internal URLs, your cloud sync quietly uploading your project folder to a consumer Google account, or your home WiFi visible to your ISP.

Proton is the privacy stack that pairs with a local AI coding setup: end-to-end encrypted email for when your client sends specs, encrypted Drive for when you need to sync without leaking, Proton VPN for the network layer, and a zero-knowledge password manager for the credentials your AI tools never need to see. Swiss jurisdiction, open source clients, audited.

If you are careful enough to run your coding models locally, you are careful enough to close the other gaps too.

Get Proton Unlimited →