Your code is your competitive advantage. Every function, every architectural decision, every proprietary algorithm you paste into a cloud AI assistant is potentially feeding a training dataset you have no visibility into.

GitHub Copilot, Claude.ai, and ChatGPT are useful. They are also subscription services that route your code through corporate servers, subject to terms of service that can change, under jurisdiction-dependent data retention policies that most developers never read.

If you work on a proprietary codebase — or you simply do not want Microsoft, Anthropic, or OpenAI training on your side projects — there is now a mature ecosystem of AI coding assistants that run entirely locally or self-hosted. No cloud handshake. No code leaving your machine.

Here are the six best options in 2026, tested and ranked.

The Quick Answer

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Bottom line before we go deep: If you already run Ollama or LM Studio, pair it with Continue.dev — 10 minutes to set up, gives you 80% of Copilot's daily-driver UX with zero data leaving your machine. If you want a full Cursor replacement with a polished GUI, use Void. If you are equipping a team, Tabby is the right call.

Why Privacy Matters for Code Specifically

Most privacy arguments are abstract. This one is concrete.

When you paste code into a cloud AI assistant — or even just use a cloud-connected IDE plugin — the code transiting their servers includes more than you typically intend to share:

API keys and credentials. Developers hardcode secrets in development more than they admit. If that file is in your editor's context window, it goes along for the ride.

Proprietary algorithms. Trade secrets, unpublished product logic, and client IP can all appear in the snippets that get sent for inference. Most enterprise NDAs explicitly prohibit this. Most developers do it anyway because it is the path of least resistance.

System architecture. Function signatures, database schemas, service interfaces — the shape of your system is often more sensitive than any single file.

The terms of service for major AI coding tools typically allow you to opt out of training data usage. What they do not offer is an opt-out from code transiting their inference infrastructure. If your threat model includes "I do not want a third-party company to see my proprietary code," training opt-outs do not resolve the issue.

Local inference closes this gap entirely. The model runs on your hardware. Your code never leaves your machine.

How We Evaluated These Tools

We tested each tool against a consistent set of criteria over several weeks of real development work:

Setup friction: How long from zero to working completions?
Inline autocomplete quality: How relevant and correct are suggestions in daily use?
Chat and instruction following: Can you describe a task and get useful output?
Local model compatibility: Does it work with the models developers actually run (Ollama, LM Studio, GGUF)?
Context window handling: Does the tool use surrounding file context intelligently?
Privacy posture: What exactly leaves your machine, and when?

We specifically tested with qwen2.5-coder:7b via Ollama as the baseline local model — widely available, fits on 16GB RAM machines, and representative of what most developers running local inference are actually using.

1. Continue.dev — Best Overall for VS Code and JetBrains

Continue.dev is the tool most developers should start with. It is a free, open-source extension for VS Code and JetBrains IDEs that connects to any AI model — local models via Ollama or LM Studio, or cloud APIs if you choose. The key word is "if you choose."

The IDE integration is genuinely polished. You get inline autocomplete that activates as you type, a sidebar chat panel for longer conversations, and a command palette for common tasks — explain this function, write a unit test for this, refactor this to use async/await. All of these work using files open in your editor as context, without sending them anywhere unless your configured backend is a remote server.

Setting it up with Ollama takes about ten minutes:

```bash

Step 1: Pull a code-focused model

ollama pull qwen2.5-coder:7b

Step 2: Install the Continue VS Code extension

(search "Continue" in the VS Code extension marketplace)

Step 3: Open Continue's config.json and add Ollama as a provider

The extension walks you through this on first launch

```

Once configured, autocomplete suggestions come from your local Ollama instance. Chat responses come from your local Ollama instance. Nothing hits an external server.

Model recommendations for Continue + Ollama:

qwen2.5-coder:7b — best balance of speed and quality for 16GB RAM machines; fast enough that autocomplete does not feel sluggish
deepseek-coder-v2:16b — meaningfully better on complex tasks; needs 32GB RAM or a GPU with enough VRAM
qwen2.5-coder:32b — excellent output quality for demanding work; needs a machine with 32GB+ RAM or dedicated GPU

Trade-offs: Continue's quality ceiling is your local hardware. A 7B parameter model is genuinely competitive with cloud tools for autocomplete and simple tasks. For complex multi-file architectural reasoning or tasks that require reasoning about large codebases, cloud models with 100B+ parameters still have an edge. That gap is narrowing.

Privacy verdict: When configured with a local Ollama or LM Studio backend, Continue.dev sends nothing to external servers. The extension itself is open source and auditable on GitHub.

2. Aider — Best for Power Users and Large Refactors

Aider is not an IDE plugin. It is a terminal-based AI pair programmer that operates on your actual git repository. You give it instructions in plain English; it reads the relevant files, makes changes, shows you the diff, and commits them to git if you approve.

This workflow sounds awkward until you use it on a large refactor. Renaming a concept across 40 files, adding a required parameter to a function called in 30 places, converting a codebase from one error handling pattern to another — these are tasks where IDE plugins hit their limits. Aider handles them well.

Example interaction:

```

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

Added src/ files to the chat.

> Refactor all API handlers to use the new ErrorResponse class

> instead of throwing raw Error objects. Update tests to match.

I'll make changes across 8 files:

src/handlers/auth.ts

src/handlers/users.ts

src/handlers/payments.ts

...

[shows unified diff for all changes]

Apply these changes and commit? (y/n)

```

Connecting Aider to local models:

```bash

Install Aider

pip install aider-chat

Run with Ollama

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

Run with LM Studio's local server

aider --openai-api-base http://localhost:1234/v1 --model openai/local-model

```

What makes Aider stand out: It works on the file system level, not clipboard snippets. It understands git context. You can ask it to fix a failing test, explain what changed in the last commit, or implement a feature described in a GitHub issue. The quality of its output is directly proportional to how precisely you describe the task — this rewards developers who think in terms of specifications.

Trade-offs: Terminal-first means a learning curve if you prefer graphical tools. Aider is best paired with a separate tool like Continue.dev for inline autocomplete while you edit. Both tools can point to the same Ollama instance with no conflict.

Privacy verdict: When configured with a local model backend, Aider runs entirely on your machine. Cloud API configuration is explicit — you know exactly which mode you are in.

3. Tabby — Best Self-Hosted Option for Teams

Tabby is what you deploy when you need to give an entire team access to AI coding assistance without routing code through GitHub's infrastructure or paying per-seat cloud subscription costs.

It is a self-hosted AI coding server with a web admin interface and IDE plugins for VS Code, JetBrains, and Neovim. You run the server — on a spare workstation, a Linux server with a GPU, or a cloud VM you control — and every developer on the team connects to it via a lightweight plugin. The model inference happens on your hardware.

Getting started with Tabby via Docker:

```bash

CPU inference (slower but works without a GPU)

docker run -it -p 8080:8080 -v $HOME/.tabby:/data \

tabbyml/tabby serve --model Qwen2.5-Coder-1.5B

NVIDIA GPU (dramatically faster)

docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data \

tabbyml/tabby serve --model Qwen2.5-Coder-7B --device cuda

Apple Silicon

docker run -it -p 8080:8080 -v $HOME/.tabby:/data \

tabbyml/tabby serve --model Qwen2.5-Coder-7B --device metal

```

What makes Tabby stand out for teams:

Tabby includes repository indexing. It can crawl your codebase and use that indexed knowledge to provide suggestions grounded in your actual patterns — not generic training data patterns. If your team has established conventions for how you structure services, handle errors, or name things, Tabby will reflect those conventions in its suggestions after indexing.

The admin interface shows per-user usage metrics, allows token management, and lets you swap models without touching each developer's workstation.

Model recommendations for Tabby:

Qwen2.5-Coder-1.5B — fast, low hardware requirements, adequate for autocomplete
Qwen2.5-Coder-7B — the sweet spot for a team server with a decent GPU
StarCoder2-7B — strong multi-language support, particularly for less common languages

Trade-offs: Tabby requires server setup and ongoing maintenance. It is not complicated, but it is more operational overhead than installing an extension. For solo developers, Continue.dev or Aider gets you there in a fraction of the time. Tabby's value scales with team size.

Tabby Cloud offers hosted inference as an option. That tier does route code through Tabby's servers — worth knowing if you choose it.

Privacy verdict: Self-hosted Tabby is fully private. Your code, your server, your inference hardware.

4. Void — Best Cursor Replacement for Privacy-Conscious Developers

Cursor became the breakout AI editor of 2024. It also routes your code through Anthropic and OpenAI APIs by design — that is the product, not a bug. If you agreed to Cursor's terms of service, you agreed to this.

Void is an open-source fork of VS Code that replicates Cursor's AI UX while giving you explicit control over the inference backend. You get Cursor's integrated experience — inline edits with a keyboard shortcut, an agent mode that makes multi-step changes, a composer for larger tasks — with the option to wire it to a local Ollama instance instead of a cloud API.

Configuring Void with local models:

Download Void from voideditor.com
Open Settings → AI Providers
Set provider to Ollama, point to http://localhost:11434
Select your model from the dropdown

The setup takes five minutes if you already have Ollama running.

What makes it stand out: If you have Cursor muscle memory — the keyboard shortcuts, the workflow patterns, the inline edit behavior — Void preserves them. It is the lowest-friction privacy upgrade for former Cursor users. You are changing where the inference happens, not how you interact with the editor.

Trade-offs: Void is newer than the other tools on this list and has had rough edges. Extension compatibility with the VS Code ecosystem is generally good but occasionally specific extensions have issues. Development is active, and stability has improved significantly over 2025.

Privacy verdict: With a local model backend configured, Void keeps all inference on your machine. Like Aider, it supports cloud API connections too — the configuration makes it explicit which you are using.

5. Cody by Sourcegraph — Best for Large Proprietary Codebases

Cody has one capability that no other tool on this list matches: it indexes your entire repository and uses that index to answer questions grounded in your actual codebase, not in generic patterns from its training data.

Ask Cody "where does our application handle authentication?" and it searches your actual codebase to answer. Ask it to "write a new API handler that follows our existing patterns" and it looks at your existing handlers before writing. For large, mature codebases with established conventions, this produces significantly better results than tools that only have access to your currently open files.

Deployment options:

Self-hosted Sourcegraph + Cody: Fully private. Your server, your model backend, your codebase index. Requires standing up a Sourcegraph instance, which is non-trivial operational work.
Cody.dev (hosted): Your code goes to Sourcegraph's servers for indexing and inference. Different privacy posture.

The self-hosted path is the right one for teams with strict IP requirements. It requires Sourcegraph Enterprise licensing, which affects the economics for smaller teams.

Trade-offs: The setup complexity is real. Self-hosted Sourcegraph is enterprise software. For a small team or solo developer, the operational overhead is hard to justify when Continue.dev + a well-organized editor window gets you most of the way there.

Privacy verdict: Self-hosted deployment is fully private. The hosted Cody.dev option routes code through Sourcegraph's servers.

6. CodeGPT — Best for Developers Switching from Copilot

CodeGPT is a VS Code extension built for developers who want to swap out their AI backend without changing how they work. The interface is deliberately familiar — inline completions, a chat panel, code explanation on demand — and connecting it to a local model is a configuration change, not a workflow change.

Setting up CodeGPT with Ollama:

Install the CodeGPT VS Code extension
Open the extension settings
Select "Ollama" as the provider
Enter http://localhost:11434 as the API endpoint
Choose your model

That is it. Your completions now run locally.

What CodeGPT does well:

Lowest barrier to entry on this list — familiar UX for anyone coming from Copilot
Supports Ollama, LM Studio, and most OpenAI-compatible local servers
Clean settings interface that does not require editing JSON config files

What it does not do as well as Continue.dev:

Less configurable — fewer options for custom prompts, context providers, or model profiles
Smaller community and fewer integrations
No agentic or multi-step task capabilities

For developers who want privacy with minimal friction and no need for advanced features, CodeGPT gets there fastest. For developers who want to customize their AI workflow, Continue.dev is the better investment of setup time.

Privacy verdict: With a local Ollama endpoint configured, CodeGPT is local-only. CodeGPT Plus is a hosted tier — if you use it, code routes through their servers.

Hardware: What You Actually Need

All six tools run better with more RAM and a capable GPU. Practical guidance by setup:

16GB RAM, modern CPU, no discrete GPU:

Continue.dev or CodeGPT + Ollama with a 7B model. Autocomplete feels responsive. Chat responses take a few seconds. This setup works for most daily development tasks.

32GB RAM, no GPU (or Apple Silicon Mac):

You can run 13B–32B parameter models comfortably. The jump from 7B to 32B produces noticeably better output on complex tasks — better reasoning about multi-file changes, more accurate test generation, fewer hallucinated API signatures.

NVIDIA GPU (8GB+ VRAM):

GPU inference is dramatically faster than CPU inference for the same model. An RTX 3060 12GB or RTX 4070 gives you a meaningful quality jump by making larger models practical to use. The RTX 4090 is overkill for most developers but handles 70B models reasonably.

Apple Silicon Macs:

The unified memory architecture makes M-series Macs genuinely competitive with GPU setups for local inference. An M3 Pro with 36GB unified memory runs 32B models well. M4 Max machines handle even larger models effectively.

If you want a portable machine capable of running 13B models as a daily driver, Framework Laptop 16 supports RAM upgrades to 64GB and has GPU module options — more serviceable and upgradeable than comparable alternatives.

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Recommended Setups by Developer Type

Solo developer on VS Code or JetBrains:

Continue.dev + Ollama + qwen2.5-coder:7b. Install time: 15 minutes. Cost: $0. This is the right starting point for almost everyone.

Power user who lives in the terminal:

Aider + Ollama for agentic tasks, Continue.dev for inline autocomplete. Both point to the same Ollama instance. No conflict.

Developer migrating from Cursor:

Void + Ollama. Preserves your muscle memory, replaces the cloud backend with local inference. Configure time: 20 minutes.

Small team (2–10 developers):

Tabby on a shared server with a GPU. Everyone connects via the VS Code or JetBrains plugin. No per-seat subscription costs. Shared codebase indexing.

Team on a large, mature proprietary codebase:

Evaluate Cody with self-hosted Sourcegraph if you can justify the operational overhead. The codebase-aware context is the differentiating feature that smaller-scope tools cannot match.

The Fastest Path to Local AI Coding

If you want to start today:

```bash

Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Pull a code model (fits on 16GB RAM)

ollama pull qwen2.5-coder:7b

Install Continue from the VS Code extension marketplace

Open Continue's settings, select Ollama, select your model

Start coding

```

You will have a working private AI coding assistant in under 15 minutes. No account. No subscription. No code leaving your machine.

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Last updated: 2026-05-27