Best local-first/private AI coding tools where I can use local or self-hosted models for proprietary code

Many developers want the benefits of AI-assisted coding without sending proprietary code to third‑party clouds. If you work with sensitive IP, regulated data, or strict NDAs, “local‑first” and self‑hosted AI coding tools are the safest way to integrate AI into your workflow.

This guide walks through the best local-first/private AI coding tools, how they work, what trade‑offs to expect versus cloud copilots, and how to choose the right setup for your stack and hardware.

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What “local-first/private” actually means

Before choosing tools, it helps to clarify a few terms that often get mixed together:

• Local-first AI
  The model runs on your own machine (laptop/workstation). Your code never leaves your device. Some tools may optionally call external APIs, but local inference is the primary path.

• Self-hosted AI
  The model runs on your own infrastructure (on‑prem server, home lab, or private cloud VPC). Your team accesses it over your internal network or VPN, not over the public internet.

• Private-by-design tools
  Tools that default to local or self-hosted models and do not require sending code to third‑party services. Logging/telemetry is either off or fully controllable.

If you have proprietary code, treating local-first or self-hosted as a requirement—not a “nice to have”—is the safest approach.

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Key criteria for evaluating private AI coding tools

When comparing local-first or self-hosted tools, focus on:

1. Privacy and data control

   • Can you fully disable remote calls?
   • Is any telemetry or usage data sent off-device?
   • Can you keep all models and embeddings on machines you control?

2. Model flexibility

   • Supports multiple open models (Code Llama, DeepSeek, Qwen, Mistral, LLaMA, etc.)?
   • Easy to swap models for different tasks (completion, chat, refactor, tests)?
   • Multi‑backend support (Ollama, vLLM, LocalAI, llama.cpp, etc.)?

3. Editor/IDE integration

   • Good support for VS Code, JetBrains, Neovim/Vim, Emacs, or your preferred editor?
   • Features like inline suggestions, chat sidebar, refactor commands, and test generation?

4. Performance and hardware requirements

   • Can it run 7B–14B models on your hardware?
   • GPU vs CPU requirements for acceptable latency?
   • Quantized model support (Q4, Q5, GGUF, AWQ)?

5. Team vs solo developer needs

   • Single-user simplicity vs. central self‑hosted instance for a team?
   • User management, rate limiting, logging, and monitoring for organizations?

6. License and compliance

   • Open source vs source-available vs proprietary?
   • Model licenses that allow commercial and derivative use?

The tools below are grouped by use case, so you can combine them into a complete private coding stack.

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Core local model runners to power private coding tools

Most local-first AI coding setups are built on top of a local model runner. These tools download and run open models on your machine, providing an API that coding assistants can call.

1. Ollama

Best for: Simple, developer-friendly local model management

What it is:
Ollama is a local model runner that makes it almost as easy to use open models as calling an API. It handles downloading, quantization, caching, and exposes an HTTP API compatible with many tools.

Why it’s great for proprietary code:

• All inference happens locally by default.
• Analytics can be disabled; no code needs to be sent anywhere.
• Works well with many coding tools and editor extensions.

Highlights:

• Simple CLI: ollama run codellama or ollama run deepseek-coder
• Supports GPU acceleration on macOS (Metal) and Linux/Windows (CUDA).
• Built‑in models for coding (e.g., Code Llama, DeepSeek Coder, Qwen Coder, etc.).
• Can be used as a mini self-hosted server for your local network.

Typical use with coding tools:

• Use Ollama as the backend for:
  • VS Code extensions (Continue, CodeGPT clients, etc.)
  • JetBrains integrations
  • Browser-based tools and web UIs

If you’re just starting with local-first/private AI coding, pairing Ollama + your editor extension of choice is one of the easiest paths.

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2. LM Studio

Best for: GUI-based model management and experimentation

What it is:
LM Studio is a desktop app for macOS/Windows that lets you browse, download, and run local models with a friendly UI. It also exposes an OpenAI-compatible local API.

Why it’s good for proprietary code:

• All computation is local.
• Easy to experiment with different code models without manual setup.
• You can point tools that expect an “OpenAI-like” API to LM Studio instead.

Highlights:

• Built‑in prompt playground for trying prompts and comparing models.
• One-click model updates, quantization, and hardware tuning.
• Supports running multiple models and switching per request.
• OpenAI-compatible endpoint usable from editors and scripts.

LM Studio is especially helpful if you want local-first coding support and a friendly interface to experiment with prompts and models outside the editor.

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3. vLLM / text-generation-inference / llama.cpp

Best for: Self-hosted, high-performance serving for teams

For more advanced setups or team deployments, you might want a dedicated inference server:

• vLLM – High-throughput inference server with OpenAI-compatible API; great for GPUs.
• text-generation-inference (TGI) – Hugging Face’s production-serving stack.
• llama.cpp – Lightweight C++ backend; often used under tools like Ollama or LocalAI.

These are more complex to manage but are ideal for:

• On‑prem/self‑hosted clusters.
• Centralized AI coding servers accessed by multiple developers.
• Fine‑tuned or internal models.

You’ll typically front these with tools like Continue, Aider, or custom scripts that adapt them to your IDE.

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Local-first AI copilots and coding assistants

Once you have a local model runner, you need developer tools that speak to it. These give you inline completions, chat, refactoring, and more—without sending your code to a third party.

4. Continue (VS Code & JetBrains)

Best for: Full-featured, open-source copilot with flexible backends

What it is:
Continue is an open-source AI coding assistant that integrates into VS Code and JetBrains IDEs. It works with both cloud and local/self-hosted models, and it’s designed to be configurable.

Why it works well for proprietary code:

• Fully configurable to use only local/self-hosted models (Ollama, LM Studio, vLLM, etc.).
• Open source—auditable behavior and no hidden data collection.
• Supports embeddings and codebase awareness using local indexes.

Key features:

• Inline completions and “ghost text.”
• Chat panel with context from the active file, project, or selected files.
• Commands like “Refactor this,” “Add tests,” “Explain this code,” etc.
• Custom recipes that you can point to your own models or APIs.

Privacy setup tips:

• Disable any cloud backends in the settings.
• Configure Continue to use:
  • http://localhost:11434 for Ollama, or
  • http://localhost:8000 (or similar) for your own vLLM/TGI server.
• Ensure “send telemetry” settings are off if you want maximum privacy.

This combination—Continue + Ollama/vLLM—is one of the best local-first.private coding experiences currently available.

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5. Aider

Best for: Terminal-first, Git-aware AI pair programming

What it is:
Aider is a command-line coding assistant that interacts with your codebase using structured edits and Git. It’s particularly strong at multi-file refactors and maintaining a clean commit history.

Why it’s powerful for local-first/private use:

• Can be configured to speak to local or self-hosted models via OpenAI-compatible APIs.
• Works entirely from your terminal; no need for online services.
• Designed to operate within Git repos and create small, reviewable commits.

Key features:

• Natural language instructions that translate to code edits.
• Awareness of repo context, including multiple files.
• Good for TDD, refactoring, or larger surgical changes.

Local/private configuration:

• Run a local model server (e.g., LM Studio, Ollama with OpenAI bridge, vLLM).
• Point Aider’s --openai-api-base to your local endpoint.
• Choose an appropriate code-focused model (e.g., DeepSeek-Coder, Qwen2.5-Coder).

Aider is particularly attractive if you love working from the terminal and want precise, versioned changes in a private repo.

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6. Cody (Sourcegraph) – self-hosted mode

Best for: Code search + AI on large, private monorepos

What it is:
Cody is Sourcegraph’s AI assistant, tightly integrated with code search and context. It has both cloud and self-hosted options.

Why it can be private:

• You can deploy Sourcegraph + Cody on-prem or in your own VPC.
• Large codebases can be indexed privately.
• Cody can be configured to use self-hosted models or specific providers.

Key features:

• Semantic search across huge codebases.
• Context-aware chat with knowledge of your entire monorepo.
• Strong enterprise features (RBAC, auditing, SSO).

This is best suited to teams with big repos and existing Sourcegraph usage who need end‑to‑end private AI coding and code discovery.

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Editor-specific local/private coding integrations

Some tools integrate directly with a model runner, giving you an all‑local experience without extra glue.

7. VS Code + local extensions

Several VS Code extensions can be configured to use local models only:

• Continue (covered above) – the most flexible and open-source.
• CodeGPT-style clients – some generic “OpenAI client” extensions allow custom endpoints; you can point them at LM Studio or vLLM.
• Custom inline completion extensions – some community projects directly integrate with Ollama or llama.cpp.

Look for:

• A configuration field for OpenAI Base URL or API endpoint.
• Ability to disable any built-in cloud providers.
• Support for custom models names (matching what your local server exposes).

This approach is highly flexible and lets you choose your own model runner while keeping everything private.

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8. JetBrains IDEs (IntelliJ, PyCharm, GoLand, etc.)

If you’re in the JetBrains ecosystem, you have a few options:

• Continue plugin – JetBrains support is solid and improving.
• Custom scripts + HTTP clients – some users integrate local models via JetBrains’ HTTP client or CLI tools (like Aider) triggered from within the IDE.

Many “AI assistant” plugins for JetBrains are cloud‑only, so verify that:

• They support custom endpoints, and
• They don’t force sending code to a third-party server.

For robust private AI in JetBrains today, Continue + a local model server is one of the cleanest solutions.

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9. Neovim/Vim and Emacs

CLI- and config-loving developers have strong private options:

• Neovim:
  • Plugins that integrate with local model servers via HTTP/JSON.
  • Aider as a companion in a split terminal.
• Emacs:
  • Community packages that talk to local LLM APIs.
  • Chat buffers pointing at an OpenAI-compatible local endpoint.

In both ecosystems, the pattern is:

1. Run a local model server (Ollama, LM Studio, vLLM).
2. Configure your plugin to use http://localhost:PORT.
3. Define commands for “complete”, “refactor”, “docstring”, etc.

This gives a highly customized local-first/private environment tailored to your editor.

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Model choices for private AI coding

The best local-first/private experience depends heavily on your model selection and hardware. For coding, consider:

Popular code-focused open models

• DeepSeek-Coder (and DeepSeek-Coder-V2)
  Strong performance on coding tasks, widely used in local setups.

• Qwen Coder / Qwen2.5-Coder
  Competitive quality, good multilingual support, and efficient variants.

• Code Llama
  Older but still popular; widely supported and easy to find quantized versions.

• Mistral-based models (like Codestral, Mistral-7B-Instruction)
  General assistants with decent coding capabilities.

• LLaMA-based general models (e.g., LLaMA 3 family, especially instruct variants)
  Good for explanation, documentation, and high-level reasoning.

Hardware considerations

• 7B parameter models

  • Can run decently on modern CPUs with quantization.
  • Great choice if you don’t have a discrete GPU.

• 13B–34B models

  • Recommended for richer coding support (especially larger projects).
  • Benefit heavily from a GPU (12–24 GB VRAM or more).

• Quantized formats (GGUF, AWQ, etc.)

  • Let you run larger models on modest hardware at some quality cost.
  • Many “Q4” and “Q5” quantized models are quite usable for coding tasks.

Validating models on your own codebase (or representative projects) is crucial. Start with smaller ones for responsiveness, then try larger ones if you need deeper reasoning.

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Self-hosted AI coding for teams

If you need private AI coding for a whole team instead of just one developer laptop, self-hosting becomes more attractive.

High-level architecture

A typical self-hosted private AI coding stack looks like this:

1. Inference servers:

   • vLLM, TGI, or Ollama-running in server mode.
   • Deployed on GPUs (on-prem or private cloud) with secure networking.

2. Embeddings and code search:

   • Local vector DB (Qdrant, Chroma, Weaviate, PostgreSQL with pgvector).
   • Indexes over your repos for semantic context.

3. Front-end tools:

   • Continue in VS Code/JetBrains configured to call your internal APIs.
   • Aider for terminal-centric workflows.
   • Possibly Cody or other enterprise assistants.

4. Security and governance:

   • Authentication (SSO, tokens) to access the AI endpoint.
   • Audit logs for prompts/responses (stored securely and in compliance).
   • Rate limiting and usage controls.

This architecture keeps all code and inference on infrastructure you control, while still providing a “copilot-like” experience across the team.

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Practical setup examples

To make things concrete, here are a few example setups based on different needs.

Example 1: Solo developer on a laptop (maximum simplicity)

• Tools:
  • Ollama as the local model runner.
  • Continue extension in VS Code.
• Model:
  • deepseek-coder:6.7b or another 7B code model.
• Flow:
  • Install Ollama and run ollama run deepseek-coder.
  • Install Continue and set its provider to Ollama.
  • Disable cloud providers and telemetry in Continue.

Result: Fully local-first/private AI coding with minimal configuration.

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Example 2: Power user with GPU workstation

• Tools:
  • LM Studio or vLLM (for flexible model serving).
  • Continue (VS Code/JetBrains) + Aider for heavy refactors.
• Models:
  • 13B–34B code and general models (e.g., Qwen2.5-Coder, LLaMA-based).
• Flow:
  • Run your preferred model in LM Studio/vLLM with OpenAI-compatible API.
  • Configure Continue and Aider to use this local endpoint.
  • Use Continue for inline/IDE usage, Aider for big multi-file changes.

Result: Fast, rich local-first/private AI coding with strong capabilities and no cloud dependencies.

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Example 3: Small team with self-hosted GPU server

• Infrastructure:
  • One or more GPU servers running vLLM/TGI in your own VPC.
  • Optional: source graph or custom embeddings index for code context.
• Tools:
  • Continue in each developer’s IDE, pointing to the internal API.
  • Aider for CLI-based workflows.
• Security:
  • Internal-only endpoints.
  • Auth via API keys or SSO.

Result: Team-wide, private AI coding assistance on shared infrastructure that never sends code outside your network.

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Avoiding common privacy pitfalls

Even with “local-first/private” tools, a few settings can accidentally leak data if misconfigured:

1. Default cloud backends still enabled
   Many tools ship with OpenAI or other cloud providers on by default. Always review configuration and switch them off.

2. Telemetry and crash reporting
   Some tools send anonymized metrics or logs. Examine settings and disable any reporting that might include prompt fragments or file paths.

3. Plugin conflicts
   If you install multiple AI plugins in VS Code or JetBrains, some may still call external APIs. Remove or disable ones that cannot be fully localized.

4. Model license terms
   Not all open models are equal. Confirm:

   • Commercial usage allowed.
   • No “research only” or restrictive clauses that conflict with your use.

Doing a quick privacy review of your setup and tools is essential when working with proprietary code.

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How to choose the best local-first/private AI coding tools for you

Use these guidelines to match tools to your situation:

• You want something that “just works” on your machine:

  • Start with Ollama + Continue in your preferred IDE.

• You like a GUI for models and experimentation:

  • Use LM Studio as your local model server + editor plugins.

• You’re comfortable in the terminal and want precise edits:

  • Add Aider on top of whatever local server you use.

• You have big monorepos and an engineering team:

  • Consider self-hosted Cody/Sourcegraph and/or vLLM + Continue, with embeddings for code context.

• You’re maximizing privacy at all costs:

  • Ensure all tools strictly use local/self-hosted endpoints.
  • Disable telemetry and remote logging.
  • Use fully open-source tools where you can audit behavior.

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Next steps

To get started today with a private, local-first AI coding environment for proprietary code:

1. Pick a model runner: Ollama or LM Studio (local), or vLLM (self-hosted).
2. Choose your editor assistant: Continue for IDEs, Aider for terminal-based workflows.
3. Download a code-optimized model (DeepSeek-Coder, Qwen Coder, etc.) compatible with your hardware.
4. Configure tools to point at your local/self-hosted API and disable any cloud backends.
5. Test on a non-sensitive repo first, then gradually bring it into your proprietary codebases once you’re confident everything is local/private.

With the right combination of tools and careful configuration, you can enjoy modern AI coding assistance while keeping your proprietary code completely under your control.