Best Self-Hosted AI for Coding: On-Premise Solutions in 2026

Cloud-based AI coding assistants like GitHub Copilot and Cursor have transformed software development, but they come with a significant trade-off: your code leaves your machine. For enterprises handling proprietary algorithms, regulated industries bound by compliance mandates, and privacy-conscious developers who simply want full control, self-hosted AI for coding is the clear answer. If you’re exploring options, check out our guide to Copilot vs Cursor vs Windsurf.

In 2026, the landscape of on-premise AI coding tools has matured dramatically. Open-source models like DeepSeek Coder V2, Qwen 2.5 Coder, and StarCoder2 now rival closed-source alternatives in quality, while tools like Continue.dev and Tabby make deploying them as easy as running a Docker container. Whether you want tab completions in VS Code, an AI pair programmer in your terminal, or a full enterprise deployment behind your firewall, there is a self-hosted solution that fits. We also cover this topic in our guide to best AI for coding.

This guide reviews 8 of the best self-hosted AI coding tools available today, comparing their features, pricing, model support, and ideal use cases so you can make an informed choice.

TL;DR: Quick Comparison

Short on time? Here is the quick overview:

• Best Overall (Free + Flexible): Continue.dev + Ollama
• Best Turnkey Server: Tabby (TabbyML)
• Best for Enterprise Teams: Sourcegraph Cody (Enterprise)
• Best for Terminal Developers: Aider with Local Models
• Best Open-Source Foundation Model: StarCoder2 by BigCode
• Best Copilot Drop-in Replacement: FauxPilot
• Best for Meta Ecosystem: Code Llama / Llama Code
• Best IDE Agent with Local Models: CodeGPT

1. Continue.dev + Ollama

The Gold Standard for Free, Local AI Coding

Continue.dev is an open-source AI code assistant that runs directly inside VS Code and JetBrains IDEs. When paired with Ollama as its local model backend, it delivers a fully self-hosted coding experience with zero data leaving your machine and zero subscription costs.

The setup is straightforward: install Ollama, pull a coding model like DeepSeek Coder V2 or Qwen 2.5 Coder, install the Continue extension, and start coding. The Autodetect feature scans your local Ollama installation and automatically lists all available models, so you can switch between them without editing config files.

Key Features

• Tab autocomplete, inline chat, and full agent mode
• Supports VS Code and JetBrains IDEs
• Automatic model detection from local Ollama instance
• Works with any OpenAI-compatible API endpoint
• Team features with centralized configuration and shared agents
• LiteLLM proxy support for multi-user deployments

Pricing

• Solo: Free ($0/developer/month) — full open-source extension
• Team: $10/developer/month — centralized config, shared agents
• Enterprise: Custom pricing — governance, priority support, self-hosting

Recommended Models

For chat and reasoning, DeepSeek R1 32B performs well. For fast autocomplete, Qwen 2.5 Coder 1.5B offers excellent speed with minimal resource usage. Teams with powerful GPUs can run larger models like Qwen 2.5 Coder 32B for enhanced accuracy.

Best For

Individual developers and small teams who want a completely free, fully private AI coding assistant with excellent IDE integration. If you are already using VS Code or a JetBrains IDE, this is the fastest path to self-hosted AI coding.

2. Tabby (TabbyML)

The Self-Contained, Self-Hosted Code Completion Server

Tabby takes a different approach from extension-only tools: it is a complete, self-hosted AI coding assistant server. With over 32,000 GitHub stars, Tabby has become one of the most popular open-source AI coding projects. It runs as a standalone service that provides code completion, chat, and context-aware suggestions to any connected IDE through its plugin ecosystem.

What sets Tabby apart is its all-in-one architecture. There is no external database management system required, no cloud dependency, and deployment is as simple as running a single Docker command. It ships with built-in support for popular code models including CodeLlama, StarCoder, and DeepSeek Coder.

Key Features

• Self-contained server with no external dependencies
• Code completion engine with real-time contextual suggestions
• Built-in Answer Engine for in-IDE Q&A
• Inline chat for real-time AI collaboration
• Context Providers to pull data from docs, configs, and APIs
• Enterprise SSO, auditability, and GPU/CPU inference
• Plugins for VS Code, JetBrains, Vim, and more

Pricing

• Community Edition: Free and open-source with unlimited tab completions
• Enterprise: Custom pricing — SSO, audit logs, priority support

Best For

Teams that want a turnkey, self-hosted AI coding server with minimal setup. Tabby is ideal if you need a centralized AI service that multiple developers can connect to from different IDEs, all running behind your firewall.

3. Sourcegraph Cody (Enterprise)

Enterprise-Grade AI Coding with Deep Codebase Understanding

Sourcegraph Cody is not just a code completion tool — it is an AI assistant built on top of Sourcegraph’s powerful code intelligence platform. This means Cody understands your entire codebase at a level that most competitors cannot match, drawing context from repositories, documentation, and code relationships across your organization.

For self-hosted deployments, the Enterprise plan allows Cody to run within your private network, giving you the advanced AI capabilities of a cloud tool with the data sovereignty of an on-premise solution. Cody can be configured to use different LLM backends, and its deep integration with Sourcegraph’s code graph provides uniquely accurate responses.

Key Features

• Repository-wide code context and intelligence
• Unlimited autocompletion suggestions
• Chat and custom prompt creation
• Multi-repo search and cross-reference capabilities
• Self-hosted Enterprise deployment option
• Flexible LLM backend configuration
• Enterprise SSO, access controls, and audit logging

Pricing

• Free: $0/month — unlimited autocomplete, 200 chats/month
• Enterprise Starter: $19/user/month — cloud-hosted, for growing teams
• Enterprise: $59/user/month — self-hosted option, advanced AI and code intelligence

Best For

Large engineering organizations that need AI coding assistance with deep understanding of massive, multi-repository codebases. If your team already uses Sourcegraph for code search, adding Cody is a natural and powerful extension.

4. Code Llama / Llama Code (Meta)

Meta’s Open-Weight Foundation Model for Self-Hosted Coding

Code Llama is Meta’s code-specialized variant of the Llama model family. Unlike the tools above, Code Llama is not an IDE extension or a server — it is a foundation model that you deploy on your own infrastructure and connect to your preferred coding tools. This makes it extremely flexible but requires more setup effort.

Available in 7B, 13B, 34B, and 70B parameter sizes, Code Llama supports Python, C++, Java, PHP, TypeScript, C#, Bash, and many more languages. The Python-specialized variant was further trained on 100 billion tokens of Python code, making it exceptionally strong for Python-heavy workflows. With the broader Llama 4 ecosystem now available, teams have access to even more capable open-weight models for coding tasks.

Key Features

• Multiple model sizes from 7B to 70B parameters
• Specialized Python variant trained on 100B tokens of Python code
• Instruction-tuned variant for natural language code generation
• OpenRAIL license allows commercial use
• Runs via Ollama, vLLM, TGI, or NVIDIA NIM containers
• Integrates with any tool supporting OpenAI-compatible APIs

Pricing

• Model weights: Free to download under community license
• Infrastructure cost: Your hardware only (GPU recommended)

Best For

Teams that want full control over their AI model, including the ability to fine-tune on proprietary codebases. Code Llama is the right choice if you have GPU infrastructure and want a proven, well-documented foundation model for coding that you can customize to your specific domain.

5. CodeGPT with Local Models

Privacy-First IDE Agent with Self-Hosted Flexibility

CodeGPT is an AI coding agent platform that brings agentic capabilities to VS Code, JetBrains, and Cursor. What makes it relevant for self-hosted workflows is its robust support for local model execution and BYOK (Bring Your Own Key) architecture — your code flows directly to AI providers under your own account, not through CodeGPT’s servers.

When configured with local models through Ollama or LM Studio, CodeGPT runs entirely offline with zero data sent to the cloud. Its agent can autonomously search, edit, and create files while understanding your entire project context, making it one of the more capable self-hosted coding assistants available.

Key Features

• Agentic coding: autonomous file search, editing, and creation
• Full project context awareness (not just single-file)
• BYOK architecture with all major LLM providers
• Local model support via Ollama and LM Studio
• Integration with AWS Bedrock, Azure OpenAI, and Google Vertex AI
• Custom AI agent creation for team workflows
• VS Code, JetBrains, and Cursor support

Pricing

• Free: 30 interactions/month, 200 autocomplete operations
• BYOK: Unlimited usage with your own API keys
• Teams: Starting at $7.20/month per user
• Enterprise: Custom pricing with self-hosted deployment

Best For

Developers who want a polished IDE experience with agentic capabilities while keeping data local. CodeGPT bridges the gap between the convenience of cloud AI agents and the privacy of self-hosted models, making it a strong option for teams transitioning from cloud-first to privacy-first workflows.

6. FauxPilot

The Open-Source Copilot Drop-In Replacement

FauxPilot takes a unique approach to self-hosted AI coding: it provides a Copilot-compatible API server. This means you can use existing GitHub Copilot extensions and IDE integrations while running your own backend server with open-source models. If your team is already using Copilot and wants to switch to a self-hosted solution without changing their workflow, FauxPilot offers the smoothest migration path.

Under the hood, FauxPilot uses NVIDIA’s Triton Inference Server with the FasterTransformer backend, running models like SalesForce CodeGen and SantaCoder. The setup requires more technical expertise than other options on this list, but it provides complete control over the inference infrastructure.

Key Features

• Copilot-compatible API for seamless migration
• Works with existing Copilot IDE extensions
• NVIDIA Triton Inference Server backend
• Support for CodeGen and SantaCoder models
• Multi-GPU model splitting for larger models
• OpenAI API, Copilot Plugin, and REST API access
• Docker-based deployment

Pricing

• Software: Free and open-source
• Infrastructure: Requires NVIDIA GPU with Compute Capability ≥ 6.0

Hardware Requirements

FauxPilot requires an NVIDIA GPU with sufficient VRAM for your chosen model. The 6B parameter CodeGen model needs approximately 12GB of VRAM. If you have multiple GPUs, you can split the model across them — two RTX 3080s can run the 6B model effectively.

Best For

Teams currently using GitHub Copilot who want to transition to a self-hosted solution without retraining developers on new tools. FauxPilot is also a strong choice for regulated industries that need Copilot-style completions behind a corporate firewall.

7. StarCoder2 (BigCode)

The Transparent, Research-Backed Open-Source Code Model

StarCoder2 is developed through an open-scientific collaboration between ServiceNow, Hugging Face, and NVIDIA under the BigCode project. It stands out for its exceptional transparency: unlike most coding models, StarCoder2 publishes its datasets, curation scripts, and training code alongside the model weights.

Available in 3B, 7B, and 15B parameter sizes, StarCoder2 was trained on The Stack v2 — a massive dataset spanning 619 programming languages built from Software Heritage’s source code archive, GitHub pull requests, Kaggle notebooks, and code documentation. The training set is 4x larger than the original StarCoder dataset.

Key Features

• 3B, 7B, and 15B parameter models
• Trained on 3.3 to 4.3 trillion tokens across 619 languages
• 15B model matches or outperforms CodeLlama 34B on key benchmarks
• Full transparency: published datasets, scripts, and training code
• Runs via Ollama, Hugging Face, or NVIDIA NIM containers
• OpenRAIL-M license permits commercial use
• Self-aligned instruction variant (StarCoder2-Instruct)

Pricing

• Model weights: Free under OpenRAIL-M license
• Infrastructure: Your hardware costs only

Performance Highlights

The StarCoder2-3B punches well above its weight, outperforming other code LLMs of similar size on most benchmarks. The 15B model is particularly impressive, matching or exceeding CodeLlama-34B despite being less than half its size. This efficiency makes StarCoder2 an excellent choice for teams with moderate GPU resources.

Best For

Organizations that value transparency and reproducibility in their AI toolchain. StarCoder2 is ideal for research teams, enterprises with audit requirements, and developers who want a well-documented, high-performing code model they can understand end-to-end.

8. Aider with Local Models

Terminal-First AI Pair Programming with Git Integration

Aider takes a fundamentally different approach to AI-assisted coding. Instead of living in your IDE as an extension, Aider operates from your terminal as an AI pair programmer that directly edits your codebase and manages Git commits. When connected to local models through Ollama, it becomes a fully self-hosted coding assistant with no data leaving your machine.

What makes Aider unique is its deep Git integration. It automatically commits changes with descriptive messages, understands diffs and history, and makes surgical edits across your codebase. This makes it particularly effective for refactoring, bug fixing, and feature implementation in large projects.

Key Features

• Terminal-based AI pair programming
• Automatic Git commits with descriptive messages
• Full codebase mapping for context-aware edits
• Support for 100+ programming languages
• Auto-linting and test execution on generated code
• Voice control for hands-free coding
• Works with local models via Ollama
• Compatible with any OpenAI-compatible API

Pricing

• Software: Free and open-source (Apache 2.0 license)
• LLM costs: Free with local models, or pay-per-use with cloud APIs

Recommended Local Setup

For local usage, pair Aider with Ollama running DeepSeek Coder V2 or Qwen 2.5 Coder. Processing files costs are essentially zero with local models. For complex projects requiring higher reasoning, you can optionally connect to a cloud model while keeping your code local through a self-hosted proxy.

Best For

Terminal-first developers who want transparent, auditable AI code changes with first-class Git integration. Aider is the top choice for developers who prefer command-line workflows and want every AI-generated change tracked in version control automatically.

Comparison Table: Self-Hosted AI Coding Tools in 2026

Tool	Type	Free Tier	Paid Plans	IDE Support	Local Models	Best For
Continue.dev + Ollama	IDE Extension	Yes (full)	From $10/user/mo	VS Code, JetBrains	Ollama, LM Studio, any OpenAI-compatible	Best free all-rounder
Tabby (TabbyML)	Self-hosted Server	Yes (full)	Enterprise (custom)	VS Code, JetBrains, Vim	CodeLlama, StarCoder, DeepSeek	Turnkey team server
Sourcegraph Cody	Platform + Extension	Yes (limited)	$19–$59/user/mo	VS Code, JetBrains, Neovim	Via Enterprise self-hosted	Large enterprise teams
Code Llama	Foundation Model	Yes (model weights)	Infrastructure only	Via integration tools	Native (is the model)	Custom fine-tuning
CodeGPT	IDE Agent	Yes (limited)	From $7.20/user/mo	VS Code, JetBrains, Cursor	Ollama, LM Studio	Agentic IDE workflows
FauxPilot	API Server	Yes (full)	Infrastructure only	Any Copilot-compatible	CodeGen, SantaCoder	Copilot migration
StarCoder2	Foundation Model	Yes (model weights)	Infrastructure only	Via integration tools	Native (is the model)	Transparent & auditable AI
Aider	Terminal Tool	Yes (full)	LLM API costs only	Terminal (+ IDE extensions)	Ollama, any OpenAI-compatible	Terminal-first Git workflows

How to Choose the Right Self-Hosted AI Coding Tool

Selecting the right self-hosted AI coding tool depends on several factors specific to your team and workflow. Here is a decision framework to guide your choice.

Consider Your Deployment Model

If you want the simplest possible setup, Continue.dev + Ollama or Tabby are your best options. Both can be running in under 30 minutes. If you need a centralized server that multiple developers connect to, Tabby’s architecture is purpose-built for this. For enterprise deployments with SSO, audit logging, and dedicated support, Sourcegraph Cody Enterprise or Tabby Enterprise provide the governance features you need.

Evaluate Your Hardware

Self-hosted AI coding requires GPU resources. Here are general guidelines:

• 7B models (entry level): 8GB VRAM — RTX 3060 or Apple M1 Mac
• 13B–30B models (balanced): 16–24GB VRAM — RTX 4090 or M2 Pro/Max Mac
• 70B+ models (maximum quality): 48GB+ VRAM — multiple GPUs or A100

For CPU-only setups, quantized models (Q4 or Q5) through Ollama can run on machines with 16GB+ RAM, though response times will be slower.

Match Your Workflow

• IDE-first developers: Continue.dev, Tabby, CodeGPT, or Cody
• Terminal-first developers: Aider
• Teams migrating from Copilot: FauxPilot
• Teams wanting to fine-tune models: Code Llama or StarCoder2

Budget Considerations

Most self-hosted coding AI tools are free at the software level. Your primary costs will be hardware (GPUs) and electricity. For a single developer, a consumer GPU like the RTX 4090 ($1,600–$2,000) provides excellent performance. For teams, a dedicated inference server with an A100 or multiple RTX GPUs starts around $8,000–$15,000 but eliminates ongoing per-seat subscription costs.

Hardware Requirements at a Glance

Model Size	VRAM Needed	Example GPU	Use Case
1.5B–3B	4–6 GB	RTX 3060, M1 Mac	Fast autocomplete
7B	8 GB	RTX 3070, M1 Pro	Good all-around coding
13B–15B	16 GB	RTX 4090, M2 Pro	High-quality completions
32B–34B	24 GB	RTX 4090 (24GB), A5000	Near-cloud quality
70B+	48 GB+	A100, dual RTX 4090	Maximum accuracy

Frequently Asked Questions

What is the best free self-hosted AI for coding?

Continue.dev paired with Ollama is the best free self-hosted AI for coding in 2026. The extension is completely free, Ollama is open-source, and models like DeepSeek Coder V2 and Qwen 2.5 Coder are free to download. Your only cost is the hardware to run them.

Can self-hosted AI coding tools match GitHub Copilot quality?

With the right model, yes. Models like DeepSeek Coder V2, Qwen 2.5 Coder 32B, and StarCoder2-15B perform comparably to cloud-based solutions on most coding tasks. The gap has narrowed significantly in 2026, especially for common programming languages and standard development patterns.

What GPU do I need to run a local coding AI?

For basic autocomplete with small models (1.5B–3B), a GPU with 6GB VRAM suffices. For high-quality completions and chat, you want at least 16GB VRAM (RTX 4090 or Apple M2 Pro). For the best results with large models, 24GB+ VRAM is recommended. CPU-only inference is possible with quantized models but will be significantly slower.

Is self-hosted AI coding legal for commercial use?

Yes, most self-hosted coding models and tools use permissive licenses. StarCoder2 uses OpenRAIL-M, Code Llama uses Meta’s community license, and tools like Continue.dev, Tabby, and Aider use Apache 2.0 or similar open-source licenses. Always review the specific license terms for your chosen model and tool combination. For more recommendations, see our list of AI code review tools.

How do self-hosted AI coding tools handle code privacy?

When running locally, your code never leaves your machine or network. Tools like Continue.dev with Ollama create a completely air-gapped environment. Even team deployments with Tabby or FauxPilot keep all data within your infrastructure. This is the primary advantage over cloud-based alternatives.

Can I use self-hosted AI coding tools without a GPU?

Yes, but with limitations. Ollama supports CPU-only inference using quantized models (GGUF format). Performance varies: small models (1.5B–3B) run reasonably well on modern CPUs with 16GB+ RAM, while larger models will be noticeably slow. Apple Silicon Macs offer a good middle ground with unified memory that enables decent performance without a discrete GPU.

Final Thoughts

The self-hosted AI coding landscape in 2026 offers genuine alternatives to cloud-based services. Whether you are a solo developer protecting your side project’s code, a startup navigating data privacy requirements, or an enterprise with strict compliance mandates, there is a self-hosted solution that fits your needs and budget.

For most developers, starting with Continue.dev + Ollama is the recommended path. It is free, easy to set up, and works with the IDEs you already use. As your needs grow, you can explore Tabby for team deployments, Sourcegraph Cody for enterprise-scale intelligence, or Aider for terminal-driven workflows.

The key takeaway: you no longer need to sacrifice code quality or developer experience to keep your code private. Self-hosted AI for coding has arrived, and it is ready for production use.

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