Refact.ai
Refact.ai / refact.ai
Open source AI coding agent and IDE extension that can be self-hosted on your own infrastructure, providing code completion, chat, and autonomous coding agents with full data privacy.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
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What is Refact.ai?
Refact.ai is an open source AI coding assistant that differentiates from GitHub Copilot and Cursor by offering a self-hosted deployment option — companies can run Refact.ai's server on their own infrastructure, so all code remains within their own network without being sent to any external cloud service.
The self-hosted architecture is particularly valuable for organisations with strict data security requirements — government contractors, financial services firms, healthcare companies, and enterprises with proprietary code that cannot be sent to external AI APIs. With self-hosting, the AI model runs on the company's own GPU servers and code never leaves the internal network.
The code completion and chat capabilities are available as IDE extensions for VS Code and JetBrains, providing an experience similar to GitHub Copilot but backed by the self-hosted server. Multiple models are supported including Code Llama, StarCoder, and DeepSeek Coder, giving the administrator flexibility in choosing models based on their own capability and hardware requirements.
Refact Agent, the agentic coding feature, can use tools — running bash commands, reading files, executing tests — to complete multi-step coding tasks autonomously. This agentic capability in an open source self-hosted package makes Refact.ai notable for enterprises that want agentic AI without cloud dependency.
For teams that are not comfortable with code going to GitHub Copilot, Cursor, or other cloud AI services due to data policies, Refact.ai provides a credible open source alternative with active community development.
How Refact.ai works
Refact.ai runs as llm coding model software built around code and text workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on vs code, jetbrains, and cli, with API access for teams that want to embed it into their own products.
Watch Refact.ai in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating Refact.ai.
Self-hosted deployment
Server runs on customer's own infrastructure (on-premise or private cloud) so code never leaves the internal network, providing full data sovereignty for security-sensitive organisations.
Refact Agent
Agentic multi-step coding capability using tools including bash execution, file reading, and test running — autonomous coding tasks in a self-hosted package without cloud dependency.
Multiple model support
Configurable model backend supporting Code Llama, StarCoder2, DeepSeek Coder, and other models, allowing selection based on capability needs and available hardware.
Best use cases
Who should use it
Pros
- Self-hosted deployment keeps code within customer infrastructure — strongest data privacy among AI coding tools
- Open source model allows inspection and modification of the server code
- Multiple model support gives flexibility to choose based on capabilities and GPU hardware
- Agentic multi-step coding in a self-hosted package is unique in this space
Cons
- Self-hosting requires GPU hardware investment and IT operational overhead
- Model quality is competitive but below frontier models like Claude and GPT-4o on complex coding tasks
- Active development but smaller community than GitHub Copilot or Cursor ecosystems
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Is it worth the price?
Free and open source self-hosted version. Refact.ai cloud pro plan $20/month. Enterprise on-premise custom pricing.
Model
Open Source
Starting price
Free
Free trial
No
Tools like Refact.ai
GitHub Copilot provides the strongest models but all code goes to Microsoft/GitHub. Cursor provides the best IDE experience but also requires cloud. Tabnine provides privacy-focused cloud coding AI with enterprise data controls. Continue.dev provides open source AI coding with BYO model.
Refact.ai vs Meta Llama
A side-by-side look at the closest alternative in this category.
Refact.ai
Refact.ai
Meta Llama
Meta AI
Free and open source self-hosted version. Refact.ai cloud pro plan $20/month. Enterprise on-premise custom pricing.
Free to download and use commercially (with usage limits at scale). Meta provides API access via partners (AWS, Azure, NVIDIA). Open source under the Llama Community License.
Self-hosted: data never leaves customer's infrastructure. Cloud version: review Refact.ai privacy policy. Enterprise includes data handling agreements.
Open source model under Meta Llama Community License. Derivative models subject to same licence terms. Commercial use permitted with usage threshold restrictions.
Meta's data handling: no data sent to Meta when running locally. Cloud API providers subject to their own data handling policies.
Self-hosted deployment keeps all code within customer's own infrastructure. Cloud service processes code on Refact.ai's servers — review privacy policy for cloud version use.
When running Llama locally, no data is sent to Meta or any external party. Meta's cloud API providers have their own data handling policies. Review the Llama Community License for commercial use restrictions above 700M MAU.
- Self-hosted deployment keeps code within customer infrastructure — strongest data privacy among AI coding tools
- Open source model allows inspection and modification of the server code
- Multiple model support gives flexibility to choose based on capabilities and GPU hardware
- Agentic multi-step coding in a self-hosted package is unique in this space
- Completely free to use without API fees — massive cost advantage for high-volume applications
- Full data privacy when run locally — no data leaves company infrastructure
- Largest ecosystem of fine-tuned derivatives, tools, and community support of any open source model
- Llama 3.1 405B performance competitive with frontier proprietary models on many benchmarks
- Self-hosting requires GPU hardware investment and IT operational overhead
- Model quality is competitive but below frontier models like Claude and GPT-4o on complex coding tasks
- Active development but smaller community than GitHub Copilot or Cursor ecosystems
- Running 405B model requires significant GPU hardware investment for on-premise deployment
- Smaller Llama variants (8B) less capable than frontier proprietary models for complex tasks
- Meta's commercial use licence has restrictions at very large scale (700M+ MAU) — review for large applications
Technical & deployment info
Key facts about model providers, platforms, and team support.
Model Provider
Open Source (Code Llama, StarCoder, DeepSeek Coder)
Models
Code Llama, StarCoder2, DeepSeek Coder
Platforms
VS Code, JetBrains, CLI
Deployment
Self-hosted, SaaS
Integrations
GitHub, GitLab, Docker, Kubernetes, API
Team Collaboration
No
Launch Year
2023
Security & privacy
Compliance signals and data-handling notes as reported by the vendor.
Self-hosted: data never leaves customer's infrastructure. Cloud version: review Refact.ai privacy policy. Enterprise includes data handling agreements.
Self-hosted deployment keeps all code within customer's own infrastructure. Cloud service processes code on Refact.ai's servers — review privacy policy for cloud version use.
What users are saying
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Common questions about Refact.ai
Editorial Verdict
Should you use Refact.ai?
Refact.ai is the best AI coding tool for organisations with strict data security requirements who need on-premise or air-gapped AI coding assistance where code cannot be sent to external cloud services.
Last verified July 24, 2026.