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Refact.ai - AI Coding Assistant with Self-Hosting

Refact.ai is a privacy-focused AI coding assistant with self-hosting and on-prem options. It provides code completions, chat, and agentic features while keeping your code secure.

AI, Coding and Development
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ℹ️

WhatAI Decision Box

✓
Best for:

Developers and enterprises that require strong data privacy, self-hosting, or on-prem deployment while still wanting capable AI coding assistance.

✗
Not for:

Users who want the absolute simplest setup with zero configuration or those needing fully managed SaaS with minimal privacy concerns.

⇆ Often compared with

ℹ️ WhatAI Field Note

  • Self-hosting gives full control over data and models but requires more initial setup and maintenance compared to cloud-only solutions.
  • Performance and suggestion quality depend heavily on the chosen LLM; local models may be slower but offer maximum privacy.

Refact.ai is an AI-powered coding assistant designed with a strong emphasis on privacy and flexibility. It delivers inline code completions, intelligent chat, refactoring suggestions, and agentic capabilities directly in the IDE. Users can run it in the cloud or self-host it on their own infrastructure, with support for local and custom models.

Features and Capabilities

Refact.ai offers context-aware code completions, multi-line suggestions, AI chat for code explanation and refactoring, agentic features for task automation, and codebase indexing for better awareness. It supports major IDEs (VS Code, JetBrains, etc.) and provides self-hosting options including fully on-prem and air-gapped deployments. Key elements include support for multiple LLMs (local or cloud), customizable rules and policies, usage analytics, and strong data privacy guarantees.

About Refact.ai

Refact.ai assists developers by offering real-time code suggestions and conversational help while keeping code data private. The workflow involves installing the IDE extension, optionally self-hosting the backend, connecting preferred models (local or cloud), and receiving completions or using chat for tasks. It supports both individual and enterprise use with strong governance features. Additional functions include codebase awareness, custom rules, and analytics. Plans differ in features, usage limits, and deployment options.

Use Cases

Developers working with sensitive code use Refact.ai self-hostedEnterprises implement secure AI coding assistance with Refact.aiTeams needing on-prem deployment choose Refact.aiSecurity-conscious organizations run Refact.ai air-gappedIndividual developers seeking privacy-focused alternatives use Refact.ai

Pricing

Free / Community

$0

  • • Basic features
  • • Self-hosted or limited cloud use

Pro

$15-$25/user/mo

  • • Higher usage limits
  • • Advanced features
  • • Priority support

Enterprise

Custom

  • • Full self-hosting
  • • On-prem/air-gapped
  • • SSO
  • • Advanced governance
  • • Dedicated support
  • • Custom SLAs

Pricing varies by plan and region — see current pricing.

Details

Categories: AI, Coding and Development, Agents & Automation, Productivity
Skill Level: intermediate
Access Methods: browser, api

Tags

ai coding assistantrefact aiself hosted coding aion prem ai codingprivacy focused coding assistantai code completionai refactoring toollocal llm codingenterprise ai codingsecure code generation ai

Refact.ai Community Discussions

Explore community discussions. Ask and answer questions on Refact.ai to grow and learn together.

snorra_builds · Refact.ai AI, Coding and Development

Refact.ai ranked number one open-source on SWE-bench verified with 352 of 500 real issues resolved autonomously

The Refact.ai specific capabilities video https://www.youtube.com/watch?v=k4eSitgTKSc covers the SWE-bench milestone and the specific capabilities that produce that benchmark result. Resolving 352 out of 500 real-world software engineering issues from GitHub autonomously is the benchmark that requires clarification to appreciate. SWE-bench Verified does not test code generation quality. It tests autonomous end-to-end issue resolution: read the issue, understand the codebase, implement the fix, pass the existing tests. That is a multi-step autonomous engineering task, not completion assistance. The self-hosting and enterprise deployment options being available is the security consideration that determines whether Refact is viable for organisations with code confidentiality requirements. Your code stays on your infrastructure rather than passing through a cloud service. The customisation via fine-tuning on your specific codebase being available changes the suggestion quality from generic to project-specific over time. An agent that has learned your team's patterns, naming conventions and architectural preferences is different from one suggesting generic implementations of standard algorithms. For teams evaluating AI coding agents specifically for autonomous task completion rather than completion assistance: how are you currently measuring the quality of autonomous resolution versus needing to intervene and correct?
♥ 1 💬 0 👁 10 Reply →
haukur_writ · Refact.ai AI, Coding and Development

The shift to autonomous AI coding agents in 2026 is real and this video explains what actually changed

The AI coding agents state of the art video https://www.youtube.com/watch?v=zB3_cKkw940 is not specifically a Refact.ai video but it provides the industry context that makes Refact's SWE-bench ranking meaningful. The structural shift from AI as smart autocomplete requiring line-by-line supervision to AI agents that autonomously plan, execute and verify multi-step development tasks is the change that makes the SWE-bench benchmark relevant. SWE-bench tests whether an AI can resolve real GitHub issues autonomously, not whether it can suggest good code completions. The 2026 landscape described in the video has agents that understand project-wide context, write and run tests against their own code, identify and fix failures and integrate changes following team conventions without requiring step-by-step human guidance. That is a qualitatively different capability from the AI coding tools most developers are familiar with from 2023-2024. The implication for evaluation: the right benchmark for current AI coding tools is not whether they suggest good completions. It is whether they can take a well-specified task from description to merged PR with minimal intervention. That is what SWE-bench measures and what Refact's #1 open-source ranking on it is claiming. What specific development task category have you found benefits most from the autonomous agent approach versus still requiring significant human guidance at each step?
♥ 0 💬 2 👁 12 View 2 replies →
logn_writes · Refact.ai AI, Coding and Development

AI code review becoming a dedicated category in 2026 is the context that makes Refact.ai's testing tools relevant

The AI code review tools landscape video https://www.youtube.com/watch?v=rO2dx7nF6ZI frames the problem accurately: as AI coding assistants generate more code, human code review has become the bottleneck, and a new category of AI review tools exists specifically to address that bottleneck. The structural irony is elegant: AI generates more code, which creates more review work, which slows development, which means AI generation speed advantage is partially cancelled by review bottleneck. AI code review tools close that loop by making the review step faster and more thorough simultaneously. The tools in this category are not replacing human judgment in code review. They are handling the systematic checks, security pattern detection, test coverage gaps, style consistency, architectural violations, that are tedious but critical, and surfacing the results so human reviewers can focus on the architectural and logic questions that actually require judgment. Refact.ai's position in this landscape as both a generation and review tool is the integration argument. A tool that understands your codebase well enough to generate contextually appropriate code is also the right tool to review code against your project's specific patterns. For engineering teams: what percentage of your code review time currently goes to systematic checks versus genuine architectural or logic review and would AI review tools change that ratio?
♥ 0 💬 2 👁 10 View 2 replies →
OpenSourceDevOps_Finn · Refact.ai AI, Coding and Development

Refact.ai ranked number one on SWE-bench and I wanted to understand why so I tested it properly

When a tool ranks first on SWE-bench verified, resolving over 70% of real-world software engineering tasks, it gets my attention. I have been using Copilot and Cursor for a while and I was curious whether Refact.ai was actually meaningfully different or just benchmark-optimized. Spent a few weeks with it in VS Code and here is what I found. The codebase context is the first thing that stands out. It does not just work from the current file or a few tagged references. It analyzes your entire codebase and fine-tunes itself to your specific project, so suggestions are grounded in how your code actually works rather than generic patterns. That difference is noticeable when you are working in a large or idiosyncratic codebase rather than a clean greenfield project. Autonomous operation is the capability that separates it from standard autocomplete tools. You can give it a task and it plans, executes and deploys code without you micromanaging each step. The diff view shows you what changed so you can review before accepting, and file rollbacks are available if something goes wrong. Model flexibility is a genuine advantage. You can choose between GPT-4o, Claude 3.5 Sonnet and Gemini 2.5 Pro, or bring your own API keys. For teams with model preferences or cost constraints that is practically useful rather than just a feature checkbox. Integrations with GitHub, GitLab, MySQL, Postgres and the Chrome browser for testing round out a platform that covers the full development workflow rather than just the code editor step. It is open source which matters for teams with security or compliance requirements. The technical walkthrough that convinced me to properly evaluate it rather than just read about it is at https://www.youtube.com/watch?v=k4eSitgTKSc and it covers the autonomous operation and codebase context features with real examples.
♥ 1 💬 3 👁 6 View 3 replies →
privacy_dev · Refact.ai AI, Coding and Development

What makes Refact.ai different from Copilot for a developer who cares about privacy?

I work on client projects that involve sensitive codebases and I have always been uncomfortable with the idea of sending proprietary code to a third-party cloud service for AI completions. GitHub Copilot is useful but the fact that code snippets are sent to Microsoft's servers is a hard no for some of the clients I work with, and it is a blocker to adopting AI coding tools more broadly on those projects. [Refact.ai](http://Refact.ai) has come up as an option that can be self-hosted, which would address the privacy concern entirely. But I want to make sure that the trade-off in terms of completion quality is not so large that the privacy benefit is not worth it. I have seen self-hosted AI coding tools before and some of them have been significantly worse than the cloud alternatives to the point where they create more friction than they remove. Has anyone run [Refact.ai](http://Refact.ai) on their own infrastructure and found the completion quality acceptable for professional development work? I want to know what the hardware requirements look like for a small team of developers, how the setup and maintenance compares in complexity to just using a cloud service, and whether the fine-tuning feature that lets you train on your own codebase is practical enough to be worth the effort for a team of our size.
♥ 1 💬 0 👁 3 Reply →
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Refact.ai Showcase

4 items
Refact.ai ranked number one open-source on SWE-bench verified with 352 of 500 real issues resolved autonomously

Refact.ai ranked number one open-source on SWE-bench verified with 352 of 500 real issues resolved autonomously

snorra_builds
The shift to autonomous AI coding agents in 2026 is real and this video explains what actually changed

The shift to autonomous AI coding agents in 2026 is real and this video explains what actually changed

haukur_writ
AI code review becoming a dedicated category in 2026 is the context that makes Refact.ai's testing tools relevant

AI code review becoming a dedicated category in 2026 is the context that makes Refact.ai's testing tools relevant

logn_writes
Refact.ai ranked number one on SWE-bench and I wanted to understand why so I tested it properly

Refact.ai ranked number one on SWE-bench and I wanted to understand why so I tested it properly

OpenSourceDevOps_Finn
👍 👎

Refact.ai Pros & Cons

Privacy & Deployment

👍 Pro

Excellent self-hosting and on-prem options give full control over code and models; strong privacy guarantees with no training on user code by default.

👎 Con

Self-hosting requires significant setup effort, server management, and ongoing maintenance.

Feature Set

👍 Pro

Solid code completions, chat, refactoring, and basic agentic capabilities; good support for local and custom LLMs.

👎 Con

Some advanced features (deeper agentic workflows, certain enterprise tools) are still maturing compared to larger competitors.

IDE Support

👍 Pro

Works well in VS Code and JetBrains; responsive inline suggestions.

👎 Con

Narrower IDE support compared to tools like GitHub Copilot or Codeium.

Enterprise & Governance

👍 Pro

Custom rules, usage analytics, and governance tools for team consistency.

👎 Con

Full enterprise features require higher-tier plans or custom quotes.

Pricing

👍 Pro

Free tier available; competitive pricing for privacy-focused features.

👎 Con

Self-hosted infrastructure costs are separate and can be significant.

Discuss Refact.ai

Refact.ai is a privacy-first AI coding assistant that provides code completions, chat, and agentic features with full self-hosting and on-prem options. It is well-suited for developers and teams that prioritize data security and control while still wanting powerful AI assistance.

Join the conversation below to share your experience, ask questions, post reviews, suggest new features or integrations, or discover similar AI coding tools. All feedback is welcome.

Refact.ai — Frequently Asked Questions

How does Refact.ai work?

It provides inline completions and chat by analyzing code context, with the option to run everything on your own infrastructure.

Does Refact.ai support self-hosting?

Yes — it offers full on-prem and air-gapped deployment options.

Is my code used for training?

No — Refact.ai is designed with strong privacy guarantees; code is not used for training unless you explicitly allow it.

Which IDEs are supported?

VS Code and JetBrains IDEs are the primary supported environments.

Is there a free tier?

Yes — a free tier is available for individual use with limited capabilities.

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Sources & References

  1. Official Refact.ai website ↗
  2. Refact.ai pricing page ↗
  3. Refact.ai documentation ↗

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