What Refact.ai Does
Refact.ai is the only AI coding assistant in 2026 that bundles a self-hostable inference server with a private fine-tuning pipeline. The model adapts to your codebase rather than relying purely on the generic training data that powers Copilot, Cursor, and Codeium — and crucially, it can do that without your source ever leaving your infrastructure. Refact runs as VS Code and JetBrains extensions backed by a Refact server that you can host on your own GPUs, on a managed cloud instance, or via Refact's hosted service. The combination of on-premise inference and codebase-specific tuning is what makes it a category of one.
Private Fine-Tuning: How the Model Learns Your Codebase
Refact's fine-tuning engine trains a lightweight LoRA adapter on the code you point it at. The adapter is small enough to load on top of the primary inference model at runtime, which means every completion and chat response can pull on patterns Refact learned from your repo: internal utility functions, naming conventions, idiomatic API usage, and architectural patterns that a generic model would never see. This is meaningfully different from RAG-style retrieval — the model is actually shifted toward your code, not just shown chunks of it at inference time.
The real-world impact shows up most on large, mature codebases where in-house conventions matter. Engineers consistently report higher acceptance rates on Refact completions for internal-library-heavy code, where Copilot or Codeium tend to hallucinate function names. On small or greenfield projects the value is harder to justify — there is not enough code yet for the adapter to learn anything the base model does not already know, and the operational overhead of running a fine-tuning pipeline is not free.
Deployment Options: Cloud vs Self-Hosted
The self-hosted path is Refact's headline feature and its biggest operational ask. The minimum recommended GPU is something like an RTX 3090 or 4090 with 24 GB of VRAM, or a cloud GPU instance with equivalent specs. Setup is Docker Compose-based and well-documented, but you are still the one operating an inference server: monitoring, restarts, upgrades, and capacity planning fall on your team. For organizations that already run ML infrastructure, this is normal work; for everyone else, it is the line that separates Refact from drop-in SaaS tools.
For teams without GPU infrastructure, Refact also offers a managed cloud tier and a BYOK mode that wires Refact's interface up to OpenAI, Anthropic, Mistral, or any OpenAI-compatible endpoint. BYOK preserves the privacy posture for your IDE-side context (the extension does not phone home through Refact servers) while letting you skip the GPU bill. The trade-off: BYOK disables the private fine-tuning feature that is the main reason to choose Refact in the first place. The decision tree comes down to whether your codebase is large enough and your privacy needs strict enough to justify operating the full stack.