Service
Run AI models on your own infrastructure — no data leaves your network, no per-token bills, no vendor lock-in. Predictable costs, full compliance, and production-grade performance.
Why self-host inference?
Overview
Private inference means running AI models on hardware you control — your servers, your VPS, or your edge devices — instead of sending data to third-party API providers. The models are the same frontier and open-weight models you would use via Anthropic, OpenAI, or Google, but inference happens locally.
This is not about training models. It is about serving them. You download a pre-trained model, deploy an inference engine on your infrastructure, and make predictions via a local API — just like calling OpenAI, but every request stays inside your network boundary.
Your data
Inference runs on your hardware — no data ever leaves your network boundary
Your model
Choose any open-weight model, swap anytime, no vendor lock-in
Your infrastructure
Single binary deployment — no Python, no Node, no Docker required
General purpose
Minimalist ML framework from Hugging Face. Ideal for most self-hosted inference scenarios — small binary (~8 MB), broad model support (Llama, Mistral, Phi, Gemma, Stable Diffusion, Whisper, YOLO), GPU acceleration via CUDA and Metal, and first-class WASM support for browser or edge deployment.
Large models, single GPU
Blazing-fast LLM inference in pure Rust with zero Python dependencies. Designed for running 30B+ parameter models on single consumer GPUs. Achieves up to 197 tok/s decode for large models with aggressive KV cache compression (TurboQuant 2–4 bit) and continuous batching.
Comparison
Most teams should start with API-based inference (Claude, GPT, Gemini) — it is the fastest path to value. Self-hosting makes sense when volume, privacy, or compliance requirements outweigh the operational overhead.
API-based inference (Claude, GPT, Gemini) is the fastest way to get value from AI. You sign up, get an API key, and start making requests in minutes. The provider handles infrastructure, model updates, and scaling. Per-token pricing is ideal for low-volume, variable, or exploratory workloads where fixed infrastructure costs would be wasteful.
Self-hosted inference runs open-weight models on your own hardware. The same models you would call via API (Llama, Mistral, Qwen, Gemma, Phi) run locally with zero data leaving your network. Candle and xinfer deploy as single Rust binaries with no Python or Node dependencies — just download, configure, and serve.
Our recommendation: Start with API-based inference to validate your use case and prove ROI. Migrate to self-hosted when your volume justifies the fixed infrastructure cost, your data privacy requirements demand it, or you need offline capability. We help you plan the transition at the right time.