Featherless AI

Featherless uses a proprietary GPU orchestration system that loads and unloads models in under 5 seconds. Standard GPU setups typically take 30 minutes to load a new model. This allows Featherless to keep tens of thousands of models 'warm' and accessible without dedicated per-model GPU allocation, which is the core reason its catalogue is so large.

Yes. Featherless exposes an OpenAI-compatible API endpoint, meaning any application already calling the OpenAI API can point to Featherless with minimal code changes — typically just swapping the base URL and API key. This includes popular clients like SillyTavern, LangChain, and OpenCode.

Each plan includes a concurrent unit budget. Small models (under 16B) cost 1 unit per request, larger models (under 34B) cost 2 units, and very large models (70B+) cost 4 units. A 4-unit plan can run 4 small requests, 2 mid-size requests, or 1 very large request simultaneously. Requests exceeding the budget are rejected with HTTP 429 and retried when units free up.

No. Featherless explicitly does not collect or store prompts or completions sent through its API. The platform only retains aggregate usage metadata such as token counts for service improvement. This no-log policy is confirmed in its official Privacy Policy and documented in its developer docs.

Featherless launched a managed agent runtime starting with OpenClaw, one of the fastest-growing open-source agent frameworks. It provides a secure, sandboxed container environment (powered by Daytona) with persistent storage, allowing agents to run multi-day workflows. The runtime is connected to Featherless's full 30,000+ model catalogue under flat monthly pricing with no per-token billing.