Nimbus deploys your models to a global fleet of accelerators with sub-second cold starts, automatic scaling, and billing measured in milliseconds.
A single API from your first prototype to a billion requests a month. Bring a checkpoint, pick a region, and ship.
Snapshots are kept warm in regional memory pools, so the first token of a cold replica lands in under 700 ms — no pre-warming schedule to babysit.
Requests are routed to the lowest-latency healthy region automatically. Failover between zones happens in a single request lifecycle.
You pay for compute time actually consumed, measured to the millisecond. Idle capacity between requests is on us, not on your invoice.
One-click deployments for hundreds of open-weight models, or upload your own artifacts — weights, tokenizer, and config versioned together.
SOC 2 Type II, GDPR-ready data residency, and per-tenant encryption keys. Audit logs stream to your SIEM in real time.
Per-request traces, token-level latency breakdowns, and drift dashboards — exported via OpenTelemetry to wherever you already look.
Drop-in OpenAI-compatible endpoints for chat, embeddings, and vision. Or use the native SDK with streaming, tool calling, and structured outputs.
# install pip install nimbus-sdk # serve any open model in one call from nimbus import Client client = Client(api_key="nm_live_•••") stream = client.chat.completions.create( model="nimbus/nemotron-8b", messages=[{"role": "user", "content": "Explain KV caching."}], stream=True, region="auto", # lowest-latency healthy region ) for chunk in stream: print(chunk.choices[0].delta.content, end="")
No idle fees, no per-seat charges. Start on the free tier and scale as traffic grows.