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to11 is the AI engineering platform for teams shipping LLM features to production. Route your model traffic, keep it secure, and see exactly what your application is doing — on one shared data plane, instead of a stack of disconnected tools. The name is a This Is Spinal Tap reference. The product is the joke: everything goes to eleven.

Quickstart

Send your first traced request.

Connect your SDK

Point your OpenAI, Anthropic, or Vercel AI SDK at to11.

Deploy the gateway

Route and secure your model traffic.

Observe production

Explore your request traces.

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The three pillars

Everything in to11 sits on one gateway and one trace store, so the same request you route is the request you secure and the request you observe.

Gateway

Send requests in OpenAI or Anthropic format. to11 routes to any upstream provider and responds in the caller’s native format — with under a millisecond of added overhead and zero-copy streaming on the fast path.

Security

PII detection and blocklist filtering run inline, before a request ever leaves the gateway. ML-based prompt-injection detection is coming soon.

Observability

Every call is traced end to end — latency, token counts, and cost per request — and lands in the dashboard with no extra instrumentation.

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Explore the docs

Learn

Core concepts, instrumentation, routing, and observability — start to finish.

Integrations

Point the OpenAI, Anthropic, or Vercel AI SDK at to11 with a few lines of config.

Reference

The HTTP API, SDK reference, providers, and error handling.

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How it’s organized

to11 nests everything into three levels — Organization → Workspace → Project. Access, settings, and API keys hang off them. See Core Concepts.