Watch a team scale an AI feature past the prototype stage and you’ll see the same pattern every time. Week one: one provider, one API key, hardcoded model name. Week six: three providers, quota errors, a Slack thread titled “why is Claude down?”, and a bespoke failover script that nobody wants to own.
At some point someone says the words “we should put this behind a gateway” and a six-month build begins.
LLMGrid.ai is that gateway, already built.
What a gateway actually has to do
The surface area of a real LLM gateway is not small:
- Unified API — one schema across OpenAI, Anthropic, Mistral, Google, Groq, Together, Bedrock, and local models.
- Routing — by cost, by latency, by capability, by compliance zone, by fallback chain.
- Observability — per-request tokens, cost, latency, cache hit, routing decision, full replay.
- Rate limiting — per-key, per-team, per-tenant, per-model.
- Cost attribution — chargeback to the business unit that made the call.
- Caching — semantic and exact, with invalidation you actually trust.
- Safety filters — PII, prompt injection, content policy, applied consistently.
- Key rotation — bring your own keys or use pooled keys, rotate without downtime.
Every team that scales past a handful of models ends up building this. Most build it badly because it’s not their core product.
The LiteLLM foundation
LLMGrid is built on LiteLLM — the same OSS layer many of the best AI teams already use under the hood. We add the operational skin around it: a control plane, audit logs, SSO, team/project scoping, spend dashboards, and policy rules that operators actually want to write.
Where this goes
The next frontier is semantic routing — letting the gateway decide which model is best for a given prompt based on live quality signals, not static rules. That’s what we’re building next. The era of “hardcode the model name” is ending.
One API. 100+ models. → llmgrid.ai