One interface,
any provider underneath.
LiteLLM gives every model provider the same interface, so routing a request to a cheaper model, a different vendor, or a fallback during an outage is a configuration change — not a rewrite. Paired with Redis for response caching, it's how we keep inference cost from scaling 1:1 with request volume.
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The provider abstraction,
not hand-rolled.
LiteLLM is an open-source proxy that normalizes dozens of model providers behind one consistent API. It's the concrete implementation of the pattern we always recommend: a thin internal interface between your application code and whichever provider's SDK is doing the actual work, so a provider's breaking change or price shift gets absorbed in one place.
See our note on avoiding AI vendor lock-in for why this architectural choice matters more than which specific provider you start with.
Routing and caching,
not just abstraction.
The abstraction layer is also where the cost-control logic lives.
Simple, high-volume requests go to a cheaper model tier; harder requests route to a more capable one — configured once, applied automatically.
Repeat or near-duplicate requests reuse a cached response instead of paying for a fresh model call every time.
Cost per call becomes a tracked metric alongside latency and error rate in the same MLOps dashboards, not something discovered at month-end.
Real inference bills,
brought back under control.
This is the exact technique set behind a real client engagement, not a hypothetical.
Model routing and caching cut per-document inference cost roughly 70% for a fintech underwriting pipeline — a real, disclosed-placeholder engagement.
The broader service this sits inside — connecting models to your existing systems without new infrastructure.
The monitoring layer that tracks cost, latency, and error rate once routing and caching are in place.
Before you
book a call.
The questions we get asked most about LiteLLM and multi-provider routing — answered straight, no sales pitch.
What does LiteLLM actually do?
It's an open-source proxy that gives every model provider a single, consistent interface, so routing a request to a different model or provider is a configuration change rather than a rewrite of application code calling that provider's specific SDK.
How does Redis fit in?
As the cache layer behind the router. Repeat or near-duplicate requests — the same document re-submitted, the same question asked twice — reuse a cached response instead of paying for a fresh model call.
What have you shipped with this stack?
Model routing by request complexity and response caching are two of the core techniques behind our real, disclosed-placeholder case study on cutting LLM inference cost for a fintech underwriting pipeline.
Tell us what
your inference bill looks like.
Book a 30-minute call — we'll tell you honestly whether your pipeline has room to shrink its per-call cost, and by how much.