It sits between your app and every model you call — routing each request to the right backend, failing over when one degrades, and keeping cost, latency, and reliability for all of them in one place. You change one base URL; the tangle stays on our side.
Wiring up a couple of providers by hand starts fine. Then one tightens its rate limits, another has an outage in the middle of your launch, and a third quietly raises its price — and now you're maintaining glue code instead of shipping. Inferweave is the routing, resilience, and visibility that keeps that pile of keys behaving like one dependable system.
Every request goes to the backend that fits it — cheapest, fastest, or highest-quality — decided by policies you write, not model names hard-coded across a dozen files. Set spend caps and rate limits per key, per environment, per model. When a call would push you past a budget, it drops to a cheaper backend on its own instead of failing or overspending. And because all of that lives in one place, changing how you spend doesn't mean redeploying five services to do it.
A provider degrades, rate-limits, or goes dark; the request retries against a healthy alternative before it ever comes back to you as an error.
Every call is traced — model, tokens, latency, outcome — and broken down by team, feature, and model in one view, so the monthly bill stops being a surprise. Prompts that repeat (or nearly repeat) come back from cache, so you're not paying full freight for the same answer twice.
We orchestrate the providers you already pay for, with your credentials and your pricing — your keys stay in your account, and we route and observe rather than resell or mark up a single token. We'll also say the quiet part out loud: we don't host models. If everything you've connected is down at once and you never set a fallback, there's no capacity for us to conjure — we can only route to what you've actually plugged in.
Inferweave speaks the OpenAI-compatible API, so adopting it is a base-URL change — not a migration.
# Point your existing client at Inferweave. from openai import OpenAI client = OpenAI( base_url="https://api.inferweave.cloud/v1", api_key="iw_live_...", ) # One call. Inferweave picks the backend, # retries on failure, and records the trace. resp = client.chat.completions.create( model="auto", # or pin a model messages=[{"role": "user", "content": "Summarize Q3."}], policy="lowest-latency", # cost | quality | latency )
Swap your base URL to Inferweave and keep the SDKs and request shapes you already use.
Add the providers and hardware you already pay for. Inferweave discovers the models each one exposes.
Choose routing by cost, latency, or quality — globally or per request — and define budgets and fallbacks.
Traffic flows through the fabric. Watch spend, latency, and reliability update per model in real time.
Real applications don't call one model. They call several, from several providers, under changing prices and limits. Inferweave is built for exactly that.
Keep serving through a provider's rate limits or downtime by weaving a healthy backend into the same request path, transparently to your users.
Route low-stakes traffic to smaller, cheaper models and reserve premium models for the calls that matter — with budgets that enforce themselves.
Because routing is policy-driven, swapping or adding a provider is a config change — not a code change spread across every service you run.
Blend hosted APIs with your own GPU fleet under one endpoint, sending sensitive or high-volume workloads to hardware you control.
Give platform teams a single, auditable view of which models are used, by whom, and at what cost — instead of a dozen scattered dashboards.
A/B new models against production traffic behind a policy, compare quality and cost side by side, and promote the winner without a redeploy.
We designed Inferweave around a simple stance: you should own your models, your keys, and your data. We coordinate them.
Inferweave uses the provider accounts you already have. We never resell inference or mark up tokens.
An OpenAI-compatible surface means you can adopt — or leave — without rewriting your application.
Every request is measured. You never have to guess where your spend or your latency is going.
Failover and budgets aren't add-ons. They're the reason an orchestration layer exists in the first place.
Point one client at Inferweave and see your routing, cost, and reliability in a single place. We'll help you design the policies that fit your workloads.