Inferweave is the orchestration layer that sits between your application and the models it depends on — routing each request to the right backend, failing over automatically, and giving you one place to see cost, latency, and reliability across every provider and every piece of hardware.
# 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 )
Most teams don't have a model problem — they have a coordination problem. Inferweave gives you the routing, resilience, and visibility that turn a pile of API keys into a dependable system.
Send each request to the backend that fits — cheapest, fastest, or highest quality — using policies you define, not hard-coded model names scattered through your code.
When a provider degrades, rate-limits, or goes dark, Inferweave retries against a healthy alternative in-flight — so a single outage upstream never becomes your outage.
Every request is traced with the model used, tokens spent, latency, and outcome. See spend and reliability per team, per feature, and per model in one dashboard.
Inferweave orchestrates providers using your own credentials and contracts. Your keys stay in your account; we route and observe, we don't resell tokens.
Set spend caps and rate limits per key, per environment, and per model. Fall back to a cheaper backend automatically when a budget threshold is reached.
Recognize repeat and near-repeat prompts and serve them from cache, cutting cost and latency on the traffic that doesn't need a fresh generation every time.
Inferweave speaks the OpenAI-compatible API, so adopting it is a base-URL change — not a migration.
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.