The unified inference cloud for production AI.
One API. Every frontier model. Every GPU cloud. Inferly routes each request to the cheapest, fastest, most accurate provider — automatically — with sub-second failover and built-in caching.
Powering inference at
Inference is the new networking layer. We built the router.
Hard-coding a single provider was fine when there were three. With 100+ open and closed models and a 10× spread in GPU prices, the routing decision is now the workload.
A complete inference control plane.
Routing, caching, evaluation, observability and governance — engineered as one system, exposed behind one OpenAI-compatible endpoint.
Policy-based model routing
Declare a latency SLO, a cost ceiling, an accuracy floor and PII constraints. Inferly picks the optimal model + provider per request and re-evaluates as the market moves.
Semantic + exact-match cache
Embedding-based deduplication catches paraphrased prompts. Cache hit rates of 30–60% are typical within two weeks of traffic.
Sub-second failover
Provider down? Quota hit? We retry on a healthy peer before the user notices.
Guaranteed structured output
JSON-schema enforcement across every provider, not just the two that support it natively.
Continuous evals
Run your golden set against every candidate model on a schedule. Regressions block deploy.
Per-request traces
Token counts, provider chosen, retry chain, latency breakdown, cost and accuracy — for every call. Exportable to your warehouse.
PII, residency, allowlists
Pin sensitive workloads to specific regions or on-prem providers. Auditable allow/deny per team, per workflow.
A one-line change to your SDK.
Inferly is OpenAI API compatible. Point your existing client at our base URL and routing, caching, evals and observability turn on instantly.
- — No proxy infrastructure to run
- — No vendor SDK to install
- — Works with OpenAI, Anthropic, LangChain, LlamaIndex, Vercel AI SDK
- — Streaming, tools, vision, embeddings — all supported
# Python from openai import OpenAI client = OpenAI( base_url="https://api.inferly.com/v1", api_key="sk_live_...", ) response = client.chat.completions.create( model="auto", # let Inferly pick messages=[{"role": "user", "content": "Summarize Q3."}], extra_body={ "inferly": { "slo_ms": 350, "max_cost_usd": 0.002, "min_accuracy": 0.92, "region": "eu-west", }, }, )
Watch a single request hit the optimal provider.
Inferly evaluates every viable provider against your policy in under 200ms, then dispatches. Every decision is traceable.
Built for production. Not for demos.
A routing layer that fails open in the wrong direction will quietly drain your budget. Inferly is engineered for the opposite.
| Inferly | Single-provider stack | DIY router | |
|---|---|---|---|
| Models supported | 100+ | 1–3 | whatever you wire |
| GPU clouds | 10+ neoclouds + on-prem | vendor only | one or two |
| Failover | sub-second, policy-aware | none | retry-on-error only |
| Semantic cache | embeddings-based | — | exact-match only |
| Continuous evals | built-in | — | DIY |
| Cost telemetry | per-request, exportable | invoice only | DIY |
| Compliance | SOC 2, HIPAA-ready, EU | varies | your problem |
Used by AI engineering teams from seed-stage to F500.
"We cut inference spend 64% in the first month. The router caught a Sonnet → Haiku swap our team had argued about for a quarter."
"The semantic cache alone paid for the platform. By month two we were running on 38% the tokens for the same product."
"We replaced an internal proxy four engineers maintained. Inferly's evals catch regressions our test suite never did."
Ship AI to production. Stop overpaying for it.
Free tier includes 1,000 routed requests per day. No credit card. Drop-in OpenAI compatibility.