infras sits between your app and every LLM API you call — logging every token, routing every request, and cutting your AI bill, all without touching your code.
Drop-in replacement for the OpenAI SDK. One line to integrate. <2ms proxy overhead.
| Model | Route | Tokens | Latency | Cost | Status |
|---|---|---|---|---|---|
| gpt-4o | /v1/chat/agent | 2,418 | 388ms | $0.0112 | cache · 92% off |
| claude-3.7 | /v1/chat/summarize | 6,102 | 841ms | $0.0293 | 200 |
| gemini-2.5-flash | /v1/embed/batch | 48,920 | 156ms | $0.0008 | 200 |
| gpt-4o-mini | /v1/chat/classify | 312 | 204ms | $0.0002 | 200 |
| claude-3.7 | /v1/chat/agent | 9,338 | 1,204ms | $0.0441 | retried → 200 |
One gateway · every major model
infras gives your team full visibility and control over AI spend, latency, and reliability — from a single drop-in gateway.
Every request logged with tokens, cost, latency, and errors. Break down spend by feature, team, model, or customer — in real time, not on your monthly invoice.
Send each request to the cheapest model that can handle it. Simple work goes to fast small models; hard reasoning escalates automatically. Cut spend up to 40%.
Repeated and near-duplicate requests are answered instantly from cache. Users see the same quality, you pay a fraction of the tokens.
Provider outage or rate limit? Requests reroute to a healthy provider in milliseconds and retry with backoff — your users never see the 500.
Set per-team and per-feature budgets with hard caps and soft alerts. The days of a runaway loop torching your credit card are over.
Gemini-powered summaries explain your bill in plain English: what spiked, why, and exactly which routing rule would have prevented it.
infras speaks the OpenAI API, so the SDK you already use keeps working — you just change one URL.
Swap the base URL in your existing OpenAI, Anthropic, or LangChain setup. That's the whole integration.
Each request is metered, cached, routed to the best provider, and retried on failure — with ~2ms of added overhead.
The dashboard shows spend, cache hit rate, and reliability per feature and model — and suggests routing rules as it learns.
# before: client = OpenAI() from openai import OpenAI client = OpenAI( base_url="https://gateway.infras.app/v1", api_key="infras_sk_...", # your real keys live in the dashboard ) resp = client.chat.completions.create( model="gpt-4o", # or "auto" to let infras pick the cheapest capable model messages=[{"role": "user", "content": "Explain vector databases"}], )
// before: new OpenAI() import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://gateway.infras.app/v1", apiKey: "infras_sk_...", // your real keys live in the dashboard }); const resp = await client.chat.completions.create({ model: "gpt-4o", // or "auto" to let infras pick the cheapest capable model messages: [{ role: "user", content: "Explain vector databases" }], });
# Point any OpenAI-compatible request at the gateway curl https://gateway.infras.app/v1/chat/completions \ -H "Authorization: Bearer infras_sk_..." \ -H "Content-Type: application/json" \ -d '{ "model": "auto", "messages": [{"role": "user", "content": "Explain vector databases"}] }'
Start free, scale as your AI usage grows. Every plan includes the full gateway, dashboard, and caching.
For side projects and early experiments.
For teams shipping AI features in production.
For companies with compliance and scale needs.
model: "auto", infras classifies each request's complexity and routes it to the cheapest model likely to succeed — then learns from retries and feedback. Teams typically save 30–40% versus hardcoding one flagship model everywhere.Point your first request at infras in under five minutes.
Get started freeNo credit card · 500 free credits · works with your existing SDK