For teams building AI products
Your AI product shouldn't own the infrastructure around AI.
INFRO sits between your application and the AI providers. Route requests, understand every call and every dollar, and add operational control without rebuilding your product around each provider.
your applicationINFROthe model providers you already use
Start with one API and add control as your AI workload grows. INFRO is in early access — the console and the docs are public today, and the gateway opens to early accounts first.
The infrastructure problem is the same one every time.
An agent, a video tool, an SEO product, a sales assistant — the products look nothing alike and the plumbing underneath them ends up nearly identical. Providers, routing, retries, latency, usage, cost, budgets, reliability. INFRO gives those a place to live that is not your codebase.
If you run one model from one provider and spend little, going direct is genuinely the right answer, and we would rather say so than waste your evaluation. This gets useful at the second provider, the first incident, or the first bill nobody can explain.
- An account and a key per provider, each with its own request shape
- Retries, timeouts, and whatever you did about rate limits
- Per-model cost arithmetic, and a spreadsheet that goes stale
- Logging you added after the first incident nobody could explain
None of it ships a feature, and all of it is yours to maintain.
What you get for a base URL.
Four things, and each one is work you would otherwise do yourself.
One integration, not one per provider
At launch — Committed to the first release. In build now, not usable yet.Text speaks the OpenAI request shape, so an existing client points at a new base URL. Image, video, and audio use one INFRO shape across every model instead of a different SDK each.
Provider choices stop touching your code
At launch — Committed to the first release. In build now, not usable yet.Adding a model, moving a workload, or putting a standby behind one is a change in this layer. Your application keeps asking for what it needs and does not learn who served it.
Every request is recorded with what it cost
At launch — Committed to the first release. In build now, not usable yet.Model, route, latency, tokens or units, and exact price — attributable to a project, a key, or one of your own end users. That is the number you need when someone asks about margin.
A ceiling that stops rather than emails
At launch — Committed to the first release. In build now, not usable yet.A runaway loop or a bad prompt is a bill. A spend limit per project and per key refuses requests at the cap instead of telling you afterwards.
What your AI spend actually looks like.
The console is open at dash.infro.io as a demo workspace on a generated workload. This is one screen of it — the one that answers where the money went.
As AI usage grows, infrastructure becomes part of your margin. The useful time to have this is before the number is large enough to need explaining.
Adopting it is a base URL, a key, and a model string.
If your product already calls an OpenAI-compatible API, this is the whole change. Your client library, request bodies, response parsing, and error handling are untouched.
import osfrom openai import OpenAI client = OpenAI( api_key=os.environ["OPENAI_API_KEY"],) response = client.chat.completions.create( model="gpt-5.6-terra", messages=[{"role": "user", "content": "Summarize this release."}],)import osfrom openai import OpenAI client = OpenAI( api_key=os.environ["INFRO_API_KEY"], base_url="https://api.infro.io/v1",) response = client.chat.completions.create( model="openai/gpt-5.6-terra", messages=[{"role": "user", "content": "Summarize this release."}],)Two lines. The client library, the request body, the response parsing, and your error handling are untouched — which is the point. Same application, new infrastructure layer underneath it.
Building an AI product? Put INFRO in the stack.
Connect once, and add routing, visibility, and spend control as your workload earns them.
Nothing is sold until the gateway ships. The early-access page lists what exists today and what does not.