Scale to zero: stop paying for AI that's waiting

Most AI infrastructure bills are dominated by capacity that isn’t doing anything. A model endpoint provisioned “just in case,” a server kept warm for traffic that comes in bursts — you pay for the waiting, not the work.

Our default is the opposite. Agents run on Cloud Run with scale-to-zero: when nothing’s happening, there’s nothing running, and the cost approaches zero. When a request arrives, capacity appears in seconds and scales with demand.

The trade everyone worries about

The usual objection is cold starts — the first request after idle being slow. In practice, for the agentic workloads we build, the warm-up is small relative to the model call itself, and the economics are overwhelming: you stop paying for a fleet that’s mostly idle.

For workloads that genuinely need to be instant at all times, we provision a warm floor. The point isn’t zero capacity everywhere — it’s not paying for capacity you’re not using.

Why it matters for a growing business

Scale-to-zero turns infrastructure from a fixed cost into a usage cost. You can stand up a new agent, let it sit until it’s needed, and pay for it only when it earns its keep. That’s the difference between experimenting freely and rationing every deployment.