The path to useful robots runs through inference. Open-source and custom robot foundation models are improving fast, but serving them in production still means GPUs, queues, retries, and bills nobody can predict.
Serving is the constraint.
Real robots need low latency, high reliability, and a budget they cannot overrun. Training more capable models will not fix the path between a policy and the hardware it drives.
Every lane is priced at provider cost, with no markup. Agents connect through our MCP server, and robots stream actions with the same keys, in our cloud or yours.
What we do
Robot models, served.
Open-source and custom robot foundation models behind one API, with the low latency and high reliability real robots demand.
In our cloud or yours.
Run on our GPUs, or self-host the same stack in your own cloud when the data cannot leave it.
Hard spend limits.
Every request reserves its maximum cost before it runs and settles to measured usage, so no agent outspends the ceiling you set.
Every request reserves its maximum cost against your monthly limit before it runs, then settles to measured usage. API keys are shown once and stored only as digests.
We’re a small team building towards abundant labor, backed by HF0, Northside and Susa Ventures, with angels from Anthropic, OpenAI and Dyna Robotics, and part of NVIDIA Inception. If that is what you want to spend the next few years on, we would like to hear from you.