What it sells
Distributed, hardware-agnostic AI inference
Business model breakdown
Buyer: who owns the budget?
UnverifiedEngineering/AI infrastructure lead at teams with large GPU or inference bills.
Trigger: what makes them buy now?
Hyperscaler or provider cost and uptime pain; claims 10-20% lower cost at high throughput and 2.5x faster autoscaling.
Workflow: what inputs become what output?
Call models through one API or launch dedicated GPUs; the network routes each request to the best provider by latency, cost and availability, with automatic failover.
Economics: what it saves and how it charges
Usage-based per-token billing and GPU rental; supply side recruits GPU providers (including consumer GPUs) who earn by contributing compute.
Distribution: how it reaches customers
Self-serve signup with card deposit, docs/API, plus Book a demo for dedicated capacity and SLA.
Funding history and coverage
| Round date | Stage | Amount | Investors | Source |
|---|---|---|---|---|
| — | Y Combinator Summer 2026 | — | Y Combinator | ycombinator.com |
Profiles summarize public reporting and company websites. Business-model notes are MaxBC's reading of that material, not confirmed facts; unknowns are left blank rather than guessed. Updated 2026-10-12.