Your inference bill grows faster than your revenue.
Fix the unit economics now, before the next board deck asks why gross margin slipped.
Turn your fastest-growing cost line into your most predictable one. Structurally lower inference spend that compounds as you scale.
CLōD is the cost layer for AI-native founders who are tired of watching inference eat their margin.
Smarter infrastructure, not thinner margins
Patented energy-aware routing sends every call to the lowest-cost data center in real time. You pay less per token because the infra is smarter, not because anyone is cutting corners.
Unit economics that improve with scale
Your cost per token gets better as usage grows instead of worse, so growth stops working against your gross margin.
No rewrites to adopt
OpenAI-compatible. Point your existing calls at one base URL and you are live, with nothing to re-architect.
less inference spend
Inference is roughly 20 to 23% of an AI product's spend and climbs with scale. Patented energy-aware routing sends every call to the lowest-cost data center in real time, and early deployments show up to 60% less inference spend, enough to move gross margin by several points.
"It moved inference from a scary variable cost to a number I can forecast and defend. Margin math finally works."
Marcus E.
Founder & CEO, YC-backed AI startup
How it works
Point your calls at one API key
Point your existing calls at one CLōD API key. No migration, no rewrites.
Patented energy-aware routing
OpenAI-compatible, so you keep your SDK and just change the base URL. CLōD tracks real-time electricity prices across North America and routes every call to the lowest-cost data center automatically, giving you the same output at a lower cost per token. No config on your end.
You get the controls
Per-key spend caps, per-member visibility, and zero data retained.
The same models your team already uses, routed to cost less, with the controls to prove it.
Book a demo
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