Edges that capital alone can’t buy.
Proximity, speed-to-power and real estate we already control compound into a moat — the parts of an AI build-out you can’t simply spend your way past.
Real-time and agentic models that stall at 65 ms feel instant at 3 ms. We put the compute in the metro it serves — not a region away.
Sited beside the user — 10–20× closer than centralized cloud.
The building, footprint and grid tie-in already exist — not a 3–5 year greenfield.
A national footprint deployed on real estate we already control.
Every site scored on nearby voltage, distance and grid headroom.
Co-located battery storage shortens interconnection and stacks grid revenue.
Data residency and compliance solved by where the compute physically sits.
Hyperscale performance,
without the hyperscale bill.
Inference on real estate we own — with direct power, on-site storage and a 1.15 PUE — strips out the cost layers that make centralized AI expensive. The Edge passes that structural advantage straight to the customer.
lower cost per GPU-hour vs. hyperscale cloud
lower data egress — process where the data already lives
more capital-efficient per IT-megawatt than market
availability target — enterprise-grade SLAs
Figures reflect platform targets vs. published hyperscaler list pricing. Actual savings depend on workload and term.