Inference fleet
GPU, multi-region.
GPU clusters across multiple regions with autoscaling, request routing, KV-cache reuse, and fallback policies — so customers don't see a model outage, even when one provider does.
Why inference is the new compute bottleneck
Training is bursty; inference is permanent. The economics of an AI product are dominated by per-request cost and per-request latency, and both are decisions the inference fleet makes a thousand times per second. We build the fleet so those decisions are explicit, attributable, and tunable.
Inference primitives we operate
Multi-provider routing
Anthropic, OpenAI, Google, Bedrock, self-hosted — one interface, one budget.
KV-cache reuse
Prompt prefix caching, session caching, embedding caching — across requests.
Speculative decoding
Where it pays off, latency wins of 1.5–3× with no quality loss.
Autoscaling
Per-region, per-model autoscaling with explicit cold-start budgets.
Fallback policies
Provider outage, region outage, model outage — each with a defined fallback path.
Cost attribution
Token-level accounting tagged to customer, feature, and team.