Low-latency inference.
Serve models with P99 latency you can publish. Continuous batching, speculative decoding, and autoscaling down to zero — the economics of inference finally behave like software.
Latency budgets, honored.
P99-optimized serving
Continuous batching and prefill/decode separation keep tail latency flat while throughput climbs.
Speculative decoding
Draft models and verified sampling cut time-to-first-token without touching quality.
Autoscale to zero
Traffic dropped? Replicas go to zero. Traffic spikes? Warm pools snap in ahead of the queue.
Multi-region failover
Anycast endpoints and active replica sets across regions for single-digit-failure-9 uptime.
Model-agnostic
vLLM, TensorRT-compatible stacks, or your own server — we run your engine on our metal.
Usage-based visibility
Per-endpoint tokens, cost, and error rates streamed into the observability platform.
One endpoint, three deployment styles.
Managed endpoint
Upload a model, get an API endpoint with autoscaling, canary deploys, and a dashboard. Zero ops.
Bring your engine
Deploy your own inference container on dedicated bare metal with full control over kernels and config.
Edge of the fabric
Colocate the inference cluster with your training fabric for instant weight transfers and shared data.
Ready to push the frontier?
Provision bare-metal GPUs in minutes, scale capacity on demand, and get a real engineer on call — not a ticket queue.