From training run to token stream, closer to every user
Rent high-throughput GPU clusters by the hour, serve streaming inference endpoints from the edge and distribute model weights worldwide so the first token arrives before users notice.
What AI & Machine Learning teams tell us keeps them up at night
GPUs sit idle
Reserved clusters are expensive and often half empty between experiments.
Inference feels slow far away
Users far from a single inference region wait for every token to cross an ocean.
Weights are huge
Moving multi-gigabyte models to new regions takes too long to react to demand.
How AI & Machine Learning workloads run on Cirrolane
A proven layout our solutions architects deploy with teams in this sector. Every layer is isolated, observable and replicated across regions by default.
Training grid
On-demand GPU clusters with fast interconnect and shared checkpoints.
Model registry
Versioned weights with signed provenance and staged promotion.
Edge weight cache
Weights pre-positioned near users based on live demand.
Streaming endpoints
Token streaming over persistent connections with per-tenant quotas.
Capabilities that matter in your sector
Per-second GPU billing
Pay only while jobs run, down to the second.
Streaming inference
Low-jitter token streams from the nearest location.
Weight pre-hydration
Popular models are warm in the regions that need them.
Vector storage
Managed embeddings storage replicated beside your endpoints.
Private by default
Your prompts and outputs never train anyone else's model.
Usage analytics
Track tokens, latency and cost per model and per customer.
CogniEdge AI
“Our users type code at human speed; Cirrolane ensures our AI models respond before their fingers leave the keyboard.”
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