Inference infrastructure for AI teams
Sirvich deploys your models to the clouds and clusters you already pay for, scales them with real traffic, and shows what every request costs. Your engineers ship models instead of tending machines.
Runs on the stack you already use
Built for teams serving
Production is a different job
The model is ready.The infrastructure is not.
GPU memory at 94% on three replicas
Long prompts are queueing. Two replicas from the warm pool bring p95 back under 400 ms.
Autoscaler · 2 min ago
Scaling, handled
Sirvich watches queue depth, not CPU graphs. When traffic climbs it adds replicas from a warm pool, routes around slow nodes, and hands the GPUs back when the rush is over.
Deployment
support-agent-v3
Requests up 3.4x in six minutes
Autoscaler responds
Meet your control plane
Sirvich keeps a live map of what you serve and where it runs, so you can go from a slow request to the GPU that served it in two clicks.
How it works
Link the cloud accounts and clusters you already have. Sirvich runs inside them, with your keys.
Push a model from your registry. Sirvich picks the runtime, precision, and GPU that fit it.
Replicas follow queue depth, with a warm pool for spikes and scale to zero at night.
Every request is traced. When a rollout hurts latency, Sirvich rolls it back and tells you which check failed.
Features
1.0Autoscaling
Set a target for waiting requests. Sirvich adds and removes replicas to hold it, and keeps a warm pool so new replicas serve within seconds.
1.1Safe rollouts
Every new version starts as a canary and is compared with the live one on latency, errors, and your own evals. If one slips, it rolls back on its own.
1.2Every GPU you have
Rack 12 is full. New requests go to Azure westus3.
Pool GPUs across clouds, regions, and your own racks. When one region runs short, requests move to the next one that meets your latency target.
Cost per request
Sirvich meters GPU time per request and per team, then points out the jobs that would run as fast on cheaper hardware.
Monthly budget
$4,180of $6,000
Suggestion
Move nightly-backfill to L4 GPUs. It finishes in the same window and saves about $510 a month.
Works with your stack
Sirvich connects to the clouds, registries, and tools your team already runs, so there is nothing to migrate and nothing to rewrite.
Add integrations
Connect the places your models live and the tools your team watches.
Built for production
Models and data stay in your accounts, behind your network rules, with your keys.
Every rollout starts as a canary and rolls back on its own if latency or errors slip.
Standard containers and plain config files. Export everything and leave whenever you like.
Pricing
Every plan bills GPU time by the second. Higher plans add seats, regions, and support.