SIVARO
The pattern behind most AI infrastructure bills.
Idle GPUs on the invoice
Capacity provisioned for peak and paid for at 3 AM. Utilisation has never been charted next to spend.
A model version nobody can name
What is serving traffic right now, and which dataset trained it? If that takes a Slack thread, it is a risk.
Runaway loops
A tool calling loop or retry storm burns five figures before anybody sees the graph.
Deploys that need a hero
Every release depends on one engineer being awake, because rollback is a manual sequence of steps.
What we actually do here.
Model versioning, traffic split testing, automated rollback, GPU autoscaling matched to request load, and per query cost tracking so finance conversations have numbers.
See the whole stackChart utilisation against spend
The two lines on one graph. That picture usually ends the debate about what to fix first.
Make the platform boring
Helm, Terraform, versioned model artefacts. Every environment reproducible, every change with an audit trail.
Tune the serving layer
vLLM or TensorRT for throughput, semantic caching, rate limits and batch sizes fitted to your traffic shape.
Automate the safety net
Canary traffic, drift and cost anomaly alerts, and rollback that triggers on metrics rather than intuition.