The same fleet, configured for the constraints that matter to you — latency, isolation, compliance, or cost.
Scoring, underwriting, and fraud detection run on models that have to be fast and, more importantly, private. Keep warm replicas in multiple regions so a settlement window never waits on a cold start, and peer a VPC so inference traffic never crosses the public internet.
Clinical and research workloads carry compliance obligations and heavy batch jobs. Run overnight fine-tuning on spot capacity, keep protected data inside a VPC, and hold the whole deployment to a HIPAA posture with model versioning you can trace.
Vision models on a warehouse floor follow shift schedules, not steady-state traffic. Autoscaling that tracks the schedule means GPUs spin up for the first shift and down when the line stops — and cost attribution tells you what each camera line costs to run.
Document review runs in bulk and in confidence. Overnight batches on spot capacity keep it cheap, checkpointing keeps an interruption from erasing progress, and a VPC keeps client material out of shared infrastructure. Token accounting maps usage to client billing.
Traffic is bursty — a launch, a season drop, a match. Autoscale from zero to peak and back without a manual runbook, and lean on warm pools so the first wave of players doesn't hit a cold-start wall.
Recommendation and search models serve steady, high-concurrency traffic that spikes with every promotion. Multi-region serving keeps latency down globally, and observability shows exactly where a slow request spent its time.
Tell us the workload and the constraint — latency, isolation, compliance, or cost — and we'll point you at the right setup.
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