Infrastructure · Established · Advanced

Model Serving

The systems that expose a trained model as a reliable, scalable, monitored endpoint.

What Model Serving is

Serving covers request handling, batching, autoscaling, versioning, canary rollout, fallbacks and observability — the same concerns as any production service, plus GPU scheduling.

How it works

Inference servers group concurrent requests into batches, manage the KV cache, stream tokens, and route between model versions. Blue-green and canary deployments allow safe rollout and instant rollback.

Why it matters

Most AI incidents are serving incidents: a timeout, a cost spike, a silent quality regression after a version change.

Common uses

→Production LLM endpoints
→Real-time scoring APIs
→Multi-model routing gateways

Strengths

✓Predictable latency and cost with good batching

Watch for

✓GPU capacity planning is unforgiving
✓Version drift breaks prompts

Continue exploring

Inference LLMOps (2)AI Latency AI Observability