Configurer la mise à l’échelle automatique et la concurrence

Autoscaling adds container instances when traffic rises and removes them when it falls. You serve spikes without paying for idle machines. 📈

Concurrency is how many requests one instance handles at the same time. It is the dial that decides when the platform adds more instances.

The platform divides incoming load by your concurrency to size the fleet. Lower concurrency means more instances for the same traffic.

On Cloud Run you cap requests per instance with --concurrency. Tune it to how many calls your model can serve without slowing down.

If each prediction is CPU heavy, a high concurrency starves every request. Set it low so the platform spreads load across more instances.

A traffic flood could spawn endless instances and a huge bill. A max instances ceiling protects your budget and downstream databases.

With min instances at zero, the platform removes every container when idle. You pay nothing between requests, at the cost of a cold start.

Each instance gets the CPU and memory you request. A big model needs enough memory to load, or the container crashes on startup.

When all instances hit their concurrency cap, new requests wait in a queue. Rising queue time is your signal to scale out faster.

Pick settings by measuring, not guessing. Load test rising traffic and watch latency to find the concurrency that holds your SLA.

Concurrency and instance limits work together to balance latency against cost. Tune both to serve users fast without overspending.

Let us check what concurrency really controls.

You tuned concurrency, capped max instances, right-sized resources, and load tested. Your service now scales with demand and budget. ⚖️