Elastic capacity and concurrency

Scale on demand, not on guesswork

Apex manages runtime capacity behind the deployment interface. Teams set application-level limits and observe concurrency, latency, and error behavior rather than provisioning servers for a peak they may never see.

Concurrency is an application decision

• CPU-bound endpoints should use tighter concurrency to preserve response time.
• I/O-heavy handlers can often handle more concurrent requests when upstream services are healthy.
• Cold-path work should be moved out of request execution when it routinely blocks user-facing traffic.

Protect downstream services

Elastic edge capacity can move pressure into your database or API provider. Use connection limits, request budgets, caching, and backpressure so scaling the application does not accidentally overwhelm a dependency.

Operational signals

Watch concurrency, queue depth, p95 latency, upstream timing, and error rate together. A rising request rate with stable latency is a different event from stable traffic with growing dependency latency.