Predictive, per-region, per-signal. Scale on RPS, CPU, p95, queue depth — or your own metric. Warm pools keep cold starts at zero milliseconds, and scale-to-zero on staging means you stop paying for idle.
Mix and match. Combine with weights. Per-service, per-region, per-environment. Or, if your stack is weird, emit your own metric and we'll scale on that.
Window-aware. Smooths over 30s. Scales up in <1s, down with a 90s cooldown.
For batch and CPU-bound services. Target utilization with hysteresis to prevent flap-storms.
Scale up the second user-perceived latency starts creeping. Bound by error budget burn-rate.
For job runners and Kafka consumers. Scale on lag, queue depth, or oldest-message age.
We learn your traffic shape — Black Friday, Monday standup, the 14:00 EU rush. Capacity is in place 90 seconds before the curve.
Reactive autoscalers see a spike, then chase it — and your users see 503s while pods boot. Scalable trains on 28 days of traffic per service and pre-warms the pool 90s before predicted demand.
Staging at 03:00 doesn't need 12 pods. Preview environments don't need anything. Pause when nobody's looking — wake on the first request, in ~140ms.
Median customer saves 68% on compute in the first 30 days — same throughput, same latency.
"Black Friday hit and we didn't get a single page. Capacity was just already there."
Plug your repo in, and we'll learn your traffic shape in 28 days. Median customer cuts compute spend by 68% — without a single perf regression.