stepscale
New ECS autoscaling best practices that actually work

Autoscaling that tunes itself

stepscale AI learns your workload patterns and continuously optimizes the scaling parameters your AWS ECS or Kubernetes autoscaler uses. Lower cloud spend, faster scale-up, zero manual threshold guessing.

AWS ECS + Kubernetes
No infra rewrite
Deploy in < 30 min
stepscale.ai/services/checkout-api tuning

$ stepscale analyze checkout-api

→ Collected 72h of metrics

→ Detected weekday burst pattern (8-10am UTC)

→ Detected idle window (00-06 UTC)

Recommendation apply
  min_tasks:        10  3
  max_tasks:        40  60
  scale_up_thresh:  70% 55%
  cooldown_in:      60s 300s

Spend

-38%

p99 latency

-22%

Pages

-65%

Illustrative output. Real numbers depend on your workload.

Works with the stack you already run

30-50%

typical ECS spend reduction

On workloads with static configs that haven't been re-tuned in 6+ months.

< 1 min

tuning loop latency

From metric collection to new config applied to your autoscaler.

0

runtime dependencies in your traffic path

stepscale AI runs out-of-band. If we go down, your scaler keeps running.

Capabilities

Stop tuning autoscaling configs by hand

Static thresholds quietly cost you money and slow your response to traffic. stepscale AI learns your workload and keeps the configuration current.

Auto-Tuning

Thresholds, min/max task counts, and scaling ratios adjusted continuously from real workload data. Stop guessing.

Anomaly Detection

Daily 9am peak versus 3am incident - different signals, different scaling strategies. Alert only when it matters.

Cost Insights

Concrete reports on over-provisioning, before/after comparisons, and dollar-quantified savings you can show finance.

Multi-Platform

First-class support for AWS ECS (target tracking, step scaling) and Kubernetes (HPA, KEDA). No lock-in.

Low Overhead

AI runs periodically, not on every scaling cycle. Negligible compute footprint, zero extra runtime in the traffic path.

Production-Tested

Built by engineers running ECS and Kubernetes autoscaling at scale across SaaS, fintech, and ad-tech workloads.

Before / After

Static config vs AI-tuned config

Same workload, same day. Static config holds 18 tasks all night and lags the morning peak. AI-tuned config drops to 3 overnight, pre-warms before the spike, scales tightly to actual demand.

Static config
stepscale AI
Task count, 24-hour view
00:0004:0008:0008:3009:0012:0018:0022:00

Idle hours waste

15 tasks × 8h saved

Peak readiness

Pre-warmed 30 min early

Total cost delta

~38% lower this day

Talk to us

See it on your own workload

A 30-minute walkthrough with our engineering team. Bring a real ECS or Kubernetes service. Leave with a concrete cost-savings estimate. No sales pitch.