Autoscaling should configure itself

Our approach

• AI-First. Machine learning analyzes historical workload patterns and tunes scaling configurations automatically - thresholds, min/max values, scaling ratios.
• Hybrid Architecture. AI advises, your reactive scaler executes. The intelligent tuning layer runs periodically while your existing autoscaler handles every real-time decision.
• Multi-Platform. AWS ECS and Kubernetes are first-class. No lock-in to a single orchestrator.
• Cost Transparency. Every recommendation comes with a before/after impact and a dollar number you can show finance.
• Engineering-Driven. Built by engineers who have run autoscaling at scale in production. The product solves problems we have hit ourselves.

Our team

stepscale.io brings together experts in cloud infrastructure, AI/ML, and DevOps. Our team has shipped autoscaling for high-traffic ECS and Kubernetes workloads across SaaS, fintech, and ad-tech environments.

We are a small, distributed team. We hire engineers who care about the cost and shape of production systems, not just shipping features.