Autopilot
ML-driven real-time horizontal scaling with workload-specific models that react in less than 10 seconds.
Overview
Autopilot replaces traditional Kubernetes HPA with ML-driven horizontal scaling that reacts to traffic changes in less than 10 seconds. Unlike HPA which relies on simple CPU/memory thresholds, Autopilot trains workload-specific ML models on your actual traffic patterns to make intelligent scaling decisions.
Autopilot continuously learns optimal scaling patterns for each deployment, understanding when to scale aggressively during traffic surges and when to scale down conservatively to avoid thrashing. This eliminates the manual trial-and-error of tuning HPA configurations.
Why Better Than Kubernetes HPA
| Kubernetes HPA | Wave Autopilot | |
|---|---|---|
| Reaction Time | ~30 seconds to detect and react to load changes | Less than 10 seconds using workload-specific performance models |
| Decision Model | Simple threshold-based rules (CPU > 70% → scale up) | Workload-specific ML models trained on actual traffic patterns |
| Configuration Management | Manual tuning of targets, stabilization windows, and behavior policies | One-click setup with continuous learning and auto-tuning |
Key Outcomes & Benefits
~2× faster than HPA, preventing downtime during traffic spikes
ML models eliminate over-scaling and under-scaling
Prevent downtime with fast, intelligent scaling
Interactive Speed Comparison
Click the button to simulate a traffic spike and see how each system responds
Kubernetes HPA
HPA: Monitoring
Wave Autopilot
Autopilot: Ready
~20 second advantage – Autopilot scales in under 10 seconds versus roughly 30 for HPA, preventing service degradation during traffic spikes
How It Works
1. Deploy and Learn
One-click deployment. Autopilot begins collecting traffic metrics and learning workload patterns.
2. Model Training
ML models train on your specific traffic patterns, understanding peak times, surge behaviors, and normal loads.
3. Real-Time Decisions
Autopilot makes scaling decisions in less than 10 seconds, comparing measured load against the workload's performance model.
4. Continuous Optimization
Models continuously retrain on new data, adapting to changing workload behaviors automatically.
Scale ~2× Faster with ML-Driven Autopilot
Replace HPA with intelligent scaling that prevents downtime.
Book a Demo