Autonomous Kubernetes Workload Autoscaler

CloudPilot AI intelligently optimizes Kubernetes workloads in real time across any cloud or on-prem environment, using in-place pod resizing to ensure zero downtime and avoid resource waste caused by unnecessary restarts.

It continuously optimizes costs, boosts application performance, and frees platform teams from manual tuning.

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Features

Run Every Workload
Smarter, Faster, Leaner

Smart & Safe Resource Rightsizing

Automatically recommend and adjust CPU/Memory requests using historical data, real-time metrics, and safety buffers, eliminating both underutilization and overprovisioning for optimal performance and cost efficiency.

Automatically Works with Node Autoscaler

When workload requests are optimized, CloudPilot AI enables the node autoscaler to dynamically scale down node types and counts, achieving deeper cloud cost savings without manual effort.

Learn more about Node Autoscaler

Zero-Downtime Resizing

Keep applications running smoothly while adapting to real-time demand. With In-Place Pod Resizing (K8s 1.33+), CloudPilot AI adjusts CPU and memory for live pods without restarts, improving stability and eliminating resource waste.

Unified Policies, Optimized Everywhere.

Define autoscaling rules once for all workloads, with the flexibility to fine-tune individual workloads as needed, and let CloudPilot AI optimize across any cloud or on-prem, simplifying operations and freeing platform teams from tedious manual tuning.

Granular Control & Flexible Updates

Apply autoscaling policies by namespace, workload type, API version, or name. Schedule changes by time (e.g., recommend-only during the day, automate at night) and choose update modes with built-in rollback for safety.

Enterprise-Ready Stability

CloudPilot AI ensures stability with full Kubernetes QoS compatibility, while offering flexible limit policies — keep, remove, or set ratios to requests — so workloads stay stable, efficient, and resilient.

Use Case

How CloudPilot AI Workload
Autoscaler Solves Real Problems

OOM Kill Prevention

Proactively detects abnormal memory usage and adjusts allocations to prevent OOM crashes and ensure application continuity and zero downtime.

CPU Throttling Mitigation

Dynamically scales CPU resources in real time without restarts, protecting SLAs and maintaining consistent performance under heavy load.

IT Cost Optimization

Continuously analyzes historical usage and automatically rightsizes resources, maximizing efficiency and reducing waste across any Kubernetes environment.

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