Feature Overview

Datadog Kubernetes Autoscaling continuously monitors your Datadog telemetry signals to generate intelligent resource recommendations across your Kubernetes workloads and clusters. Automate rightsizing safely with multidimensional workload scaling and pre-simulated instance type recommendations to immediately drive cloud cost savings while maintaining application performance.

Identify idle Kubernetes resources and reduce wasted spend

Identify overprovisioned and inefficient instance types
Use automated recommendations to optimize Kubernetes for cost, performance, or balance
Track cost savings impact over time to prove ROI

Automate Kubernetes rightsizing with built-in guardrails

Leverage multidimensional scaling to balance replica counts and resource requests together
Apply pre-simulated instance optimizations with automated node configuration and migration
Deploy changes in your existing workflows in-app or via GitOps

Harness rich telemetry to guarantee performance

Easily scale based on any custom query or metric available in Datadog
Power your Kubernetes autoscaling operations with observability your engineers already trust
Allocate container spend to teams, products, or services with Cloud Cost Management—even for complex or shared infrastructure