Scaling Kubernetes workloads on custom metrics
Datadog | The Monitor blog

Scaling Kubernetes workloads on custom metrics


Summary

The article details Karpenter, an open-source autoscaler for Kubernetes that directly manages compute resources (like EC2 instances) instead of relying on Cluster Autoscalers managing node pools. This allows for faster, more efficient scaling based on actual pod requirements, minimizing wasted capacity and reducing costs. Karpenter achieves this by provisioning right-sized instances on demand, directly registering them with Kubernetes, and terminating them when no longer needed.
Read the Original Article

This article originally appeared on Datadog | The Monitor blog.

Read Full Article on Original Site

Popular from Datadog | The Monitor blog

1
Understand session replays faster with AI summaries and smart chapters
Understand session replays faster with AI summaries and smart chapters

Datadog | The Monitor blog Apr 2, 2026 33 views

2
Datadog achieves ISO 42001 certification for responsible AI
Datadog achieves ISO 42001 certification for responsible AI

Datadog | The Monitor blog Mar 26, 2026 29 views

3
Analyzing round trip query latency
Analyzing round trip query latency

Datadog | The Monitor blog Mar 27, 2026 26 views

4
Introducing our open source AI-native SAST
Introducing our open source AI-native SAST

Datadog | The Monitor blog Apr 10, 2026 23 views

5
Introducing the Datadog Code Security MCP
Introducing the Datadog Code Security MCP

Datadog | The Monitor blog Apr 7, 2026 23 views