Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes cost overruns usually come from small, invisible scheduling decisions—not the platform itself. Over-provisioned requests, poor bin packing, and fragmented node pools quietly waste cloud spend. Cost-aware scheduling, right-sizing, and smarter node selection can deliver major savings without hurting performance. Treat cost as a first-class metric with visibility into why scaling decisions happen—not just when.

The rapid adoption of AI is fundamentally reshaping the software development landscape, driving an unprecedented surge in code generation speed. However, this acceleration has created a significant challenge for security teams: the AI velocity paradox.

Harness AutoStopping helps FinOps teams eliminate up to 70% of idle cloud spend through intelligent, policy-driven automation. By automatically stopping and restarting unused resources without disrupting developers, organizations move from reactive cost reporting to continuous, proactive cloud cost optimization.

AI that understands human insight and connects it to the changes that drive real incidents. At Harness, our story has always been about change — helping teams ship faster, deploy safer, and control the blast radius of every modification to production. Deployments, feature flags, pipelines, and governance are all expressions of how organizations evolve their software. Today, the pace of change is accelerating.

Harness has been recognized by TechStrong Group for its comprehensive, AI-native platform vision, winning Best End-to-End DevOps Platform, Best Platform Engineering Solution, and DevOps Industry Leader of the Year. At Harness, our mission has always been simple but ambitious: to enable every software engineering team in the world to deliver code reliably, efficiently, and quickly to their users, just like the world’s leading tech companies.

Integrating feature flag context into OpenTelemetry traces enhances observability by recording flag states as span attributes, making it easier to analyze how specific flags influence application behavior. When you toggle a feature flag, you're changing the behavior of your application; sometimes, in subtle ways that are hard to detect through logs or metrics alone. By adding feature flag attributes directly to spans, you can make these changes observable at the trace level.

This guide covers battle-tested chaos experiments for Kubernetes, AWS, Azure, and GCP to help you validate production resilience before real failures happen. Start with low blast radius experiments (pod-level) and gradually progress to higher impact scenarios (node/zone failures), always defining clear hypotheses and using probes to measure results. Building reliable distributed systems isn't just about writing good code. It's about understanding how your systems behave when things go wrong.

Have you ever asked yourself, what is the fastest way to turn a harmless Infrastructure as Code change into a production incident and an awkward postmortem? We did, and found that usually, it's from letting it through without any guardrails. Infrastructure guardrails in Infrastructure as Code (IaC) were once a nice-to-have. Today, they’re essential. Without clear boundaries and safety mechanisms, even well-designed IaC workflows can turn small mistakes into fast-moving, high-impact problems.

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

Harness FME continues its investment in OpenFeature, building on our early support and adoption of the CNCF standard since 2022. Evaluate flags consistently across languages and environments, and integrate them seamlessly into your applications without modifying your code. Feature flags are table stakes for modern software development. They allow teams to ship features safely, test new functionality, and iterate quickly, all without re-deploying their applications.