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.