Operations | Monitoring | ITSM | DevOps | Cloud

It’s Black Friday. Checkout latency is spiking. Your on-call engineer pulls up the dashboard and starts working through the list. Is it a regional issue? No, all regions look fine. A payment provider? Stripe, PayPal, Apple Pay all nominal. A bad deployment? Nothing shipped in the last six hours. All your infrastructure dashboards are showing green. But customers are complaining. Checkout is slow, carts are being abandoned and revenue is draining away.

Engineers are constantly context switching between tools, adding cognitive overhead on top of already complex work. You're deep in an investigation, you need to analyze some data, pull up a runbook somewhere else, and share findings back in Slack. Context gets lost in the shuffle, correlating across data sources becomes painful, and everything just takes longer. In high-pressure situations like incidents, that friction has a real cost to the business.

Do more with less. That’s the mandate we’re all hearing. AI has fundamentally changed how we work. Modern AI workloads generate 10-100x more queries than humans ever could, pushing legacy architectures past performance limits. And the audacity of it all? Legacy logging vendors continue to raise costs without delivering meaningful innovation. IT and security teams are still forced to choose between speed and retention. Investigations are still slow. Data onboarding is still painful.

International travel comes with amazing sights, cultural experiences, and local delicacies. However, most travelers know that it comes with differing economies that impact a money’s value and various currencies. When people need cash, they have to translate the money in their wallets to the local currency, which means different coins and bills. Depending on the exchange rate, the currency’s value can change as the person moves from one country to another.

Your on-call engineer gets paged. A critical service is down. Error rates are spiking. They open Sentry for errors. Flip to Grafana for metrics. Pivot to Kibana to search logs. Then jump to Lumigo, but that only covers the Lambda functions, not the Node.js backend throwing the actual errors. Three tabs become five. Five become eight. Half the incident is gone and your team is still piecing together what happened instead of fixing it. Sound familiar?

Speed is exposing the cracks. Our research shows that 69% of heavy AI users now face frequent deployment issues. To capture the ROI of AI, leaders must shift focus from code generation to delivery modernization. standardizing foundations and automating the "manual middle" that leads to developer burnout. Over the last few years, something fundamental has changed in software development.

As tools such as AI agents become more integrated with the instrumentation, governance, and centralization of product analytics data, product managers (PMs) still own the meaning of those events and the connected outcomes. Knowing when to trust the data, forming strong hypotheses, and being able to act on the insights requires an expert in the loop.

In this series, we’ve explored Karpenter’s architecture, the key metrics that reflect its health and performance, and the vendor-agnostic tools for collecting and analyzing its telemetry data. In this final post, we’ll show you how Datadog helps you monitor and alert on Karpenter alongside your Kubernetes cluster and the infrastructure that runs it.

In the first two parts of this series, we explored how Karpenter’s architecture enables just-in-time provisioning and active node consolidation, and we identified the key Karpenter metrics you should track to keep your cluster performant and cost-efficient. In this post, we’ll look at vendor-agnostic tools you can use to capture these signals.