Operations | Monitoring | ITSM | DevOps | Cloud

If you’re struggling to contain cloud costs in this suddenly volatile AI-fixated environment, it might be time to consider FinOps as an exercise in granular allocation and unit economics, with a focus on outcome.

When a payment service runs across 12 pods — each serving different customer segments — and an authentication layer spans three namespaces, performance issues can originate in both the application code and the orchestration layer. The challenge is linking request-level performance data with what’s happening inside the cluster: container CPU limits, pod scheduling decisions, and node-level events.

We’re excited to announce step metrics – a new capability coming to Bitbucket Pipelines to help you better manage and optimise your CI/CD workflows. Ever wondered what’s happening under the hood during your pipeline runs? Step metrics provide a window into the resource usage of your build and service containers. More specifically, step metrics let you monitor CPU and memory usage for each build and service container in your pipeline steps.

Elasticsearch is a distributed search and analytics engine that powers everything from log management platforms to e-commerce search bars. It excels at indexing and retrieving large volumes of data quickly, but like any complex system it can slow down under heavy load or inefficient queries.

Fall is here again! These headlines might not be as good as a maple-sugar doughnut and a pumpkin-spiced coffee, but they’re excellent news for FinOps practitioners nonetheless (and they won’t put you into a sugar coma).

We’ve seen the same story play out time and time again in numerous SaaS companies: The problem often begins when engineers — with a technical understanding of cloud costs and a deep understanding of how to build robust products — struggle to communicate the actual business impact of their efforts to company leaders.