Operations | Monitoring | ITSM | DevOps | Cloud

Key takeaway: Hyperscaler pricing models often penalize e-commerce growth due to unpredictable egress fees and unbounded auto-scaling, but moving to a resource-based allocation model allows teams to treat infrastructure costs as a deliberate business decision rather than a post-campaign surprise. Ecommerce traffic doesn't grow linearly. It spikes, and every spike rewrites your cloud bill.

Agentic workloads thrive with precision tooling. Just like developers, they need the rich context, high cardinality, and fast feedback loops that allow them to ask exploratory open-ended questions of their code. But instrumentation is costly, and from the dawn of software, developers have tried to do the most possible with the least amount of resources.

Discover Aiven for DataHub: a fully managed, open-source data catalog that gives your teams and AI agents the context they need to find and understand data. According to an MIT study, 95% of AI projects fail to deliver value. I've been thinking about why that number is so stubbornly high, and I've come to believe the answer isn't about models,compute or even data quality in the traditional sense -It's about context.

The public cloud has revolutionized IT by making infrastructure on-demand, scalable, and self-service. However, this convenience comes at a price. In the cloud, engineers can instantly spin up resources and spend company money with the click of a button or a line of code, bypassing traditional procurement and finance approval processes.

As teams scale, managing synthetic monitoring checks manually in the UI becomes difficult and error-prone. When you're dealing with dozens of checks across multiple environments, teams experience inconsistent configurations, lack of version control, and difficulty tracking changes.

The Kubernetes Monitoring Helm chart is the easiest way to send metrics, logs, traces, and profiles from your Kubernetes clusters to Grafana Cloud (or a self-hosted Grafana stack). And version 4.0 is the biggest update the chart has ever received. Representing nearly six months of planning and development, it's designed to solve real pain points that users have hit as their monitoring setups have grown.

When you identify CPU or memory spikes in your services, it’s critical to understand why they’re happening. But switching between tools or crafting complex queries can slow you down when trying to pinpoint a root cause. This is why we’re excited to share that Profiles Drilldown, an application that lets you easily explore profiling data through an intuitive, point-and-click interface (no queries required), is now integrated with Grafana Cloud Knowledge Graph.