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AI cost visibility is the ability to see what AI spend is buying, by person, team, feature, customer and activity, before the bill arrives. The invoice can't provide it: it records tokens and hours, not decisions. It comes from capturing each AI event as it occurs, with business context attached, and reconciling to the bill. Take 50 million input tokens and 10 million output tokens in a month. On Claude Haiku 5.5 that is $10. On Claude Fable 5.1, the same tokens cost $1,000.

23% of finance leaders can both allocate AI spend systematically and prove its return to a board. Only 3% of them failed to secure an AI investment, against 19% of everyone else, and they spend what their peers spend: 41% plan a FY2027 increase of 20% or more, against 38%. Measuring return alone buys nothing, because companies claiming ROI they cannot allocate report the same board-proof gap as companies claiming no return at all.

Why agentic AI is creating new opportunities for established messaging technologies and making enterprise governance more important than ever.

CPU limits are one of the first guardrails most teams add to a Kubernetes manifest. Set resources.limits.cpu: 200m, and you can trust that the container will never use more than a fifth of a core…right? That’s not always the case. A CPU limit is a request, not a restriction: the kubelet can ignore it, and if that happens, the container is free to use as much CPU time as it wants.

TL;DR This post is for architects who want to add tracing to Aiven for OpenSearch and need to know what it takes to run the pipeline. Aiven for OpenSearch includes Trace Analytics in OpenSearch Dashboards, so you can follow a request across services, see which services call which, and open the logs a slow span wrote. OpenTelemetry collects the traces, and Data Prepper writes them to the span and service map indexes that Trace Analytics reads.

We recently announced the launch of Zephyr 26.04 LTS, bringing up to 15 years of security maintenance per LTS and enterprise lifecycle management to microcontroller-grade devices. While central high-performance computer ECUs capture most of the industry spotlight in software-defined vehicle (SDV) architectures, the operational reality of modern vehicles is far more distributed.

Starlette is the ASGI layer under FastAPI, so a Starlette release is a FastAPI release whether or not your requirements.txt mentions it by name. 1.10.0 is mostly performance and correctness, with a few behavior changes worth reading before you bump.

AI agents are becoming part of everyday engineering workflows, helping teams write code, troubleshoot issues, and automate tasks. But to be truly useful in production, these agents need access to more than code. They also need observability data and context to understand what's happening across your systems, investigate problems, and help you take action.

If ECS works for your team, no single limit forces you off it. Growing teams still hit a few hard ECS and Fargate quotas that AWS won't raise, starting with 100 target groups per load balancer for preview environments. Here are the limits, the numbers, and how to add EKS for previews without moving production. Guillaume is a Staff Solutions Architect at Qovery. He helps customers troubleshoot production infrastructure and writes about Kubernetes operations, AI agents, and reliable cloud platforms.