Operations | Monitoring | ITSM | DevOps | Cloud

Every quarter, the same scene plays out in boardrooms across the Fortune 500. The CEO asks: “What is the return on everything the company is spending on AI?” The CTO talks about productivity gains and developer velocity. The CFO points at a cloud bill that doubled but cannot isolate which line items are AI. The board nods politely and tables the discussion until next quarter, when the same question will produce the same non-answer. (If this sounds familiar, you are not alone. Keep reading.)

CloudZero originated as a way to make sense of your cloud costs. Costs spread across bills with billions of line items belonging to resources that might or might not have been tagged (or taggable), spun up by engineers working across teams, on different microservices, features, and products, that served a wide range of customers. Kubernetes. Multi-cloud. Check, check, check.

If you’re prepping for your first AI or MLOps interview, the hardest part usually isn’t always the hands-on element. For me, it’s the vocabulary. Interviewers sometimes lob single-word concepts at you (“what’s quantization?”) and watch how far you can carry the thread. The questions sound clear-cut, but each one is really a doorway into a bigger topic, and the interviewer is judging how cleanly you walk through it.

HAProxy 3.4 is a milestone release that significantly advances HAProxy’s legendary flexibility, performance, security, reliability, and observability. Dynamic backend management simplifies integration with modern architectures, memory efficiency improves across a broader range of workloads, native cryptographic operations at the proxy layer open new possibilities for API security architectures, and OpenTelemetry support makes HAProxy a first-class participant in distributed tracing pipelines.

One of the important offerings of the RISC-V Instruction Set Architecture (ISA) is the ability to customize and extend the base instruction set. An initial reaction to hearing this is often to worry about software portability and compatibility, since if every RISC-V CPU offers a slightly different set of instructions, software won’t be portable.

Two things shipped this week. One is for the MSPs who manage certificates on behalf of other companies. The other lets you run the shorter 45-day renewal cycle today.

Enterprise AI has crossed an inflection point. The model problem is largely covered. What remains unsolved is the operational impact: how to run AI inference and agentic processes continuously, reliably, and at a cost that doesn’t cancel out the value. Most enterprises are discovering this the hard way. GPU utilization dashboards show 80%. Actual compute efficiency is half that. Token demand is compounding at 200-500% annually as agents multiply every action into dozens of model calls.

Here’s a problem I run into constantly: something breaks in production, I can see the 500 errors in my logs, but I can’t reproduce it locally. The trace shows me the dependency graph but not the actual request that failed. This is especially painful in microservices. I was looking at a CNCF example the other day (a simple demo app, like 4 pods) and it already had so many cross-service dependencies that understanding what broke required looking at the whole system at once.