Operations | Monitoring | ITSM | DevOps | Cloud

This OpenAI API cost calculator (also an AI inference calculator for o3/o4-mini thinking tokens) estimates your monthly OpenAI API pricing bill from three inputs: model, request volume, and average tokens per request. Toggle between standard, batch, and cached pricing and get your number in seconds. It also shows what the same workload costs on Claude and Gemini. For the full per-model rate card, see CloudZero's OpenAI API pricing guide.

Remote and hybrid work turned virtual desktops from a niche IT choice into a core way employees get their jobs done. When a desktop lives in the data center or the cloud, every logon, click, and screen refresh depends on infrastructure the user never sees. That shift is why VDI monitoring matters: it protects the end-user experience when the desktop is no longer local. The challenge is that a single slow session can have dozens of causes—across compute, storage, network, and the broker layer.

This Spring, the Apache Kafka community released version 4.2 with a “production-ready” Share Group feature. Also known as a “Kafka queue” people were eager to see this feature because it introduced elastic consumer scaling, individual message acknowledgments, and built-in "poison pill" handling; similar to what you'd find in traditional message brokers like RabbitMQ and ActiveMQ.

Benchmarks keep showing that picking the right LLM is hard. The easy answer is "just use the most powerful one." That works, but it is pricey. A small, cheap, or local model can handle many simple requests just as well as a frontier model, for a fraction of the cost. That is what semantic routing is for. Use middleware that looks at an incoming request and decides which model should answer it.

The phrase "cloud first" dominated IT strategy for the better part of a decade. It was gospel, practically unchallengeable, and for a lot of organizations, it was the right call. But something shifted between 2024 and 2026, and it shifted fast. Bills stopped being defensible. Vendor pricing imploded. Sovereignty stopped being a compliance checkbox and became a procurement requirement.

“Deployment stress doesn’t just come from high-profile public outages. It often starts much earlier, when a fear of failure seeps into team culture.” Rob Richardson, Software Craftsman Rob certainly knows the stress and embarrassment of public deployment failures. "But overall" he reflects, "I’ve had more stress in my career from internal failures.

Gigamon COO Gareth Maclachlan on deep observability, the Elastic AI Ecosystem partnership, and AI traffic governance for security teams.

If your organisation handles the personal data of people in India, the DPDPA applies to you and compliance is a legal requirement. The Digital Personal Data Protection Act, 2023 is now backed by the DPDP Rules 2025, and the Data Protection Board of India can impose fines of up to ₹250 crore for a single contravention. The obligation your IT and security teams own most directly is security safeguards under Section 8, and it is one of the first things a regulator looks at after a breach.

When an Azure service slows down or stops responding, you often hear about it from a user before your monitoring says a word. It only gets harder as you scale: Azure now runs about a fifth of the world's cloud workloads (Statista, 2026), and every new service is one more place a failure can hide. By the end, you will have a shortlist for your stack. You will also know which tools to skip, without sitting through nine sales demos to find out.

A year ago, AI coding tools worked alongside developers—suggesting the next line, completing a function, accelerating work that a human was already doing. Today, they’re writing entire modules and services independently, producing code that no human has reviewed line by line, built from components that no single person has fully mapped. And adoption is only accelerating: According to our recent AI Resilience Survey, 84% of organizations are now using AI to write, review, or suggest code.