Operations | Monitoring | ITSM | DevOps | Cloud

We shipped 62 features in June: roughly one every twelve hours! That pace isn't an accident. It's what happens when AI is writing more of the code, generating more of the tests, and clearing more of the review queue, and the rest of the delivery pipeline has to keep up.

This blog was released off the back of our white paper, The Cost of Cloud 2025 - India Edition. Research was conducted with over 600 Indian tech professionals to create a comprehensive analysis of the Indian cloud market.

Learn how F5 BIG-IP VE simplifies multicloud application delivery, security, and traffic management with MVE. As enterprise applications continue spreading across multiple clouds, the application edge is changing. A few years ago, application delivery was usually tied to a physical appliance sitting in a data center; today, applications are everywhere.

A customer emailed on a Tuesday: checkout hung for ten seconds. I opened our tracing tool, punched in the time window, and got nothing. The trace was sampled out. We keep 1% of traces, like most shops with real traffic do. The one request that actually mattered was in the 99% we threw away. I spent twenty minutes admiring our observability stack before admitting it couldn’t answer a first-grader’s question: what happened to this person? Here’s what I know now.

Harness AI Security provides a unified control plane for AI discovery, risk visibility, and runtime protection, helping organizations operationalize key requirements of the EU AI Act. Instead of relying on manual audits or fragmented tooling, teams get continuous insight into how AI systems are built, exposed, and used, along with the evidence needed to demonstrate compliance.

Every detection rule, every threat hunt, every AI agent you deploy rests on one silent assumption: that the data describing an attack actually reached your tools. When it doesn’t, nothing above it can save you, and no one gets an alert that the data was missing. Security teams invest heavily in the sharp end of the stack: detection content, threat intelligence, response playbooks, and increasingly, AI agents to triage and investigate at machine speed.

If you've shipped software in fintech, healthcare, or government, you probably know the specific dread of an upcoming compliance audit. Not because the software isn't secure, but because proving it is requires reconstructing a paper trail for decisions that were made in Jira tickets, Slack threads, and pull request comments over the last six months. The software is fine. The documentation of the software is the problem.