Operations | Monitoring | ITSM | DevOps | Cloud

Across companies of all shapes and sizes, observability practices are maturing and getting attention at the highest levels. At the same time, cost and complexity continue to hinder efforts as teams look to emerging tools to help simplify their processes in hopes of better outcomes. With so much in flux, we went into our third annual Observability Survey hoping to get a window into the ways the community is approaching observability and where it wants it to go next.

Observability metrics provide insights into the performance, behavior, and health of applications, systems, and infrastructure — enabling observability practices, which is how a system’s internal state is understood by examining its data. As organizations continue to collect more and more data, observability metrics are a key telemetry signal for observability.

Take a drink if you hate that term. Tim O’Reilly has written a post about the huge shift AI is making with our relationship to software engineering (read: The End of Programming as we Know It). Scary title. But the content is less so. Why?

In today’s digital age, businesses and customers alike are increasingly reliant on internet connectivity for day-to-day operations, communications, and transactions. Now more than ever, organizations depend on ISPs and cloud providers to deliver critical applications and services, making uninterrupted connectivity essential for success.

You may be surprised that delivering observability is a journey and isn’t about observing everything at once — it’s about driving outcomes like proactive detection, faster troubleshooting, and aligning with business priorities. If you’ve followed this series, you’ve already taken steps to.

Last week marked the inaugural HumanX conference, a convening of leaders, technologists, policy makers, and media, all brought together to discuss the state of AI and its potential impact on the future of software, business, and society.

State, local, and education (SLED) organizations need their logs to provide clear, structured insights into system performance, user behavior, and security risks. But often, the picture becomes scattered and chaotic instead, with critical log data buried in noise and gaps that make logs difficult to interpret.