Operations | Monitoring | ITSM | DevOps | Cloud

In this episode of FinOps on Azure, Michael Stephenson sits down with Frank Contrepois, independent FinOps voice and co-host of The FinOps Guys podcast — to explore what it really means to manage cloud costs from a business-first perspective. Frank has been in the FinOps space for nearly a decade and brings a genuinely different angle to the conversation. His background in commodity trading at Strategic Blue (a Morgan Stanley spinoff) shaped how he thinks about reserved instances, commitment strategies, and why most teams approach cost management the wrong way round.

Understand the real difference between observability and monitoring — and why modern IT teams in 2026 need both. Monitoring tells you something is broken; observability explains why. See real examples, faster troubleshooting workflows, and how Motadata ObserveOps unifies both in one platform. Don’t forget to like, share, and subscribe for more IT insights.

In this episode of Masters of Data, we get into the messier side of AI adoption, tackling questions like who actually owns the output when AI gets it wrong, and whether chasing efficiency is making us forget what it means to be human in the first place. We discuss tech CEOs proudly announcing they no longer think for themselves and debate whether AI is quietly eroding our critical thinking skills. We make the case that purpose-built, narrow AI is genuinely exciting, but that no efficiency gain is worth losing the human touch that makes work, connection, and creativity meaningful.

Cheryl Nomanson, SolarWinds staff technical trainer, provides a comprehensive overview of SolarWinds architecture and scaling options for self-hosted deployments. She explains the centralized deployment model starting with a single SolarWinds server that handles polling, web console, and database connections. The presentation covers key scaling indicators including polling thresholds that warn users at 85% capacity and alert at 100%. She demonstrates how to add up to 100 polling engines per server and additional web servers to handle more concurrent users.

Want to start chaos engineering? Don't randomly break stuff and hope for the best. Real chaos engineering starts with defining your system's steady state metrics like latency, throughput, and error rates. Then you form a clear hypothesis about what should happen when failures occur. Next, you inject controlled failures, starting small with single pod kills or network drops, not production meltdowns. Finally, you limit the blast radius by running experiments in safe environments first.

April was a big month at Harness. AI is changing how code gets written — and the rest of the SDLC is catching up. In this update, Dewan Ahmed walks through Harness product releases across three themes: AI in the developer workflow, security and governance for AI assets, and self-service maturity for developers and platform teams. What's covered (with timestamps): Found this useful? Subscribe for monthly product updates, and drop a comment telling us which release you want a deep dive on next.

OnPage Unveils Next-Generation Enterprise Web Management Console for its Critical Communications and Collaboration Platform.

Checkout the latest version of Graylog!

What's the most debated DORA metric? Nathen H from Google's DORA team breaks down the change lead time debate — and why even the experts can't fully agree on when a change is "committed." Is it at commit? After merge? The answer matters more than you think. Subscribe for more DevEx and DORA insights from our Web Summit series.

Most AI tools recommend the next step. BigPanda's AI specialists take it — autonomously, inside ServiceNow and Now Assist. Watch how BigPanda and ServiceNow work together to detect, triage, investigate, and resolve incidents end-to-end.