Operations | Monitoring | ITSM | DevOps | Cloud

In this session, we explore how organizations can move beyond basic cloud cost reporting to truly understand the business value of their IT investments. Using the T2Bv (Technology-to-Business Value) meta-framework alongside FinOps practices, we explain how to connect IT resources, including Azure environments, to measurable business outcomes.

Most teams treat cloud cost like something to clean up later. In reality, it is already baked into how your system behaves. Every workload has a personality. Some spike with concurrency. Some quietly run all day and never shut off. Some look efficient until scale hits and then costs accelerate. And some charge you every time they run, every query, every scan, every execution. This episode is about recognizing those patterns early. Once you understand how your architecture behaves under load and over time, you stop reacting to cost and start shaping it.

Dan breaks down the fundamental similarities and stark differences between application development and platform engineering. He shares the unexpected hurdles he faced during his transition, from complex networking and CoreDNS latency to the harsh realities exposed by chaos testing in cloud environments.

In this episode of Telemetry Talks, we explore OpenTelemetry observability signals—metrics, logs, and traces, and how VictoriaMetrics handles each of them with high performance, cost efficiency, and seamless integration. We briefly explain what each signal is, discuss common misconceptions, and share guidance on which signal to start with if you're new to observability. Together with our guests, both engineers at VictoriaMetrics, we walk through integrating VictoriaMetrics with the OpenTelemetry demo, showcase Grafana dashboards, and check the playgrounds for all three signals to see them in action.

Learn how to implement end-to-end monitoring and observability for Agno-based AI systems using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Agno workflows, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability—so you can debug faster, optimize latency, and confidently run AI systems at scale.

Is Your DevOps Strategy Ready for the AI Era? AI is accelerating modern software delivery—but it’s also raising the stakes for security, compliance, and auditability. As AI-driven change increases, many organizations are discovering that incomplete DevOps practices are creating new risk. Based on insights from 800+ global IT professionals, the 2026 State of DevOps Report reveals why vendor‑backed, enterprise‑grade DevOps platforms are becoming critical for managing AI‑driven risk and meeting evolving regulatory demands.