Operations | Monitoring | ITSM | DevOps | Cloud

In a modern DevOps environment, having just metrics or just logs is like trying to navigate with half a map because you’re missing important context that makes decisions faster and smarter. Metrics tell you what is happening (CPU spikes, request rates, failed logins) but logs tell you why it’s happening, with the timestamps to prove it.

A practical guide for IT professionals, DevOps, security teams, platform engineers, and anyone who’s dealing with logs. In contemporary distributed systems, telemetry data—logs, metrics, traces, and events—serves as the primary mechanism for understanding internal system behavior. However, as system complexity increases, so does the volume and heterogeneity of telemetry.

Rural America is experiencing a dramatic surge in network demand driven by high-bandwidth applications like 4K video streaming, real-time sports content, and AI workloads. As broadband competition and digital transformation accelerate, service providers must rethink middle-mile network architecture to be scalable, technology-agnostic, and service-aware.

As colocation demand surges, space is becoming increasingly scarce and costly. According to CBRE, the average asking rate in primary wholesale colocation markets for a 250–500 kW requirement has climbed 12.6% year-over-year to a record $184.06 per kW/month, while vacancy rates have dropped to a record-low 1.9%. With vacancy rates low and power costs rising, doing more with less in your data center is essential.

For the last decade, Gremlin has helped Fortune 500 organizations with critical uptime requirements proactively uncover reliability risks and prevent costly outages. We started with Chaos Engineering, then built Reliability Management to help teams standardize and scale their testing efforts. Today, we take another leap forward with the release of Reliability Intelligence. Reliability Intelligence draws on Gremlin expertise with each test to show you what happened and recommend remediation.

The release of OpenAI’s new open-source model, GPT-OOS, has sparked a wave of excitement across the AI community. And rightly so. For organizations that want the benefits of generative AI without sending data out to the web, this is a compelling option. Running locally, GPT-OOS offers a level of privacy, control, and cost-efficiency that’s hard to ignore. It’s fast, lean and at least in its early benchmarks, surprisingly capable in coding, math, and STEM-heavy workloads.