Operations | Monitoring | ITSM | DevOps | Cloud

When engineers search for "Apache ActiveMQ vs Apache Artemis," most of what they find is either a shallow feature checklist or a confident recommendation to "just migrate to Apache Artemis." Neither helps a senior architect deciding whether to stay on a stable, battle-hardened Apache ActiveMQ deployment, or a platform team evaluating both options for a new system with clear eyes.

Part 2: Key insights from a fireside chat with Chris Yates. Read part 1 here. Choosing a database monitoring vendor isn't just about features. Once you’re confident that it’s time to reassess your database monitoring strategy, the natural instinct is to start comparing products. However, it’s vital to know how to assess vendor relationships, support quality, and product innovation before you sign anything.

SREs and platform teams are overwhelmed by the effort of manually troubleshooting ever-more complex cloud-native environments. This pain is driving a breakneck adoption of AI SRE solutions that promise to automate core reliability practices, from root cause analysis to capacity planning. For teams with strong engineering talent, creating a DIY AI SRE seems like a straightforward challenge.

AI has quickly become part of the language of network observability. Many vendors across the observability landscape can describe, summarize, correlate, or explain some data or situation, leveraging basic LLM capabilities. At a distance, many of these offerings sound similar. They promise faster insight, efficient operations, and a more intelligent path through rising complexity. But the industry has reached a point where surface-level similarity is creating noise, not value.

Today Canonical announced the release of Ubuntu 26.04 LTS, codenamed “Resolute Raccoon,” available to download and install from ubuntu.com/download. Resolute Raccoon builds on the resilience-focused improvements introduced in interim releases, with TPM-backed full-disk encryption, improved support for application permission prompting, Livepatch updates for Arm– based servers, and Rust-based utilities for enhanced memory safety.

Real production data tells the story better than I can. Juraci Paixão Kröhling, a friend and fellow observability practitioner at OllyGarden, recently shared an example from an anonymized production environment: 1,830 occurrences of http.url and 23,984 occurrences of url.full in the same dataset. Both attributes describe the same thing. Both are actively being written to the same backend at the same time.

Following our 2025 updates, here we recap how VictoriaMetrics Anomaly Detection evolved in Q1 2026. Stay tuned for upcoming content on anomaly detection.

KubeCon + CloudNativeCon Europe in Amsterdam brought together about 13,500 attendees this year, the largest turnout yet. The size of the event showed just how much the cloud-native space has grown, and how central observability, platform engineering, and cost control have become. For VictoriaMetrics, this year’s event was a mix of talks, booth conversations, and a lot of direct feedback from users.

Proactive budget alerts turn cloud cost optimization into an everyday operational practice. If you are responsible for managing cloud infrastructure, you already know the pattern. Costs creep up quietly, and by the time anyone notices, it is the end of the month and you are explaining instead of preventing overruns. According to Flexera’s 2026 State of the Cloud Report, 85% of their respondents say managing cloud costs is their number one priority for the year.

Key takeaway: AI agents and RAG pipelines only reach production-grade accuracy when they are developed against byte-level clones of real production data. Without environment parity, the "repro gap" leads to inevitable AI failure.