Operations | Monitoring | ITSM | DevOps | Cloud

At VictoriaMetrics, we built vlagent as a high-performance log collector for VictoriaLogs. To validate its performance and correctness under a real production-like load, we developed a benchmark suite and ran it against 8 popular log collectors. This post covers the methodology, throughput results, resource usage, and delivery correctness. Collectors under the test: We’ve made all benchmark configurations and source code public, so you can reproduce and verify the results independently.

How platform and SRE teams are using Mezmo's open-core agent framework — with any LLM, any tools, any observability backend.

I’ve been staring at Claude Code telemetry for the past few weeks, and I keep noticing the same thing: most teams drop it into their environment, say “it’s amazing,” and have absolutely no idea what it’s actually doing at the system level. That’s fine for a personal dev tool. It’s not fine when you’ve rolled it out to 50 engineers.

RSAC 2026 Conference is right around the corner, and we're unveiling new security capabilities at Booth N-5285. Book a 15-minute slot if you want an in-person walkthrough!

If you’ve worked with an observability pipeline, you’ve probably experienced schema problems: a field disappears, a type shifts from string to number, or a new label quietly appears. The causes are everywhere. Different teams adopt different naming conventions. A dependency upgrade changes the shape of a library’s log output. Over time, these small, reasonable decisions compound into schema sprawl: dashboards break, alerts misfire, and teams scramble to find out what happened.

TL;DR: Autonomous IT platforms combine observability data and AI to automatically detect, diagnose, and resolve issues—shifting operations from reactive monitoring to predictive, self-healing systems.

The world is generating data at a pace that defies the human ability to draw insights and comprehend. By 2028, we’ll reach almost 400 zettabytes of global data—with over 55% of it coming from machines talking to machines. For enterprises, this isn’t just a storage problem; it’s an existential challenge.

Seamlessly vectorize high-volume data and accelerate your time to production with the new gold standard for GPU-accelerated vector search.