Operations | Monitoring | ITSM | DevOps | Cloud

Cardinality problems usually begin with a small change that looks harmless: you add a label, and suddenly one metric turns into thousands of unique series. Cardinality explosions are often caught only after performance degrades. And at that point, your observability stack may be degraded and painful to troubleshoot. vmestimator is a new project specifically designed to follow cardinality trends in real time and send you alerts before they turn into a real problem.

In the first half of 2026, we shipped a wide range of improvements across metrics, logs, traces, cloud, and the Kubernetes operator. Our main focus across open-source components and enterprise solutions was on performance, stability, and making observability easier to adopt and operate day‑to‑day. This roundup brings together the most important changes to date, including a quick look back at key anomaly detection improvements from 2025 that are now paying off today.

Many “LLM observability with OpenTelemetry” tutorials stop at a single chat.completions span. That works for a demo, but it leaves gaps once an agent fans out into 30 tool calls, two vector-DB queries, three handoffs, and a 90-second tail latency you need to attribute. This post wires the OpenLIT SDK (50+ instrumentations, OTel GenAI semantic conventions, one line of code) into the full VictoriaMetrics observability stack and shows query examples that turn agent telemetry into decisions.

If you run VictoriaLogs, your day-to-day comes down to three things: sending logs, querying them, and setting retention so the disk does not fill up. Everything else happens quietly on disk.