Operations | Monitoring | ITSM | DevOps | Cloud

Summary: Machine learning-based anomaly detection improves observability by learning normal system behavior instead of relying only on static thresholds. This article explains how vmanomaly, its MCP server, purpose-built skills, and an LLM-powered UI copilot help engineers explore telemetry, investigate anomalies, build MetricsQL queries, select suitable models, apply business constraints, and validate configurations through natural language.

Summary: The Q2 2026 development cycle moved VictoriaMetrics Anomaly Detection toward one simpler, continuously adapting workflow. The main addition is Temporal Envelope, an online model that handles trend, multiple calendar patterns, holidays, persistent changes, forecasts, and optional multivariate context without retaining the full fit history.

This quarter we’ve been busy investing a lot of effort into improving how users understand and control what they pay for VictoriaMetrics Cloud, and how to move around the console while doing it, with major UX improvements across different sections and functionalities. The big piece of work is transparent credits reporting, giving prepaid customers full visibility into balance, consumption, and expiration.

This post is a joint effort by the OpenLIT and VictoriaMetrics teams. OpenLIT brings the OTel-native GPU collector for NVIDIA, AMD, and Intel hardware, while VictoriaMetrics provides the storage and query layer for the resulting metrics. We wrote it together to show how the two projects fit into a single, self-hosted observability pipeline, and to share the queries and rules that worked well for us along the way.

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.

The VictoriaMetrics Observability Stack included two releases for VictoriaMetrics and a new LTS release for the VictoriaMetrics Operator in May. We’ve also published two detailed articles in the last few weeks: This release roundup covers updates for.

VictoriaMetrics Operator has been developing at a neck-breaking pace, bringing numerous improvements, features, and fixes to our community. We usually make at least a single release every two weeks. While this rapid iteration cycle is great for delivering fixes and improvements quickly, it can be challenging for administrators managing critical production environments.