Operations | Monitoring | ITSM | DevOps | Cloud

Summary: Jean-Jérôme Schmidt-Soisson, marketing lead at VictoriaMetrics, shows that open source and marketing are not opposites. Open source itself can be a powerful growth engine through trust, community, content, documentation, and discoverability. The article explains VictoriaMetrics’ pull-based marketing strategy, anchored in open-source values, to support adoption and business growth. This is our second article on the relationship between open source and marketing.

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.