VictoriaMetrics Observability Blog

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Observability's Sixth Sense: Grounding Anomaly Detection in Reality

Learn how VictoriaMetrics combines machine learning, MCP, and natural-language workflows to simplify observability with anomaly detection and reduce operational overhead.

What's new in VictoriaMetrics Anomaly Detection (Q2 2026)

The Q2 2026 vmanomaly update introduces Temporal Envelope, a redesigned UI, faster online-model execution, and an AI-assisted workflow that turns natural-language monitoring goals into tested configurations and alerting rules.

What's new in VictoriaMetrics Anomaly Detection (Q1 2026)

Q1 2026 brought incremental but important updates to VictoriaMetrics Anomaly Detection: UI improvements, AI assistance inside the UI, a public traces playground, new false-positive reduction controls, and continued resource optimizations.

VictoriaMetrics at KubeCon Amsterdam: Community Highlights

VictoriaMetrics participated in KubeCon + CloudNativeCon Europe 2026 in Amsterdam. The team delivered multiple talks covering platform design, Kubernetes observability, and distributed tracing optimization. A real-world case study from Miro showcased a cost-efficient, AZ-aware observability architecture built with VictoriaMetrics. With a 15-person team on site, the booth saw strong interest from users tackling scaling, cost, and performance challenges. The company also hosted its first community after-party, “After Deploy,” co-organized with Varnish and Shipfox, extending discussions beyond the conference.

What’s new in VictoriaMetrics Anomaly Detection (2025)

VictoriaMetrics Anomaly Detection has had a productive year with lots of user feedback that has had a major impact on product development. We’ve added improvements across the board: in core functionality, simplicity, performance, visualisation and AI integration. In addition to bug fixes and speedups, below is a list of what was accomplished in 2025.

How a US Software Provider Improved Traffic Alerting with VictoriaMetrics Anomaly Detection

VictoriaMetrics Anomaly Detection enables reliable alerting for highly variable, multi-domain traffic without relying on static thresholds. In this case study, fine-tuned models, backtesting, and clear visualization helped reduce alert noise, improve confidence in anomaly detection, and lower operational overhead.

vmanomaly Deep Dive: Smarter Alerting with AI (Tech Talk Companion)

Tech Talk: In this post, we explore vmanomaly through the eyes of its creators. Learn how this AI-powered alerting system helps cut through noise, avoid static rule spaghetti, and deliver actionable insights directly from your monitoring data.

VictoriaMetrics Anomaly Detection: What's New in Q3 2024?

Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including optimizations, online models, multitenantcy and mTLS support.

VictoriaMetrics Anomaly Detection: What's New in H1 2024?

Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including presets, new models, enhanced tuning, and better resource management

Anomaly Detection for Time Series Data: Techniques and Models

This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In Chapter 3, we delve into a variety of advanced anomaly detection techniques, encompassing supervised, semi-supervised, and unsupervised approaches, each tailored to different data scenarios and challenges in time-series analysis.