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 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.
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.
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.
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.
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including optimizations, online models, multitenantcy and mTLS support.
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including presets, new models, enhanced tuning, and better resource management
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.
This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In this second part, we explore the distinct anomaly types inherent to time-series and offer insights on how to tackle them effectively.
This blog post series focuses on Anomaly Detection (AD) and Root Cause Analysis (RCA) within the context of time-series data. The inaugural chapter lays the groundwork by introducing the role of AD in end-to-end observability systems, discussing domain-specific terminology, and addressing the challenges inherent to the time-series nature of the data.