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Open Source & Enterprise Observability Solutions

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Brandwatch
CERN
DFKI
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GROOVE X
HubSpot
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Spotify R&D
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What Our Users & Customers Say About Us

  • “We were searching for alternative solutions following performance issues with Prometheus and InfluxDB. VictoriaMetrics’ walkthrough of use cases, and concise detail gave us excellent insight into how they could help us. The solution’s backwards compatibility with Prometheus made implementation into the CMS monitoring cluster as smooth and seamless as possible.”
    V. Kuznetsov, Cornell University (member of CMS collaboration at CERN)
  • “Fortunately, with VictoriaMetrics, we were able to sign a contract to get the support we needed to confidently run a monitoring system at our scale. The numbers speak for themselves. Our proof-of-concept trial showed dramatically reduced compute and storage costs, translating into a 10x lower AWS bill.”
    Dmytro Shevchuk, Grammarly Engineering
  • “To effectively manage the vast amounts of data generated by Gemini’s solar panels and battery storage systems, we implemented a solution that could handle millions of metrics per second with exceptional accuracy and efficiency. VictoriaMetrics is an essential part of data management for the Gemini project and is the backbone of IHI Terrasun’s data collection and management system. The platform’s ability to handle massive datasets with ease has been invaluable.”
    Larry Kane, SVP of Project and Product Engineering, IHI Terrasun
  • “We tested other available monitoring solutions like Prometheus but it never scaled well enough for us, then we discovered VictoriaMetrics. Acting as a drop-in replacement for Prometheus, we were able to set-up our initial test system within only 20 minutes. With the Enterprise features, we will be able to keep our slightly down-sampled metrics for months, or years, instead of throwing everything away after two weeks as we do currently.”
    Dr. Carsten Aulbert, Cluster Administrator, Max Planck Institute for Gravitational Physics
  • “We decided to switch to VictoriaMetrics Cloud from Grafana Cloud (Mimir), which, with our large number of active time series, we found very expensive for storing metrics. VictoriaMetrics is compatible with Prometheus, so we didn’t need to change any of our code or queries. Due to VictoriaMetrics’ high optimization features, it handles our volume of time series on relatively modest hardware, reducing our costs of metrics storage by approximately five times.”
    Timothy Sleptsov, Senior Software Engineer, Granulate
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See Our Latest Blogs On All Things Observability

VictoriaLogs Basics: What You Need to Know, with Examples & Visuals

Cluster mode in VictoriaLogs is not a separate build. It is the same victoria-logs binary started with different flags, so you can scale out without a migration step. Storage nodes persist data on disk, while gateway nodes can stay stateless by pointing to storage with -storageNode. It also ships with practical safety switches, like read-only protection when -storageDataPath runs low and optional partial results when a storage node is down.

What's New in VictoriaMetrics Cloud Q4 2025? New tiers, more deployment options, IaC and alerting rules.

In the last quarter of 2025, VictoriaMetrics Cloud brings many great features: New powerful Capacity Tiers, the expansion to the us-east-1 (N.Virginia) AWS region in the US, new Notification Groups, a Terraform provider to complete your IaC, 9 brand new Alerting Rule Integrations and much more.

Vibe coding tools observability with VictoriaMetrics Stack and OpenTelemetry

Learn how to add observability to Vibe Coding Tools using OpenTelemetry and the VictoriaMetrics Stack. This guide explains how to configure popular vibe coding tools to export their metrics telemetry and get insights about your vibe coding sessions.

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.

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Together, we’re building a friendly and happy open-source community, where every question gets answered – fast.

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