VictoriaMetrics is a self-hosted time series database optimized for high-cardinality Prometheus metrics, offering faster ingestion, lower storage cost, and simpler operations than running Prometheus at production scale.
The Problem
Prometheus works well for small deployments but becomes expensive and operationally complex as metric cardinality grows. Storage requirements balloon quickly, query latency increases under load, and horizontal scaling requires third-party solutions like Thanos or Cortex. Teams operating large microservice fleets often spend more time managing their monitoring infrastructure than acting on the data.
How VictoriaMetrics Solves It
VictoriaMetrics is a drop-in Prometheus-compatible storage backend that ingests metrics at high speed, compresses time series data efficiently, and queries it fast via MetricsQL (a Prometheus-compatible query language with extensions). It scales from single-node to a distributed cluster without external coordination services. Apache-2.0 licensed.
Key Features
- 10-20x better compression than Prometheus for typical time series workloads, reducing storage costs significantly
- Prometheus-compatible scraping, PromQL support, and remote write endpoint for zero-migration ingestion
- Single-node binary with no external dependencies; cluster edition available for horizontal scaling
- MetricsQL extensions for easier aggregation and range queries across high-cardinality label sets
- Grafana datasource plugin included; works with AlertManager for alerting
- Apache-2.0 license with no feature gating between community and enterprise
Who It's For
VictoriaMetrics is best for DevOps and SRE teams running Prometheus-based monitoring stacks who are hitting storage or query performance limits at scale. It is particularly effective for organizations with high-cardinality metrics from Kubernetes clusters, microservices, or IoT fleets.
Compared to Prometheus
Unlike Prometheus, VictoriaMetrics is designed for long-term storage and high-cardinality workloads. Prometheus is better for short-retention monitoring close to the source, while VictoriaMetrics handles multi-year retention and millions of active series without the operational overhead of a Thanos or Cortex layer.
