VictoriaMetrics is a time series database and monitoring solution written in Go that emphasizes performance, cost-effectiveness, and scalability for handling metrics data. The project serves as both a long-term storage backend for Prometheus and a drop-in replacement for Prometheus and Graphite in Grafana deployments. It is distributed under the Apache License 2.0 in both single-node and cluster versions, with binary releases, Docker images, and source code publicly available.
The repository demonstrates substantial community engagement and active development.
VictoriaMetrics supports ingestion through multiple protocols including Prometheus remote write API, InfluxDB line protocol over HTTP/TCP/UDP, Graphite plaintext protocol with tags, OpenTSDB put messages, JSON line format, CSV data, native binary format, DataDog agent/DogStatsD, NewRelic infrastructure agent, and OpenTelemetry metrics format. This protocol diversity enables integration with existing monitoring infrastructure without requiring data transformation. The system supports both PromQL and MetricsQL query languages, with MetricsQL positioned as more performant for certain workloads.
The project is optimized for scenarios involving high-cardinality time series data and large-scale deployments. According to benchmarks cited in the repository, VictoriaMetrics achieves 10x less RAM consumption than InfluxDB for handling millions of unique time series, 7x less RAM than Prometheus or Thanos, and 20x better ingestion performance than InfluxDB and TimescaleDB. Data compression efficiency reaches 70x more data points stored compared to TimescaleDB with equivalent storage, and 7x less storage space than Prometheus, Thanos, or Cortex. The system handles high-latency IO and low IOPS environments effectively, making it suitable for cloud storage backends.
The enterprise version adds anomaly detection, backup automation, multiple retention policies for cost reduction, downsampling capabilities, long-term support releases, and comprehensive consulting support from the core development team. Security certifications have been achieved for Database Software Development and Software-Based Monitoring Services. The repository's classification by GitGenius spans storage, time series database, analytics, scalability, high-performance analytics, real-time queries, efficient ingestion, metric monitoring, and observability solutions, reflecting its broad applicability across monitoring and observability use cases.
Case studies document deployments at organizations including Grammarly, Roblox, Wix, and Spotify, demonstrating production adoption at scale.
