VictoriaMetrics is a fast, cost-saving, and scalable solution for monitoring and managing time series data. It delivers high performance and reliability, making it an ideal choice for businesses of all sizes.
VictoriaMetrics is optimized for timeseries data, even when old time series are constantly replaced by new ones at a high rate, it offers a lot of features:
* **Long-term storage for Prometheus** or as a drop-in replacement for Prometheus and Graphite in Grafana.
* **Powerful stream aggregation**: Can be used as a StatsD alternative.
* **Ideal for big data**: Works well with large amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various [Enterprise workloads](https://docs.victoriametrics.com/enterprise/).
* **Query language**: Supports both PromQL and the more performant MetricsQL.
* **Easy to setup**: No dependencies, single [small binary](https://medium.com/@valyala/stripping-dependency-bloat-in-victoriametrics-docker-image-983fb5912b0d), configuration through command-line flags, but the default is also fine-tuned; backup and restore with [instant snapshots](https://medium.com/@valyala/how-victoriametrics-makes-instant-snapshots-for-multi-terabyte-time-series-data-e1f3fb0e0282).
* **Global query view**: Multiple Prometheus instances or any other data sources may ingest data into VictoriaMetrics and queried via a single query.
* **Various Protocols**: Support metric scraping, ingestion and backfilling in various protocol.
- **Multiple retentions**: Reducing storage costs by specifying different retentions for different datasets.
- **Downsampling**: Reducing storage costs and increasing performance for queries over historical data.
- **Stable releases** with long-term support lines ([LTS](https://docs.victoriametrics.com/lts-releases/)).
- **Comprehensive support**: First-class consulting, feature requests and technical support provided by the core VictoriaMetrics dev team.
- Many other features, which you can read about on [the Enterprise page](https://docs.victoriametrics.com/enterprise/).
[Contact us](mailto:info@victoriametrics.com) if you need enterprise support for VictoriaMetrics. Or you can request a free trial license [here](https://victoriametrics.com/products/enterprise/trial/), downloaded Enterprise binaries are available at [Github Releases](https://github.com/VictoriaMetrics/VictoriaMetrics/releases/latest).
We strictly apply security measures in everything we do. VictoriaMetrics has achieved security certifications for Database Software Development and Software-Based Monitoring Services. See [Security page](https://victoriametrics.com/security/) for more details.
## Benchmarks
Some good benchmarks VictoriaMetrics achieved:
* **Minimal memory footprint**: handling millions of unique timeseries with [10x less RAM](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) than InfluxDB, up to [7x less RAM](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f) than Prometheus, Thanos or Cortex.
* **Highly scalable and performance** for [data ingestion](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b) and [querying](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4), [20x outperforms](https://medium.com/@valyala/insert-benchmarks-with-inch-influxdb-vs-victoriametrics-e31a41ae2893) InfluxDB and TimescaleDB.
* **High data compression**: [70x more data points](https://medium.com/@valyala/when-size-matters-benchmarking-victoriametrics-vs-timescale-and-influxdb-6035811952d4) may be stored into limited storage than TimescaleDB, [7x less storage](https://valyala.medium.com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f) space is required than Prometheus, Thanos or Cortex.
* **Reducing storage costs**: [10x more effective](https://docs.victoriametrics.com/casestudies/#grammarly) than Graphite according to the Grammarly case study.
* **A single-node VictoriaMetrics** can replace medium-sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB. See [VictoriaMetrics vs Thanos](https://medium.com/@valyala/comparing-thanos-to-victoriametrics-cluster-b193bea1683), [Measuring vertical scalability](https://medium.com/@valyala/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae), [Remote write storage wars - PromCon 2019](https://promcon.io/2019-munich/talks/remote-write-storage-wars/).
* **Optimized for storage**: [Works well with high-latency IO](https://medium.com/@valyala/high-cardinality-tsdb-benchmarks-victoriametrics-vs-timescaledb-vs-influxdb-13e6ee64dd6b) and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc.).
If you like VictoriaMetrics and want to contribute, then please [read these docs](https://docs.victoriametrics.com/contributing/).
## VictoriaMetrics Logo
[Zip](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/VM_logo.zip) contains three folders with different image orientations (main color and inverted version).
