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.
- Changelog: [CHANGELOG](https://docs.victoriametrics.com/changelog/), and [How to upgrade](https://docs.victoriametrics.com/#how-to-upgrade-victoriametrics)
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.
* [InfluxDB line protocol](https://docs.victoriametrics.com/#how-to-send-data-from-influxdb-compatible-agents-such-as-telegraf) over HTTP, TCP and UDP.
* [Graphite plaintext protocol](https://docs.victoriametrics.com/#how-to-send-data-from-graphite-compatible-agents-such-as-statsd) with [tags](https://graphite.readthedocs.io/en/latest/tags.html#carbon).
* [OpenTSDB put message](https://docs.victoriametrics.com/#sending-data-via-telnet-put-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.).
The provided [ZIP file](https://github.com/VictoriaMetrics/VictoriaMetrics/blob/master/VM_logo.zip) contains three folders with different logo orientations. Each folder includes the following file types:
