VictoriaMetrics

VictoriaMetrics

VictoriaMetrics is an open-source time series database optimized for high-cardinality data and high ingestion rates. It is a cost-effective alternative to Prometheus for monitoring and alerting.
Ai Tools & Services Monitoring
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time-series metrics monitoring alerting
VictoriaMetrics alternatives ➤

VictoriaMetrics: Open-Source Time Series Database

VictoriaMetrics is an open-source time series database optimized for high-cardinality data and high ingestion rates. It is a cost-effective alternative to Prometheus for monitoring and alerting.

What is VictoriaMetrics?

VictoriaMetrics is an open-source time series database optimized for high-cardinality datasets and high ingestion rates. It is purpose-built for operations monitoring and alerting use cases.

Key features and benefits of VictoriaMetrics include:

  • Highly efficient data compression enabling significantly lower storage costs compared to alternatives like Prometheus
  • Optimized for fast data ingestion rates exceeding millions of data points per second per single node
  • Horizontally scalable through sharding
  • PromQL-compatible query language for easy migration from Prometheus
  • Supports backups, replication, and retention policies
  • Has native integration with Grafana for dashboards and visualization
  • Provides high availability when deployed in clusters

VictoriaMetrics delivers exceptional price/performance ratio, making it a very cost-effective option compared to hosted Prometheus solutions. It’s a good choice for organizations looking to reduce their monitoring infrastructure costs without sacrificing stability, reliability or metrics data quality.

VictoriaMetrics Features

Features

  1. High-performance time series database
  2. Supports PromQL query language
  3. Single-node and cluster modes
  4. Data retention policies
  5. Alerting and recording rules
  6. Remote storage integrations
  7. Grafana dashboard support

Pricing

  • Open Source
  • Free

Pros

High ingestion and query rates

Efficient storage format

Easy horizontal scaling

PromQL support provides query flexibility

Cost-effective compared to Prometheus

Cons

Less ecosystem support than Prometheus

Limited dashboarding compared to Prometheus & Grafana

No native visualization or dashboarding

Official Links

Official Website
https://victoriametrics.com

The Best VictoriaMetrics Alternatives

Top Ai Tools & Services and Monitoring and other similar apps like VictoriaMetrics

Here are some alternatives to VictoriaMetrics:

Prometheus icon
Prometheus
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InfluxDB
OpenTSDB icon
OpenTSDB
TimescaleDB icon
TimescaleDB
Suggest an alternative ❐

Prometheus icon

Prometheus

Prometheus is an open source monitoring and alerting system optimized for containerized environments like Kubernetes. It provides real-time visibility into applications, infrastructure, and services running in an environment.Prometheus works by scraping and storing time series data from various systems and services. It collects metrics from targets using either a pull...
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InfluxDB icon

InfluxDB

InfluxDB is an open-source time series database optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, Internet of Things sensor data, and real-time analytics.As a time series database, InfluxDB specializes in handling time stamped data, such as server monitoring data,...
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OpenTSDB icon

OpenTSDB

OpenTSDB is an open source, distributed time series database written on top of HBase. It is specifically designed to store and serve massive amounts of time series data without losing granularity, making it well-suited for monitoring use cases that require high reliability and scalability.Some key features of OpenTSDB include:Scalability -...
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TimescaleDB icon

TimescaleDB

TimescaleDB is an open-source, relational database optimized for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), yet retains the standard PostgreSQL interface.TimescaleDB scales out complex queries by automatically partitioning data into discrete chunks, then executing queries in parallel across many chunks....
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