VictoriaMetrics vs Prometheus

Struggling to choose between VictoriaMetrics and Prometheus? Both products offer unique advantages, making it a tough decision.

VictoriaMetrics is a Ai Tools & Services solution with tags like time-series, metrics, monitoring, alerting.

It boasts features such as High-performance time series database, Supports PromQL query language, Single-node and cluster modes, Data retention policies, Alerting and recording rules, Remote storage integrations, Grafana dashboard support and pros including High ingestion and query rates, Efficient storage format, Easy horizontal scaling, PromQL support provides query flexibility, Cost-effective compared to Prometheus.

On the other hand, Prometheus is a Ai Tools & Services product tagged with monitoring, alerting, metrics.

Its standout features include Multi-dimensional data model with time series data identified by metric name and key/value pairs, PromQL, a flexible query language to leverage this dimensionality, No reliance on distributed storage; single server nodes are autonomous, Time series collection happens via a pull model over HTTP, Pushing time series is supported via an intermediary gateway, Targets are discovered via service discovery or static configuration, Multiple modes of graphing and dashboarding support, and it shines with pros like Highly dimensional model allows flexible and efficient queries, PromQL supports aggregation and recording rules for pre-calculation, Built-in alerting and notification routing, Highly available with simple operational model, Native support for Kubernetes, Strong ecosystem integration.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

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.

Categories:
time-series metrics monitoring alerting

VictoriaMetrics 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


Prometheus

Prometheus

Prometheus is an open-source systems monitoring and alerting toolkit. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if certain conditions are met.

Categories:
monitoring alerting metrics

Prometheus Features

  1. Multi-dimensional data model with time series data identified by metric name and key/value pairs
  2. PromQL, a flexible query language to leverage this dimensionality
  3. No reliance on distributed storage; single server nodes are autonomous
  4. Time series collection happens via a pull model over HTTP
  5. Pushing time series is supported via an intermediary gateway
  6. Targets are discovered via service discovery or static configuration
  7. Multiple modes of graphing and dashboarding support

Pricing

  • Open Source

Pros

Highly dimensional model allows flexible and efficient queries

PromQL supports aggregation and recording rules for pre-calculation

Built-in alerting and notification routing

Highly available with simple operational model

Native support for Kubernetes

Strong ecosystem integration

Cons

Pull-based model can miss short-lived spikes between scrapes

No automatic removal of stale metrics (extra storage usage)

Limited tooling for stats analysis, forecasting, anomaly detection

No built-in federation for massive scale

Steep learning curve for PromQL and architecture