In our series on monitoring systems, we’ve reviewed Munin, Prometheus with Grafana, and Zabbix. Now it’s time to talk about a solution that addresses one of the main pain points of Prometheus users — long-term, scalable, and efficient time-series storage. Meet VictoriaMetrics, a high-performance and cost-effective TSDB (time-series database) that perfectly complements the Prometheus ecosystem when paired with Grafana for visualization.

What Is VictoriaMetrics and Why Do You Need It?

Prometheus handles real-time monitoring and storage well, but its built-in TSDB isn’t designed for long-term retention or scaling to terabytes or petabytes of data. That’s where VictoriaMetrics comes in.

VictoriaMetrics is a fast, compact, and scalable open-source time-series database. It’s fully compatible with the Prometheus API, which means you can:

• continue using familiar exporters,
• connect it as a remote storage backend for Prometheus,
• and use Grafana without changing existing dashboards.

VictoriaMetrics Architecture

VictoriaMetrics can run in two modes:

• Single-node — a standalone binary that handles ingestion, storage, and querying. Ideal for small to medium deployments.
• Cluster version — a scalable architecture with vminsert, vmselect, and vmstorage components. Supports horizontal scaling, high availability, and petabyte-scale storage.

Integration with Prometheus and Grafana

Integration is simple and doesn’t require reworking your existing infrastructure:

1. Data collection: Keep using Prometheus or switch to vmagent, a lightweight agent from VictoriaMetrics.
2. Storage: In prometheus.yml, configure the remote_write endpoint to point to VictoriaMetrics.
3. Visualization: In Grafana, add a Prometheus-type data source pointing to VictoriaMetrics — everything else works as-is.

Advantages of VictoriaMetrics + Grafana

• Highly efficient storage: Metric compression is 5–10× better than Prometheus. Saves disk space and I/O.
• PromQL compatibility: Supports most Prometheus operators, functions, and syntax.
• Horizontal scalability: The cluster architecture scales to huge volumes easily.
• Easy deployment: Single-node runs as one binary; cluster setup is relatively straightforward.
• Optimized query engine: Handles complex queries quickly, even at large scale.
• Built for long-term retention: Designed for historical data analysis from the ground up.

Limitations and Drawbacks

• Not a standalone monitoring system: It doesn’t replace Prometheus — it complements it as storage. No built-in alerting.
• Fewer native tools: Relies mostly on external components — Prometheus, Grafana, Alertmanager.
• Relatively small community: Growing steadily but still smaller than Prometheus/Zabbix ecosystems.

Usability and Unique Features

VictoriaMetrics is a great fit if you:

• use Prometheus and need scalable storage,
• want to reduce storage costs,
• require deep historical analytics,
• value high query performance.

Unique Features:

• VMQL (VictoriaMetrics Query Language) — an extended PromQL with additional functions and optimizations.
• Multi-tenancy — isolated metric spaces for multiple clients on the same cluster.
• Server-side aggregation — reduces stored data volume and speeds up query response.
• vmagent — an efficient metric collector, usable as a Prometheus alternative in some cases.

Conclusion

VictoriaMetrics + Grafana is an excellent solution for metric storage and analysis in scalable environments. It strengthens Prometheus by solving its weaknesses in storage, scalability, and performance. This is especially relevant for organizations looking for cost-effective and reliable monitoring without sacrificing flexibility.

This stack doesn’t replace Prometheus but serves as its natural extension — an ideal backend for long-term metric analytics.