VictoriaMetrics: High-Throughput Time Series Engine That Doesn’t Collapse at Scale
General Overview
VictoriaMetrics is a time series database designed for one thing — storing and querying a massive amount of metrics, fast. It doesn’t try to do alerting, dashboards, or orchestration. You give it metrics, and it keeps them compressed, indexed, and ready to query. That’s the deal.
Originally created as a high-performance backend for Prometheus, VictoriaMetrics now works in standalone setups, multi-tenant environments, and as a drop-in replacement for Prometheus remote storage. The goal is to handle more data with less hardware, while staying predictable.
There’s no SQL layer, no schema guesswork, and no pipeline engine. It’s built to stay lean and efficient, especially when things get big.
Capabilities and Features
| Feature | What It Offers |
| Metrics Ingestion | Handles millions of samples per second on a single node |
| Prometheus-Compatible | Accepts remote_write, native pull, or exporter-based inputs |
| Cluster Mode Available | Horizontal scaling with vminsert, vmselect, and vmstorage |
| Compression Engine | Achieves 5x–20x better compression than Prometheus TSDB |
| MetricsQL | Custom query language, compatible with most of PromQL syntax |
| Built-In Web UI | Explore time series directly without needing Grafana |
| OpenTelemetry Support | Accepts OTLP format; useful in hybrid observability stacks |
| Multi-Tenant Support | Namespaces and resource limits for isolation |
| Alerting Integration | Works with Alertmanager or via webhook systems |
| Resource Efficiency | Low memory usage under load; tuned for commodity hardware |
Deployment Notes
– Available as single binary or Docker image
– Cluster deployment splits reads/writes/storage into scalable services
– Compatible with Prometheus exporters and integrations (Node Exporter, Blackbox, etc.)
– Pre-built Helm charts available for Kubernetes
– Works well with Thanos, Grafana, and Alertmanager
– No external storage dependencies — uses local files or cloud-mounted volumes
– Storage retention defined via flags (e.g., -retentionPeriod=6)
Usage Scenarios
– Replacing long-term Prometheus storage in high-cardinality environments
– Centralized metrics ingestion from distributed Prometheus instances
– Edge node metrics collection into one VM backend via remote_write
– Visualizing business-level application health with Grafana and MetricsQL
– Ingesting telemetry from Kubernetes clusters, IoT, or time-critical devices
– Hybrid cloud infrastructure monitoring at scale
Limitations
– Not a full observability suite — no dashboards, alerting rules, or notification logic
– MetricsQL has a learning curve and isn’t 100% PromQL-compatible
– Cluster mode setup is more manual than out-of-the-box TSDBs
– Lacks native UI integrations for enterprise use — everything external
– No built-in federation — needs side tooling for Prometheus-style merging
Comparison Table
| Tool | Focus | Compared to VictoriaMetrics |
| Prometheus | Scraping + short-term store | Easier to start, not built for long retention or high cardinality |
| Thanos | Federated Prometheus | More features, higher complexity and resource use |
| InfluxDB | General time series | Flexible schema, but heavier and slower under load |
| TimescaleDB | SQL-based time series | SQL queries; not as efficient for Prometheus-style workloads |
| Cortex | Scalable Prometheus backend | More complex to operate; designed for multi-tenant SaaS |
