mrkeyoor.com_
Wed 02 Sept 21:56 UTC
Dataevaluationupdated 02 Sept 2026

VictoriaMetrics review

VictoriaMetrics is an open-source system for storing, querying, and managing time-series data such as infrastructure metrics, sensor readings, and application telemetry. It can act as long-term storage for Prometheus or as a Grafana-compatible replacement for Prometheus and Graphite, with both single-node and clustered deployment options.

trackingstars / 7d
Verdict

Our build succeeded in 105 seconds, but 2 of 190 tests failed. VictoriaMetrics deserves a serious trial if Prometheus retention, ingestion scale, or cross-instance querying has become a real operational constraint, because the open-source edition includes both single-node and cluster modes. Do not adopt it on reputation alone: reproduce the test failure, model the stateful operations, and confirm which Enterprise-only features your plan actually needs.

We ran it

Install✓ · 62s0 packages
Build✓ · 105s
Tests✗ · 159s188 passed · 2 failed of 190 (go test)
Repo2365 files~304,094 lines of source · 69 MB · 8 CI workflows

Answers from our run

Does VictoriaMetrics build from source?

Dependencies installed in 62 seconds (0 packages), and the build succeeded in 105 seconds. We cloned commit 0e74caf into a clean Debian container with 3 CPUs and no project-specific setup.

Do VictoriaMetrics's tests pass?

Not all of them: 188 of 190 passed and 2 failed when we ran the project's own test command (go test). Some failures need services or credentials a bare container does not have.

Who should not use VictoriaMetrics?

Small teams whose existing Prometheus server already meets retention and query needs, because this adds another stateful system to operate

What are the alternatives to VictoriaMetrics?

Prometheus, Grafana Mimir, Thanos. Our build succeeded in 105 seconds, but 2 of 190 tests failed.

Setup3/5Build passed in 105s, but 2 of 190 tests failed
Docs4/5Clear quick starts, concepts, integrations, and upgrade links
Community4/517,640 stars and current activity, despite 780 open issues
Maturity5/5Single-node and cluster editions with active releases

Who it’s for

Platform teams that need longer retention or greater scale than a basic Prometheus setup
Operators consolidating metrics from multiple Prometheus instances into one query view
Organizations ingesting Kubernetes, IoT, industrial, financial, or application telemetry
Teams that want PromQL compatibility plus the option to use MetricsQL
Self-hosters who value a small binary and an Apache-2.0 core

Who it’s NOT for

Small teams whose existing Prometheus server already meets retention and query needs, because this adds another stateful system to operate
Buyers expecting every advanced capability in the free edition, because anomaly detection and other features are positioned as Enterprise extras
Teams requiring a completely clean test run before evaluation, because 2 of 190 tests failed in our sandbox
Operators looking for an all-in-one observability suite, because the evidence here is centered on metrics storage, ingestion, and querying

Setup reality

Our install phase succeeded in 62 seconds and installed 0 packages, the build succeeded in 105 seconds, and the test run finished in 159 seconds with 188 passed and 2 failed out of 190. That is more mixed than the README's small-binary, no-dependencies pitch suggests: compilation was straightforward in a fresh Go 1.24 Debian container, but the checkout did not pass its full test suite, and the provided log ends only with FAIL, so we cannot responsibly assign a cause. The repository also had no Dockerfile in our snapshot, although the project publishes ready-made images on Docker Hub and Quay.

It is a focused time-series store, not an observability bundle

VictoriaMetrics stores and queries the timestamped values behind infrastructure dashboards, Kubernetes monitoring, IoT fleets, and industrial telemetry. Its useful proposition is to preserve Prometheus-compatible workflows while adding a purpose-built long-term store. The project has 17,640 GitHub stars, an Apache-2.0 license, and a v1.151.0 release dated August 31, 2026.

The open-source offering comes in 2 deployment shapes. A single-node edition targets a simpler footprint, while a cluster edition serves capacity or availability requirements that demand distribution. Both are open source, rather than limited agents for a closed backend. Enterprise and LTS releases add commercial support and features for organizations that need them.

What happened when we ran it

Our install phase succeeded in 62 seconds inside a fresh, unprivileged Debian container using Go 1.24, and installed 0 packages. The build succeeded in 105 seconds. That supports the README's no-dependencies, small-binary pitch for compilation of commit 0e74caf, although it does not prove production setup is equally simple.

The test step was not clean. It ran for 159 seconds, with 188 passing and 2 failing out of 190. The log shows many libraries completing, including storage in 68.915 seconds and uint64set in 6.349 seconds, before a bare FAIL. It does not identify the 2 failures or their causes, so blaming either the code or container would be speculation.

We measured 2,365 files, roughly 304,094 lines of source, and a 69 MB checkout. The repository contained 8 CI workflows, no Dockerfile, and no separate tests directory. The last point does not mean tests are absent, as the Go run produced 190 results, but contributors should expect package-local tests rather than one top-level folder.

Protocol coverage is the practical advantage

The README presents at least 10 ingestion paths: Prometheus scraping and remote write, InfluxDB line protocol, Graphite, OpenTSDB, JSON lines, CSV, a native format, DataDog or DogStatsD, New Relic, and OpenTelemetry. This range can reduce translation services when a company has accumulated telemetry from several generations of tooling.

There are also 2 query choices: PromQL for compatibility and MetricsQL as the project's more performance-oriented language. A global query view can combine data from multiple Prometheus instances or other sources. Stream aggregation, metrics relabeling, and a cardinality limiter help control data before excessive label growth becomes an operational problem.

The operational story includes a small binary, command-line configuration, instant snapshots, and NFS-backed storage such as Amazon EFS and Google Filestore. Published images on 2 registries, Docker Hub and Quay, offer an easier route than compiling. These conveniences still need testing against the selected single-node or cluster topology.

The rough edges begin after the easy build

The failed suite is the clearest caution. A 105-second successful build says the source compiles on our box, but a 159-second run ending with 2 failures leaves evaluation unfinished. Capture the complete failing output and rerun those exact tests before treating this checkout as a production candidate.

Our snapshot had no Dockerfile even though official container images are advertised. That may matter to teams requiring a source-visible local container build. At 304,094 source lines and 780 open issues, this is a serious platform with a large surface area, not software whose cluster mode should be operated casually.

Some capabilities are reserved for Enterprise, with anomaly detection named in the README excerpt. Teams should map requirements against the open-source and paid editions before committing to v1.151.0. The documentation covers concepts, quick starts, integrations, changelogs, upgrades, security, LTS releases, and support, but documentation cannot replace a restore rehearsal.

Current activity is strong, while issue load needs triage

The health signals show active maintenance. Version v1.151.0 arrived on August 31, 2026, and the repository was pushed again on September 2. There are 8 CI workflows and 17,640 stars, plus public Slack, Reddit, X, and YouTube channels. Together, these signals say more than popularity alone.

The counterweight is 780 open issues. A large user base can create a large queue, so this is not proof of neglect beside a release only 2 days old and a push today. Adopters should inspect issues relevant to their protocols, storage, and upgrade path instead of assuming project-wide activity settles every edge case.

It fits behind collectors and in front of Grafana

Prometheus instances, OpenTelemetry components, or vendor agents can send metrics into VictoriaMetrics, while Grafana queries the consolidated store. The single-node edition is the sensible first evaluation for 1 team or contained workload. Cluster mode becomes reasonable only when retention, volume, tenancy, or availability justifies extra moving parts and a recovery plan.

Prometheus remains simpler when scraping and shorter local retention suffice. Thanos fits teams extending Prometheus with sidecars and object storage, while Grafana Mimir targets horizontally scalable, multi-tenant Prometheus storage. InfluxDB suits teams aligned with its separate ecosystem. These 4 alternatives differ architecturally, so compatibility should narrow the field before performance trials.

The evidence supports a trial, not a blind rollout

VictoriaMetrics combines broad protocol support, 2 open-source deployment modes, active maintenance, and a credible role as long-term Prometheus storage. Our run built successfully and passed 188 of 190 tests, which is promising but not a release gate. Trial representative cardinality, retention, restore, and query workloads, then proceed only after the 2 failures are understood.

Alternatives

ProjectWhat it isPick it when
Prometheus gh↗The default open-source metrics collector and PromQL server for many cloud-native stacks.Pick this instead when local scraping, alerting, and shorter retention are enough without a separate long-term metrics store.
Grafana MimirA horizontally scalable, multi-tenant long-term store for Prometheus metrics.Pick this instead when deep alignment with the Grafana ecosystem and Prometheus remote-write architecture matters most.
ThanosAdds global querying, durable object storage, and high availability around Prometheus deployments.Pick this instead when you want to preserve Prometheus servers and extend them with sidecars and object storage.
InfluxDB gh↗A time-series database with its own ingestion, query, and operational model.Pick this instead when your workload and team already center on the InfluxDB ecosystem rather than PromQL compatibility.

What people are saying

  1. [github-trending] VictoriaMetrics/VictoriaMetrics

Sources

  1. VictoriaMetrics GitHub repository
  2. VictoriaMetrics homepage
  3. VictoriaMetrics documentation

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