Teams that switch to VictoriaMetrics report 70% less RAM and 75% less disk (verified PeerSpot user reviews), and cloud bills up to 10x lower (Grammarly case study) — while keeping their existing dashboards and alerting rules working. It is an open source observability solution for metrics, logs, and traces: a drop-in replacement for Prometheus and other backends that keeps scaling when data volume, query speed, or cost no longer does. It runs anywhere — from a Raspberry Pi to thousand-core clusters, on-premises or in the cloud — and is Kubernetes- and OpenTelemetry-compatible. Built by engineers, for engineers: 1B+ Docker pulls, 19M GitHub downloads, and 17K+ GitHub stars. Simple, reliable, and efficient observability for everyone.
| Product | Mindshare (%) |
|---|---|
| VictoriaMetrics | 1.9% |
| MySQL | 12.7% |
| PostgreSQL | 11.8% |
| Other | 73.6% |
VictoriaMetrics delivers observability in two complementary forms. The open source products are complete, production-grade, and free to run at any scale. Enterprise builds on that same code with capabilities and support for the most demanding environments — it extends open source rather than gating it.
Open source. VictoriaMetrics is a high-performance time series database and monitoring solution, compatible with PromQL via MetricsQL, so existing Prometheus dashboards, recording rules, and alerting rules keep working. VictoriaLogs is a logs database for mission-critical logging, and VictoriaTraces stores and queries distributed tracing data. All three are engineered for minimal RAM, disk, and compute at high ingestion rates.
Enterprise. VictoriaMetrics Enterprise adds features for large, multi-team deployments — plus direct support from the engineers who build the product, with architectural and security guidance. VictoriaMetrics Cloud is the same solution fully managed, and VictoriaMetrics Anomaly Detection applies machine learning to cut alert noise, so the alerts that do reach your team in Slack or on their phones are the ones that matter.
Measured results from users. PeerSpot reviewers report roughly 70% lower RAM use, 75% lower disk use, 3x faster writes, and 7x faster p95 query latency after replacing Prometheus. Grammarly cut its monitoring-related AWS bill about 10x; Granulate reduced metrics storage costs about 5x after moving from Grafana Cloud (Mimir).
Why teams choose VictoriaMetric
What are the key features of VictoriaMetrics?
What benefits and ROI should users expect?
Top use cases
VictoriaMetrics runs in production across finance, telecommunications, energy, scientific research, and IoT — from CERN's particle physics experiments to IHI Terrasun's utility-scale battery storage to Grammarly's product infrastructure. Wherever real-time telemetry at scale is critical, it delivers simple, reliable, and efficient observability for everyone. By engineers, for engineers.
VictoriaMetrics was previously known as VictoriaMetrics Observability Enterprise Support.
| Author info | Rating | Review Summary |
|---|---|---|
| Software Engineer & Data Analyst at a consultancy with 11-50 employees | 4.0 | I find VictoriaMetrics a fast, stable, open-source time-series database ideal for monitoring. It efficiently handles large data, integrates well with Prometheus/Grafana, and simplifies my work, enabling quick, resource-light testing. I rate it 8/10 and recommend it. |
| Software Engineer at a outsourcing company with 201-500 employees | 4.5 | We replaced Prometheus with VictoriaMetrics for app monitoring, significantly reducing resource usage (e.g., 70% RAM, 75% disk) and boosting performance. It's easy, stable, scalable, and PromQL-compatible. I recommend switching, though documentation needs improvement. |
I am using VictoriaMetrics to monitor approximately 300 servers and several existing processes on each server, handling data coming from thousands of electronic tags. I needed a database that was fast and would later allow me to perform data analysis.
Currently, I use VictoriaMetrics in combination with Prometheus and Grafana to manage this amount of data.
VictoriaMetrics has had a positive impact on my organization, though the system I created is still in testing. The company has not yet evaluated the benefit of having this monitoring system.
During these tests, the benefit of integration with already existing standards has made it easier for me to create reporting and dashboards that would have been difficult to build otherwise. The ease of creating dashboards with Grafana for accessing the data consumed by these processes is a notable advantage.
The integration of VictoriaMetrics has simplified my daily work by providing the ease of having time to create a relational database that allowed me to create tests with great ability in a short timeframe. I overcame the fact that I did not waste time standardizing the data. The speed, standardization, and small footprint with optimization of space allowed me to create test procedures in a very short time, which is certainly a significant advantage because I can create a product quickly.
The best features offered by VictoriaMetrics are that it is a very fast database and a time series database that is very capable of handling an enormous amount of data. This is exceptional.
I find the data compression particularly useful in my daily work, and I moved from Prometheus to VictoriaMetrics precisely because of the ability to have a larger amount of data in the same space, meaning secondary storage.
I consider the speed with which I can connect to this data compared to a relational database and the fact that it is an open-source platform particularly relevant. There is a community that allows me to eventually manage an open-source project without having to worry about the possibility of receiving updates.
The fact that it is open source is very important, and it integrates well with existing tools such as Prometheus or with connectors for data access from my services.
Based on my experience, I am not aware of dedicated data access tools and query tools for VictoriaMetrics database, but I definitely know about Python integration with existing systems. For my knowledge, VictoriaMetrics is sufficient to achieve the results I want to achieve. At the moment I have not identified anything that I would request.
I compared it with my experience on other time series databases, and it has a huge advantage, so I do not see the need for urgent improvements or optimizations.
I think that access through programming libraries such as those for Python or R are already self-explanatory, so I would not find it difficult to find a very direct use of VictoriaMetrics. If VictoriaMetrics instead provided tools with a language closer to that of reporting, it would perhaps be more directly accessible without having to go through programming. The conversion of specific time series database languages into reporting languages could be one of the aspects that would improve the use of VictoriaMetrics.
I have been using VictoriaMetrics for a few months to create a monitoring system, and it is now in production, though not heavily used. I am still keeping it in test, but the company still does not want to take advantage of the results of my test.
VictoriaMetrics is absolutely stable according to my experience. I did not need to perform updates in the period between December and February of this year.
I have no experience with upgrading VictoriaMetrics and I have had no need for upgrades. At this time, I cannot go into detail.
I have not had the opportunity to interact with the support service of VictoriaMetrics.
I was using a different solution previously, which was a custom solution with a relational database, but the infrastructure required many more resources, so I had to think of an alternative.
I have implemented VictoriaMetrics in my organization using on-premises infrastructure.
I am still in the testing phase awaiting the company's evaluation, and therefore I have not yet progressed beyond the phase in which I can discuss a return or benefits.
My experience with the pricing, setup costs, and licensing of VictoriaMetrics is positive. I used the free open-source version, and it was sufficient for my needs.
Before choosing VictoriaMetrics, I went directly to Prometheus and then from Prometheus to VictoriaMetrics, also passing through Zabbix. However, I evaluated Zabbix as an alternative that was far less performant than what I needed.
On a scale from 1 to 10, I would rate VictoriaMetrics an 8.
I chose exactly 8 out of 10 because I do not have full knowledge of the product in production, and my experience stopped at the testing phase pending the company's evaluation.
Regarding the AI features of VictoriaMetrics, I did not have the opportunity to use them when I created the infrastructure that I needed.
As for the AI features of VictoriaMetrics, I rate the accuracy and reliability of its results as extremely positive because the data was reliable in real time.
The advice I would give to other people who are considering using VictoriaMetrics is that it is a reliable product with few hardware resources required, so I recommend it.
My overall review rating for VictoriaMetrics is 8 out of 10.
VictoriaMetrics is a time-series database that we use to handle a large volume of metrics coming from our application. We have multiple applications running, and we want to monitor everything to understand exactly what is happening, so we deployed VictoriaMetrics to serve as our time-series database and then use Grafana to visualize the data. VictoriaMetrics serves as a replacement for Prometheus to monitor all the time-series data that comes from our applications.
VictoriaMetrics is built with memory constraints in mind, and we saved a significant amount of memory after implementing it. The first advantage is that it is quite easy to use, and we can leverage PromQL queries in VictoriaMetrics with a straightforward deployment process.
The best aspect of using VictoriaMetrics is the reduction in resources including RAM, memory, disk, and other infrastructure requirements, which have all decreased substantially after implementation.
We observed that RAM was reduced by around 70% and disk usage by 75%, while write speed increased 3x and query per second P95 latency became 7x faster. The financial savings are significant because we observed a substantial reduction in resources such as CPU and memory after adopting VictoriaMetrics.
VictoriaMetrics is quite a good project and quite easy to use overall, though some documentation could be improved. The main area that needs improvement in VictoriaMetrics is the documentation itself, as the support is adequate and the open-source project does provide replies within one to two days.
We have been using VictoriaMetrics for around two years.
VictoriaMetrics is stable.
VictoriaMetrics's scalability is quite good.
We use the open-source version of VictoriaMetrics, so we did not require any customer support. However, we do use the open-source project to contact them and learn more about the issues and projects that we are working on.
We used Prometheus earlier, but we found that the resource usage was quite high, so we decided to try VictoriaMetrics, which resulted in a substantial reduction in resources.
We did not require any license for VictoriaMetrics since we are using the open-source version, so the setup cost is what we incurred.
We did not require any license for VictoriaMetrics since we are using the open-source version, so the setup cost is what we incurred.
Prometheus was the solution we were using, and we did not evaluate other options; we simply proceeded with VictoriaMetrics and have continued to use it.
You should use VictoriaMetrics if you are currently using Prometheus, as I believe it is quite helpful and you should consider switching. I give this review an overall rating of 9.
