Below please find public case studies and talks from VictoriaMetrics users. You can also join our [community Slack channel](https://slack.victoriametrics.com/)
You can also read [articles about VictoriaMetrics from our users](https://docs.victoriametrics.com/Articles.html#third-party-articles-and-slides-about-victoriametrics).
[AbiosGaming](https://abiosgaming.com/) provides industry leading esports data and technology across the globe.
> At Abios, we are running Grafana and Prometheus for our operational insights. We are collecting all sorts of operational metrics such as request latency, active WebSocket connections, and cache statistics to determine if things are working as we expect them to.
> Prometheus explicitly recommends their users not to use high cardinality labels for their time-series data, which is exactly what we want to do. Prometheus is thus a poor solution to keep using. However, since we were already using Prometheus, we needed an alternative solution to be fully compatible with the Prometheus query language.
> The options we decided to try were TimescaleDB together with Promscale to act as a remote write intermediary and VictoriaMetrics. In both cases we still used Prometheus Operator to launch Prometheus instances to scrape metrics and send them to the respective storage layers.
> The biggest difference for our day-to-day operation is perhaps that VictoriaMetrics does not have a Write-Ahead log. The WAL has caused us trouble when Prometheus has experienced issues and starts to run out of RAM when replaying the WAL, thus entering a crash-loop.
> All in all, we are quite impressed with VictoriaMetrics. Not only is the core time-series database well designed, easy to deploy and operate, and performant but the entire ecosystem around it seems to have been given an equal amount of love. There are utilities for things such as taking snapshots (backups) and storing to S3 (and reloading from S3), a Kubernetes Operator, and authentication proxies. It also provides a cluster deployment option if we were to scale up to those numbers.
> From a usability point of view, VictoriaMetrics is the clear winner. Neither Prometheus nor TimescaleDB managed to do any kind of aggregations on our high cardinality metrics, whereas VictoriaMetrics does.
See [the full article](https://abiosgaming.com/press/high-cardinality-aggregations/).
from [Remote Write Storage Wars](https://promcon.io/2019-munich/talks/remote-write-storage-wars/) talk at [PromCon 2019](https://promcon.io/2019-munich/).
- VictoriaMetrics [can scrape targets](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#how-to-scrape-prometheus-exporters-such-as-node-exporter) as well
- VictoriaMetrics didn't support replication (it [supports replication now](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html#replication-and-data-safety)) - we run an extra instance of VictoriaMetrics and Promxy in front of a VictoriaMetrics pair for high availability.
- VictoriaMetrics stores 1 extra month for defined retention (if retention is set to N months, then VM stores N+1 months of data), but this is still better than other solutions.
We had been running Prometheus for about a year in a test environment and it was working well but there was a need/wish for a few more years of retention than the old system provided. We tested Thanos which was a bit resource hungry but worked great for about half a year.
We are running 1 Prometheus, 1 VictoriaMetrics and 1 Grafana server in each datacenter on baremetal servers, scraping 350+ targets
(and 3k+ devices collected via SNMPCollector sending metrics directly to VM). Each Prometheus is scraping all targets
so we have all metrics in both VictoriaMetrics instances. We are using [Promxy](https://github.com/jacksontj/promxy) to deduplicate metrics from both instances.
Grafana has an LB infront so if one DC has problems we can still view all metrics from both DCs on the other Grafana instance.
Once we found VictoriaMetrics it solved the following problems:
- it is very lightweight and we can now run virtual machines instead of dedicated hardware machines for metrics storage
- very short startup time and any possible gaps in data can easily be filled in using Promxy
- we could continue using Telegraf as our metrics agent and ship identical metrics to both InfluxDB and VictoriaMetrics during the migration period (migration just about to start)
- compression im VM is really good. We can store more metrics and we can easily spin up new VictoriaMetrics instances
for new data and keep read-only nodes with older data if we need to extend our retention period further
than single virtual machine disks allow and we can aggregate all the data from VictoriaMetrics with Promxy
* As a long-term storage for messages ingested from the [NATS messaging system](https://nats.io/). Ingested messages are pushed directly to VictoriaMetrics via HTTP protocol
* As a long-term storage for Prometheus monitoring system (30 days retention policy. There are plans to increase it up to ½ year)
* As a data source for visualizing metrics in Grafana.
Please also see [The CMS monitoring infrastructure and applications](https://arxiv.org/pdf/2007.03630.pdf) publication from CERN with details about their VictoriaMetrics usage.
See [slides](https://speakerdeck.com/inletorder/monitoring-platform-with-victoria-metrics) and [video](https://www.youtube.com/watch?v=hUpHIluxw80)
from `Large-scale, super-load system monitoring platform built with VictoriaMetrics` talk at [Prometheus Meetup Tokyo #3](https://prometheus.connpass.com/event/157721/).
## German Research Center for Artificial Intelligence
[German Research Center for Artificial Intelligence](https://en.wikipedia.org/wiki/German_Research_Centre_for_Artificial_Intelligence) (DFKI) is one of the world's largest nonprofit contract research institutes for software technology based on artificial intelligence (AI) methods. DFKI was founded in 1988, and has facilities in the German cities of Kaiserslautern, Saarbrücken, Bremen and Berlin.
> Traditionally research groups in DFKI each used their own hardware. In mid 2020 we started an initiative to consolidate existing (and future) hardware into a central Slurm cluster to enable our researchers and students to run more and larger experiments. Based on the Nvidia deepops stack this included Prometheus for short-term metric storage. Our users liked the level of detail they got from our custom dashboards compared to our previous Zabbix-based solution, so we decided to extend the retention period to several years. Ideally we wanted PhD students to be able to recall even their earliest experiments by the time they finished their thesis. Since we do everything on-premise we needed a solution that is primarily space-efficient.
> We initially considered simply extending the retention period of the Prometheus instances included with deepops, since this would be the “batteries included” solution and appeared to be what everyone else was doing. We naively also liked the concept behind TimescaleDB, since it relies on Postgres for storage that has had decades of development. Turns out relational databases are not good at storing time-series and integration with existing exporters and Grafana would have been more difficult.
> VictoriaMetrics kept showing up in searches and benchmarks on time-series DB performance and consistently came out on top when it came to required storage. Quite frankly, the presented numbers looked like magic, so we decided to put this to the test. First impressions upon trial were excellent. Download the binary and point it at a storage location. Almost no configuration required. Apart from minor tweaks to the command line (turning on deduplication) and running it as a systemd unit we still use the same instance from the first tests today. It was further superior to Prometheus in every measurable way. It used considerably less CPU time and RAM than Prometheus and a third of the storage.
> While initially storage efficiency was the primary driver, the simplicity of setting up a testbed definitely helped. Seeing how effortlessly the single-node instance deals with our current setup gives us confidence that it will keep up with our growth for quite a while. And when the time comes that we outgrow it there is always the robust cluster variant of VictoriaMetrics that we can turn to.
> We like hassle-free experience with VictoriaMetrics. And at least for our use case a straight upgrade compared to Prometheus, while fully compatible with that ecosystem. While it can use cloud storage, there appears to be no downsides to using the filesystem instead, so it fits very well into our on-premise culture. It even comes with an excellent official Grafana dashboard to monitor performance.
Joachim Folz, Researcher, German Research Center for Artificial Intelligence (DFKI)
[Grammarly](https://www.grammarly.com/) provides digital writing assistant that helps 30 million people and 30 thousand teams write more clearly and effectively every day. In building a product that scales across multiple platforms and devices, Grammarly works to empower users whenever and wherever they communicate.
> Maintenance and scaling for our previous on-premise monitoring system was hard and required a lot of effort from our side. The previous system was not optimized for storing frequently changing metrics (moderate [churn rate](https://docs.victoriametrics.com/FAQ.html#what-is-high-churn-rate) was a concern). The costs of the previous solution were not optimal.
> We evaluated various cloud-based and on-premise monitoring solutions: Sumo Logic, DataDog, SignalFX, Amazon CloudWatch, Prometheus, M3DB, Thanos, Graphite, etc. PoC results were sufficient for us to move forward with VictoriaMetrics due to the following reasons:
- High performance
- Support for Graphite and OpenMetrics data ingestion types
- Good documentation and easy bootstrap
- Responsiveness of VictoriaMetrics support team during research and afterward
> Switching from our previous on-premise monitoring system to VictoriaMetrics allowed reducing infrastructure costs by an order of magnitude while improving DevOps experience and developer experience.
Numbers:
- [Cluster version](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) of VictoriaMetrics
[Groove X](https://groove-x.com/en/) designs and produces robotics solutions. Its mission is to bring out humanity’s full potential through robotics.
> We need monitoring solution for Device (Robot and Charge Station) health monitoring. At first, we used the Prometheus server, and then migrated to Thanos. But it was difficult to manage Thanos cluster and also we had a performance issue (long latency on request). Colopl, Inc. used VictoriaMetrics and we got interested in it. We built another k8s cluster besides our original Thanos cluster, and tried VictoriaMetrics in parallel for a while. It worked better and finally we decided to switch to VictoriaMetrics, because it provides low latency, it is in active development and it is easy to maintain.
> We like performance and scalability provided by VictoriaMetrics. We use metrics in our daily work, and long latency would be a big problem. Also, metrics correctness is important. We reported some inconsistencies with Prometheus during the evaluation period and received quick feedback from VictoriaMetrics developers.
[idealo.de](https://www.idealo.de/) is the leading price comparison website in Germany. We use Prometheus for metrics on our container platform.
When we introduced Prometheus at idealo we started with m3db as our longterm storage. In our setup, m3db was quite unstable and consumed a lot of resources.
VictoriaMetrics in production is very stable for us and uses only a fraction of the resources even though we also increased our retention period from 1 month to 13 months.
The mission of [MHI Vestas Offshore Wind](http://www.mhivestasoffshore.com) is to co-develop offshore wind as an economically viable and sustainable energy resource to benefit future generations.
MHI Vestas Offshore Wind is using VictoriaMetrics to ingest and visualize sensor data from offshore wind turbines. The very efficient storage and ability to backfill was key in choosing VictoriaMetrics. MHI Vestas Offshore Wind is running the cluster version of VictoriaMetrics on Kubernetes using the Helm charts for deployment to be able to scale up capacity as the solution is rolled out.
[Percona](https://www.percona.com/) is a leader in providing best-of-breed enterprise-class support, consulting, managed services, training and software for MySQL®, MariaDB®, MongoDB®, PostgreSQL® and other open source databases in on-premises and cloud environments.
Percona migrated from Prometheus to VictoriaMetrics in the [Percona Monitoring and Management](https://www.percona.com/software/database-tools/percona-monitoring-and-management) product. This allowed [reducing resource usage](https://www.percona.com/blog/2020/12/23/observations-on-better-resource-usage-with-percona-monitoring-and-management-v2-12-0/) and [getting rid of complex firewall setup](https://www.percona.com/blog/2020/12/01/foiled-by-the-firewall-a-tale-of-transition-from-prometheus-to-victoriametrics/), while [improving user experience](https://www.percona.com/blog/2020/02/28/better-prometheus-rate-function-with-victoriametrics/).
## Razorpay
[Razorpay](https://razorpay.com/) aims to revolutionize money management for online businesses by providing clean, developer-friendly APIs and hassle-free integration.
> As a fintech organization, we move billions of dollars every month. Our customers and merchants have entrusted us with a paramount responsibility. To handle our ever-growing business, building a robust observability stack is not just “nice to have”, but absolutely essential. And all of this starts with better monitoring and metrics.
> We executed a variety of POCs on various solutions and finally arrived at the following technologies: M3DB, Thanos, Cortex and VictoriaMetrics. The clear winner was VictoriaMetrics.
> The following are some of the basic observations we derived from Victoria Metrics:
> * Simple components, each horizontally scalable.
> * Clear separation between writes and reads.
> * Runs from default configurations, with no extra frills.
> * Default retention starts with 1 month
> * Storage, ingestion, and reads can be easily scaled.
> * High Compression store ~ 70% more compression.
> * Currently running in production with commodity hardware with a good mix of spot instances.
> * Successfully ran some of the worst Grafana dashboards/queries that have historically failed to run.
See [the full article](https://engineering.razorpay.com/scaling-to-trillions-of-metric-data-points-f569a5b654f2).
[Sensedia](https://www.sensedia.com) is a leading integration solutions provider with more than 120 enterprise clients across a range of sectors. Its world-class portfolio includes: an API Management Platform, Adaptive Governance, Events Hub, Service Mesh, Cloud Connectors and Strategic Professional Services' teams.
> Our initial requirements for monitoring solution: the metrics must be stored for 15 days, the solution must be scalable and must offer high availability of the metrics. It must being integrated into Grafana and allowing the use of PromQL when creating/editing dashboards in Grafana to obtain metrics from the Prometheus datasource. The solution also needs to receive data from Prometheus using HTTPS and needs to request a login and password to write/read the metrics. Details are available [in this article](https://nordicapis.com/api-monitoring-with-prometheus-grafana-alertmanager-and-victoriametrics/).
> We evaluated VictoriaMetrics, InfluxDB OpenSource and Enterprise, ElasticSearch, Thanos, Cortex, TimescaleDB/PostgreSQL and M3DB. We selected VictoriaMetrics because it has [good community support](https://slack.victoriametrics.com/), [good documentation](https://docs.victoriametrics.com/) and it just works.
> We started using VictoriaMetrics in the production environment days before the start of BlackFriday in 2020, the period of greatest use of the Sensedia API-Platform by customers. There was a record in the generation of metrics and there was no instability with the monitoring stack.
> We use VictoriaMetrics in cluster mode for centralized storage of metrics collected by several Prometheus servers installed in Kubernetes clusters from two different cloud providers. VictoriaMetrics has also been integrated with Grafana to view metrics.
[Aecio dos Santos Pires](http://aeciopires.com), Cloud Architect, Sensedia.
> [Wedos](https://www.wedos.com/) is the biggest hosting provider in the Czech Republic. We have our own private data center that holds our servers and technologies. We are in the process of building a second, stae of the art data center where the servers will be cooled in an oil bath. We started using [cluster VictoriaMetrics](https://docs.victoriametrics.com/Cluster-VictoriaMetrics.html) to store Prometheus metrics from all our infrastructure after receiving positive references from people who had successfully used VictoriaMetrics.
> We like that VictoriaMetrics is simple to configuree and requires zero maintenance. It works right out of the box and once it's set up you can just forget about it.
> We needed to redesign our metrics infrastructure from the ground up after the move to Kubernetes. We had tried out a few different options before landing on this solution which is working great. We have a Prometheus instance in every datacenter with 2 hours retention for local storage and remote write into [HA pair of single-node VictoriaMetrics instances](https://docs.victoriametrics.com/Single-server-VictoriaMetrics.html#high-availability).
> Optimizing for those points and our specific workload, VictoriaMetrics proved to be the best option. As icing on the cake we’ve got [PromQL extensions](https://docs.victoriametrics.com/MetricsQL.html) - `default 0` and `histogram` are my favorite ones. We really like having a lot of tsdb params easily available via config options which makes tsdb easy to tune for each specific use case. We've also found a great community in [Slack channel](https://slack.victoriametrics.com/) and responsive and helpful maintainer support.
Please see [Monitoring K8S with VictoriaMetrics](https://docs.google.com/presentation/d/1g7yUyVEaAp4tPuRy-MZbPXKqJ1z78_5VKuV841aQfsg/edit) slides,
[video](https://youtu.be/ZJQYW-cFOms) and [Infrastructure monitoring with Prometheus at Zerodha](https://zerodha.tech/blog/infra-monitoring-at-zerodha/) blog post for more details.
[zhihu](https://www.zhihu.com) is the largest Chinese question-and-answer website. We use VictoriaMetrics to store and use Graphite metrics. We shared the [promate](https://github.com/zhihu/promate) solution in our [单机 20 亿指标，知乎 Graphite 极致优化！](https://qcon.infoq.cn/2020/shenzhen/presentation/2881)([slides](https://static001.geekbang.org/con/76/pdf/828698018/file/%E5%8D%95%E6%9C%BA%2020%20%E4%BA%BF%E6%8C%87%E6%A0%87%EF%BC%8C%E7%9F%A5%E4%B9%8E%20Graphite%20%E6%9E%81%E8%87%B4%E4%BC%98%E5%8C%96%EF%BC%81-%E7%86%8A%E8%B1%B9.pdf)) talk at [QCon 2020](https://qcon.infoq.cn/2020/shenzhen/).
