Sentry and VictoriaMetrics are direct competitors in the application monitoring and data storage market. Sentry has the upper hand in error tracking, while VictoriaMetrics leads in managing large-scale metrics data.
Features: Sentry is distinguished by real-time error tracking, contextual insights, and integration with development tools. VictoriaMetrics prioritizes efficient data ingestion, a compact storage engine, and rapid querying.
Ease of Deployment and Customer Service: Sentry offers easy deployment, detailed documentation, and strong community support. VictoriaMetrics has straightforward deployment options across platforms but lacks a comprehensive support structure.
Pricing and ROI: Sentry's tiered subscription model ensures predictable costs and ROI through reduced downtime. VictoriaMetrics provides a cost-effective, open-source solution, optimizing ROI by lowering infrastructure costs.
I have seen a return on investment with VictoriaMetrics, saving 40% in storage costs compared to Prometheus, reducing query latency by 60%, and cutting maintenance time by half.
time was saved due to simplicity and a shorter learning curve compared to other tools.
Because VictoriaMetrics runs more efficiently and much faster, debugging an issue or understanding system performance is much easier.
We have not contacted their technical support because everything is easy to set up under Sentry.
We reached out once or twice for minor issues during the initial setup and integration, and they were helpful in resolving the issues.
VictoriaMetrics customer support is exceptional.
We use the open-source version of VictoriaMetrics, so we did not require any customer support.
It has been easy to use and configure across multiple systems, each having several environments.
It handles large volumes of time series data efficiently, using less memory and disk compared to Prometheus or Thanos.
We deploy and embed it in our Helm chart and it works out of the box.
VictoriaMetrics has handled increases in data and users smoothly so far.
In terms of accuracy and reliability, VictoriaMetrics's output is pretty sufficient and works very well in a native Kubernetes setup, which I appreciate.
The components are simple, restarts are fast, and stateless parts can be replaced without data loss.
Regarding stability, I would rate it a ten out of ten.
Integrations or single sign-on capability with Microsoft would be beneficial for securing all assets.
For our use cases, it is a really good offering, and while we would be interested to see what else it brings in the future, at this point in time it checks all our boxes and we are quite satisfied with it.
VictoriaMetrics is quite a good project and quite easy to use overall.
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.
Compared to New Relic, it provides the necessary features at a cheaper cost, especially since we moved infrastructure monitoring to Azure.
We did not require any license for VictoriaMetrics since we are using the open-source version.
I used the free open-source version, and it was sufficient for my needs.
My experience with pricing, setup cost, and licensing for VictoriaMetrics is positive since it is open-source under Apache 2.0, so there is no licensing cost.
Real-time error tracking helps our Quality Assurance team easily identify the root causes of problems or bugs and promptly inform the developers about specific issues.
At this time, I focus on finding and fixing bugs.
Sentry provides real-time error tracking which is invaluable for identifying and resolving issues quickly.
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.
In my experience, the best features VictoriaMetrics offers include that VM agent does its work very efficiently and does not use a lot of resources.
Its compact storage and fast query performance make it ideal for long-term metric storage.
| Product | Mindshare (%) |
|---|---|
| VictoriaMetrics | 0.3% |
| Sentry | 1.4% |
| Other | 98.3% |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 3 |
| Large Enterprise | 3 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 2 |
Sentry provides developers with real-time error tracking and performance metrics, facilitating efficient monitoring and logging across front-end and back-end systems. The platform enhances error visibility without direct production access, ensuring robust data protection.
Sentry offers a comprehensive integration with Git repositories, allowing users to gain context for errors and manage performance metrics across their entire tech stack. Its user-friendly interface, coupled with features like security and session replays, aids in rapid learning and error resolution. Support for platforms like Azure Web Services and seamless integration with communication tools further enhances its functionalities. The tool is appreciated for error and performance reporting, with integrations extending to platforms such as Slack and AWS Lambda. Users, however, seek improved integration with additional tools, enhanced tracing capabilities, task automation, and better customization for event handling.
What are the key features of Sentry?Sentry finds application across industries like banking, where it's deployed to monitor application performance and manage errors both on-premises and in the cloud. Teams benefit from its ability to trace and rectify errors while preserving system reliability and ensuring controlled access to databases.
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: Over 1B Docker pulls, 19M GitHub downloads, and 27K+ GitHub stars. Simple, reliable, and efficient observability for everyone.
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.
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