VictoriaMetrics

2 reviews

Offload non-trivial operational tasks for metrics storage such as replication, backup and seamless scalability to VictoriaMetrics. Query all the metrics your Prometheus instances collect via a single Prometheus-compatible datasource. Fast query engine. It excels on heavy queries over thousands of metrics with millions of metric values. VictoriaMetrics supports native PromQL. There is no need in learning yet another query language.

VictoriaLogs

0 reviews

VictoriaLogs, an open-source log database from VictoriaMetrics, is designed to be user-friendly. It seamlessly integrates with widely used log collectors and offers a more straightforward setup process than Elasticsearch and Grafana Loki. The robust LogsQL query language provides full-text search capabilities across all log fields, simplifying log management. It scales impressively with CPU, RAM, disk IO, and space, running efficiently on Raspberry Pi and high-end servers. It handles data volumes up to 30 times larger than Elasticsearch and Grafana Loki on the same hardware, making it a powerful choice for various environments. VictoriaLogs supports fast full-text search over high-cardinality log fields like trace_id, user_id, and IP. It works seamlessly with traditional Unix log analysis tools like grep, less, sort, and jq. It also offers multi-tenancy support, accommodating diverse needs.

VictoriaMetrics Enterprise

0 reviews

VictoriaMetrics Enterprise is a commercial solution designed by the creators of VictoriaMetrics for complex monitoring and observability setups. It's ideal for organizations with mission-critical, large, or rapidly scaling monitoring environments. The Enterprise version includes all the features of the community edition plus additional enhancements such as Downsampling Automated Backups / Backup Manager Data Retention per Label/Tenant Multi-Tenant Statistic & Rate Limiting Anomaly Detection. It offers stable releases with long-term support, ensuring critical bug fixes and security patches. Enterprise security compliance and prioritized feature requests are also part of the package. We help you to reduce storage costs and boost performance for historical data queries. Multiple retentions allow different storage durations for various datasets. Automatic discovery of storage nodes updates the list at insert and vmselect without restarting services. The Anomaly Detection Service automates and simplifies alerting rules, handling complex anomalies in metrics data.

VictoriaMetrics Cloud

0 reviews

VictoriaMetrics Cloud allows users to run the Enterprise version of VictoriaMetrics, hosted on AWS, without the need to perform typical DevOps tasks such as proper configuration, monitoring, log collection, access protection, software updates, and backups. We run Managed VictoriaMetrics instances in our environment on AWS and provide easy-to-use endpoints for data ingestion and querying. The VictoriaMetrics team takes care of optimal configuration and software maintenance. It comes with the following features: It can be used as a Managed Prometheus - configure Prometheus or Vmagent to write data to Managed VictoriaMetrics and then use the provided endpoint as a Prometheus data source in Grafana; Every Managed VictoriaMetrics instance runs in an isolated environment, so instances cannot interfere with each other; Managed VictoriaMetrics instance can be scaled up or scaled down in a few clicks; Automated backups; Pay only for the resources used - computing, storage, and traffic.

VictoriaMetrics Anomaly Detection

0 reviews

VictoriaMetrics Anomaly Detection is a service that continuously scans time series stored in VictoriaMetrics and detects unexpected changes within data patterns in real time. It does so by utilizing user-configurable machine learning models. In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, a part of our Enterprise offering, is a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to detect anomalies and minimize false positives, thus reducing alert fatigue. Providing simplified alerting mechanisms atop unified anomaly scores enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.