# Every Metric, Every Label. No Cardinality Cliff. > Monitor system metrics and KPIs in real time. Collect, analyze, and visualize time-series data with OpenObserve's metrics platform. Start free. Source: https://openobserve.ai/metrics/ --- Monitor your infrastructure and applications with Prometheus-native metrics and efficient time-series storage. - [Start Free Cloud Trial](https://cloud.openobserve.ai/web/login/) - [Talk to a Human](/demo/) ### Correlated Telemetry Pivot from a metric spike straight into the logs and traces behind it, no second tool. ### PromQL or SQL, Your Choice Query the same metrics either way, no forced migration. ### OTel-native Metrics Standardized collection with zero vendor lock-in ## Metrics That Don’t Fall Over at Scale Collect and query time-series data without hitting a cardinality wall. ### Metrics Collection & Analysis - **Effortless Collection** - Ingest metrics from any source with minimal overhead via Prometheus remote-write, OpenTelemetry, or direct API integration. - **Real-Time Insights** - Visualize and analyze metrics instantly without cardinality limits, enabling immediate detection of performance trends. [Learn More](https://openobserve.ai/docs/ingestion/metrics/) ### Prometheus Integration - **Native Compatibility** - Connect with Prometheus endpoints using full remote write protocol support and your existing configurations. - **Flexible Querying** - Use familiar PromQL syntax or switch to SQL, your team’s expertise remains valuable without new learning curves. [Learn More](https://openobserve.ai/docs/ingestion/metrics/prometheus/) ### Metrics Processing Advanced - **Aggregations** - Process time-series data with powerful calculations including rates, moving averages, and percentile analysis using either PromQL or SQL. - **Downsampling** - Optimize query performance for historical data through automatic downsampling of longer period requests. [Learn More](https://openobserve.ai/docs/user-guide/data-exploration/rum/metrics-reference/) ### Optimized Storage - **Columnar Storage** - Cut storage costs while maintaining query speed through columnar format designed for time-series data. - **Configurable Retention** - Define custom retention periods for each data source, ensuring you keep only what you need for as long as you need it. [Learn More](https://openobserve.ai/docs/administration/maintenance/storage-management/) ## Measured against industry leaders Same telemetry, same workloads, one platform. Every number is OpenObserve against a named vendor - not an industry average. 8x cost reduction means you can unify your observability into a single platform. 140x storage means longer retention doesn't necessarily mean expensive bills. 5x to 15x faster queries mean dashboards load in milliseconds, not minutes. See how much you would save switching today. - [See all comparisons](/comparison/) ## Teams trust OpenObserve with their metrics ## Metrics FAQs ### What is the best long-term storage for Prometheus metrics? OpenObserve works as a long-term storage backend for Prometheus via the remote-write protocol, retaining full-resolution metrics without the cardinality limits that cause most self-managed Prometheus setups to fall over at scale. Data is stored in columnar Parquet format on object storage, keeping months or years of metrics affordable instead of forcing aggressive pre-aggregation - while automatic downsampling keeps long-range historical queries fast. Your existing Prometheus configuration keeps working: remote-write ingestion, PromQL queries, label consistency, and all standard metric types are supported, and the same series can also be queried with SQL when an investigation needs joins or complex aggregations. Because metrics live beside logs and traces in one engine, long-term capacity analysis and incident investigation happen in the same place. ### How do I migrate Prometheus to a scalable backend? Configure your existing Prometheus servers to remote-write to OpenObserve’s ingestion endpoint, no changes to your scrape configs or PromQL queries required. Dashboards and alerts built for Prometheus continue working unchanged after migration. ### How does OpenObserve handle high-cardinality metrics? The platform implements specific optimizations for high-cardinality metrics: - OpenObserve stores data in parquet format and does not suffer from high cardinality issues - Efficient label indexing and compression techniques - Intelligent caching and query optimization to maintain performance ### What query capabilities are available for metrics? OpenObserve provides a dual query interface for metrics: - PromQL queries for compatibility with Prometheus workflows - SQL for more complex analytics - Support for time-based functions, mathematical operations, and advanced aggregations - Cross-metric calculations - Functions like rate and increase - Complex metric expressions ### How does Prometheus compatibility work? The platform provides native Prometheus compatibility: - Support for prometheus remote write protocol to accept metrics from Prometheus agents - PromQL support for queries - Maintenance of label consistency with Prometheus metrics - Support for all standard Prometheus metric types In practice, you point your existing Prometheus agents or the OpenTelemetry Collector at OpenObserve's remote-write endpoint, and dashboards, recording rules, and alert expressions written in PromQL keep working unchanged. The same metrics also become queryable with SQL, and storage moves to columnar Parquet on object storage, where high-cardinality series no longer threaten cluster stability. No forced migration, no proprietary agent, and no new query language for the team to learn. ### How does OpenObserve collect metrics? OpenObserve supports multiple collection methods for metrics: *Metrics collection through Prometheus remote write - Metrics collection via OTLP protocol using OTel Collector - Custom metrics ingestion through HTTP API endpoints - Infrastructure metrics collection through various integrations, including cloud provider services and the OpenObserve collector ## Explore guides, videos, and articles to help you get the most out of Metrics. ### Azure Monitor Metrics Collection [Learn more](https://openobserve.ai/blog/azure-monitor-metrics/) ### Monitor AWS Resources [Learn more](https://openobserve.ai/blog/how-to-monitor-all-aws-metrics-in-one-place/) ### Kubernetes Metrics Monitoring [Learn more](https://openobserve.ai/blog/send-metrics-using-kube-prometheus-stack-to-openobserve/) - [Explore All Blogs](/blog/) ## Ready to get started? Try OpenObserve today for more efficient and performant observability. - Get Started For Free - [Schedule Demo](/demo/)