Metrics
Get real-time infrastructure and application insights with Prometheus-native compatibility and efficient time-series storage.
Why Use OpenObserve for Metrics?
Transform your infrastructure and application monitoring with efficient collection, storage, and analysis capabilities designed for modern cloud-native environments.
Metrics Collection & Analysis
Efficient Collection
Collect metrics from any source with minimal overhead through Prometheus remote-write, OpenTelemetry, or direct API calls.
Real-Time Analysis
Process and visualize metrics instantly as they arrive, enabling immediate insights into system behavior and performance trends without worrying about cardinality.
Prometheus Integration
Native Compatibility
Seamlessly collect metrics from Prometheus endpoints with full support for remote write protocol and existing Prometheus configurations.
Familiar Querying
Leverage your existing PromQL knowledge with native support for PromQL, ensuring a smooth transition for your teams. You could also use SQL.
Metrics Processing
Advanced Aggregations
Perform sophisticated calculations on time-series data with support for rate calculations, moving averages, and percentile-based analysis. Use PromQL or SQL.
Downsamping
Downsample longer period queries to improve performance.
Optimized Storage
Columnar Storage
Optimize metric storage with advanced columnar format designed specifically for time-series data, reducing costs while maintaining query performance.
Configurable Retention
Tailor retention policies to fit your specific needs by defining how long metrics are stored for each data source.
Get Started with Metrics
Begin monitoring your application and infrastructure with OpenObserve. Start with the free tier or schedule a demo.
Openobserve Cloud Free Tier
Monthly Limits:
Ingestion - 50 GB logs, 50 GB metrics , 50 GB traces- Query volume - 200 GB
- Pipelines - 50 GB of Data Processing
- 1K RUM & Session Replay
- 1K Action Script Runs
- 3 Users
- 7-Days Retention
Get started in minutes—no credit card required.
Metrics FAQs
How does OpenObserve collect metrics?
OpenObserve supports multiple collection methods for metrics: *Metrics collection through Prometheus remote write
- Metrics collection via OTLP protocol using otelc-collector
- Custom metrics ingestion through HTTP API endpoints
- Infrastructure metrics collection through various integrations, including cloud provider services and the OpenObserve collector
What types of metrics are supported?
OpenObserve handles various metric types:
- Standard metric types: counters, gauges, summary and histograms
- Prometheus-compatible metric types and aggregation methods
- System-level metrics (CPU, memory, disk)
- Application metrics (request rates, latencies, error rates)
- Custom metrics using any numeric data type with timestamps
How does metric processing work?
The metric processing pipeline in OpenObserve is optimized for time-series data:
- Real-time processing of incoming metrics using downsampling or pipelines
- Storage in a columnar format optimized for time-series queries
- Support for various aggregation functions (rate calculations, moving averages, percentiles)
- Efficient indexing of labels and tags for fast filtering and grouping operations
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 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 visualization options are available?
OpenObserve offers comprehensive visualization options for metrics:
- Time-series graphs, gauges, and heat maps
- Various panel types optimized for different metric visualizations
- Dynamic time ranges, zoom capabilities, and legend options
- Template variables for reusable dashboards
- Automatic refresh intervals
- Annotations for highlighting specific events in time
- Drill down and correlation between logs, metrics and traces
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
What aggregation and alerting features are available?
OpenObserve provides comprehensive aggregation and alerting capabilities:
- Rolling aggregations and downsampling rules
- Custom metric transformations
- Threshold-based alerts and trend analysis
- Integration with various notification channels
- Detailed metric context in alerts
Want to learn more? Check out our blog.
Explore metrics monitoring best practices and OpenObserve capabilities.
