Top OpenTelemetry Backends for Storage & Visualization

OpenTelemetry backends provide storage, analysis, and visualization for telemetry data (traces, metrics, logs). This guide lists available OpenTelemetry-compliant backend options, categorized by use case: APM platforms, storage backends, visualization tools, and distributed tracing systems. For detailed comparison, see OpenTelemetry Backend Comparison.

OpenTelemetry Backend Categories

Choosing an OpenTelemetry backend depends on your monitoring and observability requirements, current infrastructure, scalability needs, and budget constraints.

What is OpenTelemetry?

OpenTelemetry is an open source observability framework that provides a unified approach to collecting, processing, and exporting telemetry data from applications.

OpenTelemetry simplifies code instrumentation and observability data collection, including metrics, logs, and traces, across all programming languages, frameworks, and infrastructures.

Why does OpenTelemetry need a backend?

OpenTelemetry is a framework that collects telemetry data from applications, including traces, logs, and metrics. It does not, however, include built-in storage or analysis capabilities for this data.

OpenTelemetry requires a backend system to store, process, analyze, and visualize telemetry data collected from applications. The backend is a critical component in the observability pipeline, allowing for insights and actions based on collected telemetry data.

A backend receives, stores, and analyzes the telemetry data collected by OpenTelemetry. It serves as a central hub for data storage and processing, enabling you to collect, analyze, visualize, and gain insights from the telemetry data produced by your applications.

Choosing the right backend

OpenTelemetry does not have a built-in backend. Instead, it allows you to export data to various backend systems and services to store and analyze telemetry data.

There are several factors to consider when choosing an OpenTelemetry backend:

• Feature Set. Evaluate the metrics, traces, and logs offered by each backend. Consider if the backend meets your monitoring and observability needs, including distributed tracing, metric aggregation, anomaly detection, and log analysis capabilities.
• Data storage. Telemetry data generated by applications can be voluminous, especially in large-scale distributed systems. Storing this data efficiently and reliably requires a dedicated backend system that can handle the data volume and provide the necessary durability.
• Querying and analysis. Evaluate the backend's query and analysis capabilities. Look for features that allow you to perform complex queries, aggregations, and filtering on the telemetry data. Consider whether the backend supports your organization's specific analysis and visualization requirements.
• Visualization and alerting. A backend system usually offers visualization and dashboarding features to present telemetry data in a clear and actionable way. This allows you to monitor application performance, track health metrics, and set alerts based on predefined thresholds or conditions.
• Integrations and plugins. In many cases, you may already have existing monitoring, logging, or analytics systems in your infrastructure. The backend system can integrate with these tools, allowing you to consolidate telemetry data with other observability data sources and leverage existing workflows and processes.

By carefully considering these factors, you can select an OpenTelemetry backend that meets your organization's observability goals, scalability requirements, and integration needs, ultimately enabling you to effectively monitor and analyze your telemetry data.

Uptrace

Uptrace is an OpenTelemetry APM built from the ground up to fully embrace OpenTelemetry specification and guidelines.

Uptrace provides end-to-end visibility into requests flowing through multiple services, enabling performance analysis, root cause identification, and performance optimization.

Uptrace is an open source project, which means it is freely available and encourages community contributions and enhancements. It benefits from an active community of developers who contribute to its development, maintenance and support.

Uptrace supports the OpenTelemetry standard, enabling easy integration with OpenTelemetry instrumented applications. It also provides instrumentation libraries and plugins for various frameworks and languages, facilitating the collection of tracing data.

Prometheus

Prometheus is a widely used open-source monitoring system that provides a time-series database for storing metrics data.

By combining Prometheus with OpenTelemetry, you can collect and store metrics from your applications using OpenTelemetry's instrumentation libraries, and send that data to Prometheus for storage and analysis.

Prometheus provides powerful query and alerting capabilities, allowing you to perform complex queries on the collected metrics data and set up alerts based on predefined conditions or thresholds. It also provides a rich ecosystem of exporters, integrations, and visualization tools, such as Grafana, to visualize and explore the collected metrics.

By integrating Prometheus with OpenTelemetry, you can leverage the flexibility and extensibility of OpenTelemetry to instrument your applications, while benefiting from the robust monitoring and analysis capabilities of Prometheus.

• OpenTelemetry Prometheus metrics
• DataDog vs Prometheus

Grafana

Grafana is a popular open source visualization and analytics platform that can be used to visualize and explore telemetry data collected by OpenTelemetry.

By integrating Grafana with OpenTelemetry, you can use Grafana's powerful visualization capabilities to create interactive dashboards and explore the telemetry data collected by OpenTelemetry.

Grafana provides an easy-to-use interface for creating custom visualizations, charts, and graphs to gain insight and monitor the performance of your applications.

• Grafana alternatives

Jaeger

Jaeger is an open source distributed tracing system that integrates with OpenTelemetry to collect and analyze trace data. For a comprehensive comparison of Jaeger with other distributed tracing tools, see our detailed guide.

By integrating Jaeger with OpenTelemetry, you can leverage the power of distributed tracing for troubleshooting, performance optimization, and root cause analysis of your applications.

Jaeger provides end-to-end visibility into the execution path of requests, making it easier to identify performance bottlenecks, latency issues, or errors across your distributed systems.

Elasticsearch

Elasticsearch is a scalable search and analytics engine that can be used to store and analyze logs and other telemetry data collected by OpenTelemetry.

You can configure OpenTelemetry to send log and metric data to Elasticsearch, where it can be indexed and stored in a scalable and distributed manner.

Elasticsearch provides powerful query and search capabilities that allow you to efficiently search and retrieve log data based on various criteria, such as time range, keywords, or specific fields.

Zipkin

Zipkin is another distributed tracing system that can be integrated with OpenTelemetry to collect and analyze trace data.

OpenTelemetry provides instrumentation libraries that enable you to generate trace data from your applications. These traces represent the execution flow and timing information of requests as they propagate through various services and components.

Zipkin serves as the backend system for collecting, storing, and visualizing these traces. OpenTelemetry exporters can send the generated trace data to Zipkin, where it can be stored and indexed for further analysis.

Storage Backends for OpenTelemetry Data

OpenTelemetry requires dedicated storage backends to persist telemetry data at scale. The choice of storage backend impacts query performance, retention costs, and compression efficiency.

ClickHouse

ClickHouse is a high-performance columnar database optimized for OLAP workloads and time-series data. It provides exceptional compression ratios (90%+) and query speeds for OpenTelemetry traces, metrics, and logs.

Key Benefits:

• 10x faster queries compared to Elasticsearch for time-series data
• Excellent compression (90%) reduces storage costs significantly
• Handles billions of events per day on a single server
• Native support for time-series queries and aggregations

Used by: Uptrace, SigNoz, OpenObserve, Grafana Tempo

TimescaleDB

TimescaleDB extends PostgreSQL with time-series capabilities, making it suitable for storing OpenTelemetry metrics and traces alongside relational data.

Key Benefits:

• Familiar PostgreSQL interface and SQL queries
• Automatic partitioning by time for efficient queries
• Compression and retention policies built-in
• ACID compliance for critical telemetry data

Best for: Organizations already using PostgreSQL who want time-series capabilities

VictoriaMetrics

VictoriaMetrics is a fast, cost-effective time-series database designed for Prometheus-compatible metrics storage at scale.

Key Benefits:

• High-performance metrics storage with minimal resource usage
• Prometheus-compatible, drop-in replacement
• Advanced downsampling and retention policies
• Handles millions of active time series

Best for: Metrics-heavy workloads requiring Prometheus compatibility

OpenTelemetry Visualization Tools

Visualization tools transform raw telemetry data into actionable insights through dashboards, graphs, and trace timelines.

Grafana

Grafana remains the most popular visualization tool for OpenTelemetry metrics, logs, and traces. It connects to multiple data sources including Prometheus, ClickHouse, and Jaeger.

Key Features:

• Universal dashboarding for all telemetry signals
• 150+ data source plugins including OpenTelemetry backends
• Alerting, annotations, and collaboration features
• Explore mode for ad-hoc queries

Best for: Teams needing flexible visualization across multiple data sources

Kibana

Kibana provides visualization and exploration for data stored in Elasticsearch, including OpenTelemetry logs and traces exported via Elasticsearch exporter.

Key Features:

• Native Elasticsearch integration
• Advanced log search and filtering
• Machine learning for anomaly detection
• Canvas and reporting capabilities

Best for: Organizations using Elasticsearch for log aggregation

Uptrace UI

Uptrace provides a unified interface for traces, metrics, and logs with automatic correlation between signals. Unlike generic visualization tools, it's purpose-built for OpenTelemetry data.

Key Features:

• Intuitive query builder with SQL support
• Automatic trace-to-log correlation
• Service dependency mapping
• Built-in alerting and anomaly detection

Best for: Teams wanting purpose-built OpenTelemetry visualization without configuration overhead

Who Provides OpenTelemetry-Compliant Backend Storage?

Open Source Solutions:

• Uptrace - All-in-one APM with ClickHouse storage
• Jaeger - Distributed tracing with Elasticsearch/Cassandra storage
• SigNoz - OpenTelemetry-native platform with ClickHouse
• Grafana Tempo - Trace storage with object storage backends (S3, GCS)
• Prometheus - Metrics-focused time-series database

Commercial Providers:

• Datadog - Full-stack observability with proprietary storage
• New Relic - Cloud-native observability platform
• Honeycomb - Observability platform optimized for high-cardinality data
• Lightstep - Enterprise observability with streaming architecture
• Splunk - Enterprise data platform with OpenTelemetry support

Cloud Provider Options:

• AWS X-Ray - Managed distributed tracing service
• Google Cloud Trace - Trace collection and analysis
• Azure Monitor - Integrated monitoring and logging

Best Backend for Storing OpenTelemetry Data at Scale

For large-scale OpenTelemetry deployments (100M+ spans/day), choose backends with:

1. ClickHouse-based solutions (Uptrace, SigNoz) - Best compression and query speed
2. Object storage backends (Grafana Tempo with S3/GCS) - Cost-effective for long-term retention
3. Distributed Elasticsearch - If already using ELK stack infrastructure
4. Managed cloud services - For teams without dedicated infrastructure expertise

Scalability considerations:

• Ingestion rate - Can the backend handle your peak spans/second?
• Storage efficiency - What's the compression ratio and retention cost?
• Query performance - How fast are complex queries on large datasets?
• Operational complexity - Do you have expertise to maintain the backend?

FAQ

What is an OpenTelemetry backend? An OpenTelemetry backend is a storage and analysis system that receives, stores, and processes telemetry data (traces, metrics, logs) exported from OpenTelemetry SDKs and Collectors. OpenTelemetry itself doesn't store data - it only collects and exports it to compatible backends like Uptrace, Jaeger, Prometheus, or commercial platforms.

Which OpenTelemetry backend should I use? Choose based on your needs: Uptrace for all-in-one observability with ClickHouse performance, Jaeger for distributed tracing only, Prometheus for metrics-focused monitoring, or commercial options like Datadog for enterprise support. Consider your scalability requirements, team expertise, and budget constraints when selecting a backend.

What's the easiest place to store OpenTelemetry data? Uptrace offers the easiest setup with single DSN configuration and 5-minute deployment. Managed cloud services (AWS X-Ray, Google Cloud Trace, Datadog) are also simple but have vendor lock-in. For self-hosted, SigNoz provides Docker Compose setup for quick local deployment.

Is OpenTelemetry free? Yes, OpenTelemetry is a free, open-source framework under CNCF. However, backend storage and visualization tools have varying costs: open-source options (Uptrace, Jaeger, Prometheus) are free but require infrastructure, while commercial backends (Datadog, New Relic) charge based on data volume and features.

What are the best tools for OpenTelemetry data visualization? Best visualization tools include: Grafana (universal dashboarding for all data sources), Uptrace UI (purpose-built for OpenTelemetry with automatic correlation), Kibana (Elasticsearch-focused), and Jaeger UI (distributed tracing). Grafana offers the most flexibility across backends, while Uptrace provides the best OpenTelemetry-native experience.

How to choose between ClickHouse and Elasticsearch for OpenTelemetry storage? ClickHouse offers 90% compression and 10x faster queries for time-series data, ideal for high-volume traces and metrics. Elasticsearch provides better full-text search for logs and has a mature ecosystem. Choose ClickHouse for performance and cost efficiency (Uptrace, SigNoz), Elasticsearch if you need advanced log search or already run ELK stack.

Can I use multiple OpenTelemetry backends simultaneously? Yes, OpenTelemetry Collector supports multiple exporters, allowing you to send data to multiple backends (e.g., metrics to Prometheus, traces to Jaeger, logs to Elasticsearch). This provides flexibility but increases infrastructure complexity and costs. Many teams consolidate to all-in-one solutions like Uptrace to simplify operations.

What's the difference between OpenTelemetry backend and APM? OpenTelemetry backend refers to the storage and analysis layer (like ClickHouse, Prometheus). APM (Application Performance Monitoring) is a complete platform combining storage, visualization, and analysis (like Uptrace, Datadog). Some backends are storage-only (Prometheus), while APMs provide end-to-end observability including dashboards and alerting.

Conclusion

OpenTelemetry provides a vendor-agnostic solution, enabling you to select the backend that best fits your requirements. This ensures data portability and facilitates migration to alternative systems if necessary in the future.

Key takeaways:

• All-in-one platforms (Uptrace, SigNoz) simplify operations with unified storage
• Storage backends (ClickHouse, Prometheus) offer best performance for specific data types
• Visualization tools (Grafana, Kibana) provide flexible dashboarding across sources
• Choose backends based on your scalability, budget, and team expertise requirements