# Distributed Traces, End to End. Every Span Retained. > Monitor distributed systems with end-to-end request tracing. OpenTelemetry integration, service dependency maps, and columnar storage. Start free. Source: https://openobserve.ai/traces/ --- Gain end-to-end visibility into your distributed systems, powered by OpenTelemetry. - [Start Free Cloud Trial](https://cloud.openobserve.ai/web/login/) - [Talk to a Human](/demo/) ### Correlated Telemetry Visualize and troubleshoot metrics, logs, and traces in a single pane of glass. ### 70% Less Storage Than Elasticsearch Apache Parquet compression without losing query speed. ### OTel-Native Traces Standardized collection with zero vendor lock-in. ## Follow a Request Wherever It Goes ### Trace Collection - **OpenTelemetry Integration** - Collect traces through native OTLP. Instrument applications once and export traces directly to OpenObserve, no vendor lock-in. - **Auto-Instrumentation** - Capture distributed traces using zero-code auto-instrumentation configured by the OpenObserve collector. [Learn More](https://openobserve.ai/docs/ingestion/traces/) ### Service Analysis - **Dependency Mapping** - See your microservices in action. Real-time service maps reveal how your systems truly connect. - **Performance Insights** - Spot troublemakers instantly with latency heatmaps and error trend analysis. Compare trace durations across versions to catch performance drops before users do. [Learn More](https://openobserve.ai/docs/user-guide/data-exploration/traces/service-graph/) ### Trace Analysis - **Detailed Spans** - Dive deep into every operation, examine spans with precise timing, tags, and logs. One click takes you from overview metrics to the exact problematic trace span. - **Context Propagation** - Follow requests anywhere with W3C trace context support. Visualize complete transaction journeys through your stack with flame graphs and waterfall diagrams. [Learn More](https://openobserve.ai/docs/user-guide/data-exploration/traces/traces/) ### Optimized Storage - **Efficient Storage** - Slash storage costs to 1/140th of Elasticsearch with Apache Parquet’s columnar format, exceptional compression without sacrificing query performance. - **Flexible Retention** - Set retention policies your way, no complex tiered storage needed. OpenObserve leverages object storage as your hot tier. [Learn More](https://openobserve.ai/docs/user-guide/data-processing/streams/stream-details/) ## 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 traces ## Traces FAQs ### What is the best OpenTelemetry backend? OpenObserve is a fully OpenTelemetry-native backend, accepting OTLP over HTTP or gRPC from any SDK or collector with no proprietary agent required. It stores traces in Apache Parquet, cutting storage costs by up to 140x versus Elasticsearch while keeping every span queryable with SQL - efficient enough that full-fidelity retention replaces sampling, so the trace you need during an incident is actually there. Spans arrive with W3C trace context intact, powering real-time service dependency maps, latency heatmaps, flame graphs, and waterfall views out of the box. And because traces share one engine with logs and metrics, a slow span links directly to the log lines it emitted and the metrics it moved, turning trace analysis into full-stack root cause analysis rather than an isolated view. ### How do I get distributed tracing without vendor lock-in? Instrument your services with the standard OpenTelemetry SDK and point the exporter at OpenObserve. Because OTLP is a vendor-neutral protocol, your instrumentation code never changes if you switch backends later - the exporter endpoint is the only thing that moves. Zero-code auto-instrumentation via the OpenObserve collector covers common frameworks without touching application code, and W3C trace-context propagation keeps traces intact across service boundaries regardless of language. Since OpenObserve itself is open source (AGPL-3.0), the backend is as portable as the instrumentation: self-host it, run it in your own cloud, or use the managed service - with your data in open Parquet format on object storage you control rather than a proprietary store. ### How do I correlate frontend errors with backend traces? OpenObserve’s RUM SDK and backend tracing share the same trace context, so a frontend error automatically links to the backend trace and span that caused it. From a session replay, one click takes you into the exact backend request that failed. ### How does OpenObserve collect traces? OpenObserve collects distributed traces through OpenTelemetry instrumentation. The platform accepts traces via the OTLP protocol, supporting both manual and automatic instrumentation methods. For services already instrumented with OpenTelemetry, you can configure the exporter to send traces directly to OpenObserve. The system maintains trace context across service boundaries using W3C trace context standards. ### What retention and storage options are available? OpenObserve optimizes trace storage using columnar format and efficient compression. Retention periods are configurable per data source, allowing you to balance storage costs with analysis needs. The system supports tiered storage for different trace retention requirements. Historical traces can be accessed efficiently through the query interface while maintaining quick access to recent data. ### How does service dependency analysis work? The platform automatically generates service dependency information from trace data. Service maps show both direct and indirect dependencies between components. The system calculates key metrics like request rates, error rates, and latency percentiles for each service interaction. You can analyze dependency changes over time and identify critical paths in your architecture. ## Explore guides, videos, and articles to help you get the most out of Traces. ### Traces in OpenObserve [Learn more](https://openobserve.ai/docs/user-guide/data-exploration/traces/traces/) ### What is Distributed Tracing? [Learn more](https://openobserve.ai/blog/distributed-tracing-basics-to-beyond-guide/) ### Auto Instrumentation & OpenTelemetry [Learn more](https://openobserve.ai/blog/how-to-auto-instrument-distributed-services/) - [Explore All Blogs](/blog/) ## Ready to get started? Try OpenObserve today for more efficient and performant observability. - Get Started For Free - [Schedule Demo](/demo/)