Uptrace
Uptrace is an Observability Platform (OP) (observability, application performance monitoring) built around OpenTelemetry (OTel) that provides distributed tracing, metrics, and logs correlation for monitoring backend services, microservices, and infrastructure.
- Distributed tracing for microservices and backend applications (application performance monitoring)
- Ingestion and analysis of OTel traces, metrics, and logs (observability)
- Performance dashboards, service maps, and query UI for telemetry data (monitoring and analytics)
- Error tracking, latency analysis, and root-cause investigation workflows (incident analysis)
- Support for multiple languages and frameworks via OTel SDKs and instrumentation (telemetry integration)
More About Uptrace
Uptrace is an OP (observability) focused on collecting, storing, and analyzing distributed traces, metrics, and logs to monitor applications and infrastructure. It is built around the OTel project (telemetry standard), using OTel protocols and SDKs as the primary method for instrumentation and data ingestion. The platform targets use cases where engineering teams need end-to-end visibility into requests across services, dependencies, and infrastructure layers.
The core capability of Uptrace is distributed tracing (application performance monitoring). By ingesting trace data from services instrumented with OTel, Uptrace reconstructs end-to-end request flows across microservices, databases, queues, and external APIs. It attributes latency, errors, and resource usage to individual spans and services, and provides tools to explore traces, filter by attributes, and drill down into specific operations. This supports analysis of slow requests, failing endpoints, and service-level performance characteristics.
Beyond traces, Uptrace also works with metrics and logs (observability). It can ingest telemetry exported via OTel collectors and SDKs, store time-series metrics, and correlate them with traces and logs. Dashboards and charts present service-level metrics, such as throughput, latency percentiles, and error rates. Log and span attributes can be queried to isolate problematic deployments, regions, or tenancies. This correlation allows teams to connect high-level service metrics with underlying traces and events.
Uptrace exposes a user interface (observability tooling) that includes service maps, dashboards, and query panels. Service maps visualize relationships between services, dependencies, and databases, including latency and error indicators between components. The query interface supports filtering by span attributes, tags, and time ranges, enabling targeted troubleshooting. The system can group similar traces or errors into issues, helping teams organize incident investigation and track recurring problems.
In enterprise environments, Uptrace integrates into existing telemetry pipelines built on OTel (monitoring integration). Organizations can deploy collectors to gather data from application runtimes, containers, and infrastructure, then forward that data to Uptrace for storage and analysis. This architecture allows centralized observability while using standardized instrumentation across multiple languages and frameworks. Uptrace can be positioned in an observability stack alongside logging backends, metrics stores, or alerting systems, depending on an organization’s monitoring strategy.
From a technical categorization perspective, Uptrace fits into the domains of distributed tracing, application performance monitoring, and OpenTelemetry-based observability. It is relevant for teams operating microservices, APIs, and cloud-native workloads that require detailed visibility into request paths, performance characteristics, and errors across complex systems. Its reliance on OTel protocols and data models supports interoperability with existing instrumentation, enabling consistent telemetry collection across heterogeneous environments.