OpenTelemetry is an open-source framework that standardizes how applications collect and export traces, metrics, and logs.
OpenTelemetry is an open-source observability framework that gives developers a single, vendor-neutral way to generate, collect, and export telemetry data: traces, metrics, and logs. It grew out of a merger between two earlier projects, OpenTracing and OpenCensus, and is now one of the most active projects under the Cloud Native Computing Foundation, second only to Kubernetes in contributor volume. For anyone building production AI systems or distributed services, it matters because it removes the need to rewrite instrumentation every time you switch monitoring tools.
OpenTelemetry does not store or visualize data itself. It only handles the generating, collecting, and routing of telemetry, then hands that data off to a backend of your choice.
Instrumentation: you add OpenTelemetry’s language-specific SDKs or auto-instrumentation libraries to your application. This captures traces, metrics, and logs without hand-coding every measurement point.
Traces and spans: a trace follows a single request as it moves through multiple services. Each step in that journey is recorded as a span, showing how long it took and where it happened.
The Collector: telemetry data flows into the OpenTelemetry Collector, a standalone process that receives data, processes it, and exports it to one or more backends. This decouples your application code from any specific vendor.
Export to any backend: once processed, data can be sent to open-source tools like Jaeger and Prometheus or to commercial observability platforms. Because the format is standardized, you can switch backends without touching your instrumentation.
Distributed tracing for microservices: instrument every service in a microservices architecture so you can follow a single user request across all of them and pinpoint exactly where latency or errors occur.
Full-stack observability dashboards: combine traces, metrics, and logs from OpenTelemetry with a backend like Grafana or Prometheus to build a dashboard that shows application health at a glance and lets you drill into root causes.
AI agent and LLM pipeline monitoring: instrument agentic workflows and LLM calls to track latency, token usage, and failure points across chained tool calls, which is critical when debugging multi-step agent systems in production.
Service level objective tracking: collect the metrics needed to measure uptime, response time, and error rate against predefined SLOs, then alert automatically when a service drifts out of bounds.
Compliance and audit trails: capture detailed logs and traces of user activity, API calls, and system interactions to build verifiable audit records for regulated environments.
OpenTelemetry is used to instrument applications so teams can collect traces, metrics, and logs in a consistent format. Engineers use it to track requests as they move across microservices, measure latency and error rates, and feed that data into any monitoring backend they choose, without rewriting instrumentation for each tool.
Yes. OpenTelemetry is completely free and open-source, governed by the Cloud Native Computing Foundation. There is no paid tier for the framework itself. Costs only appear if you choose a paid backend, like a commercial observability platform, to store and visualize the telemetry data OpenTelemetry collects.
OpenTelemetry generates and collects telemetry data such as traces, metrics, and logs, but it does not store or display that data. Jaeger is a backend that stores and visualizes distributed traces. In most modern stacks, OpenTelemetry instruments the application and sends trace data to Jaeger for analysis.
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