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OpenTelemetry (OTel) is a vendor-neutral framework and toolkit for generating, collecting, and exporting telemetry. It gives application and infrastructure teams shared APIs, conventions, protocols, libraries, and collection components—but it does not store or visualize telemetry. As the OpenTelemetry project puts it, “OpenTelemetry is not an observability backend itself.”
What OpenTelemetry standardizes
OpenTelemetry is a set of interoperating parts, not a single agent or monitoring product. The specification defines how telemetry is represented and exchanged. APIs and SDKs let software create and export telemetry; libraries and automatic instrumentation can add it with less custom code. Semantic conventions provide common names and attributes, while the Collector receives, processes, and exports data.
These pieces let teams use consistent instrumentation and transport even when their storage and analysis systems differ. The project documentation says more than 90 observability vendors support OpenTelemetry; that statement was last modified August 29, 2025, and is not an independently audited or verified 2026 market count. See the OpenTelemetry documentation.
How telemetry gets from a service to a backend
A typical path is: an application or infrastructure source produces telemetry through instrumentation or a receiver; an SDK or Collector pipeline handles it; an exporter sends it, commonly using OTLP, to an observability backend. The backend is where data is stored, queried, and visualized.
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Not every deployment uses every component. An application can export directly to a backend, or send data through a Collector first. The right arrangement depends on the team’s routing, processing, and operational needs. The OpenTelemetry specification overview describes the project’s components and relationships.
What traces, metrics, and logs tell you
The signals answer different questions. Consider an incident in which customers report that a request is slow:
- Metrics are numeric measurements summarized over time. A latency or error-rate metric can show when the problem began and how broadly it affects traffic.
- Traces follow an individual request across services and dependencies. A trace can help locate which part of that request took unusually long.
- Logs are timestamped messages that record event details. They can provide context about what a service reported at a particular time.
Shared context and attributes can help teams navigate between signals, and semantic conventions make common names more consistent. OpenTelemetry provides ways to emit and carry telemetry; whether signals are correlated in a useful way also depends on instrumentation, data, and backend capabilities. The OpenTelemetry observability primer explains the signals.
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Do you need an OpenTelemetry Collector?
No. The official documentation describes direct-to-backend export as a reasonable way to get started. A Collector becomes useful when a team needs a separate place to receive, process, or route telemetry. Its behavior depends on the components configured in its pipeline; do not assume a particular processor or capability is present by default.
| Approach | Useful when | Trade-offs to assess |
|---|---|---|
| Direct application-to-backend export | You want a simple initial setup with fewer components to operate. | Consider backend-specific configuration in each application, coupling to that destination, and whether centralized processing is needed. |
| Collector-based routing | You need an agent or gateway to receive, process, or route telemetry to one or more backends. | Account for deployment footprint and ownership, resiliency and backpressure needs, filtering or scrubbing requirements, and routing complexity. |
For current Collector releases and configuration details, consult the Collector documentation. Its release information was updated September 16, 2026 to v0.161.0; release versions and component details can change.
How application and infrastructure teams can start together
Keep the first implementation small enough to understand, but make ownership and data handling explicit before expanding it. A practical sequence is:
- Choose one service or infrastructure slice. Agree on the question the telemetry should help answer rather than instrumenting everything at once.
- Pick a first signal and destination. Confirm which backend will receive the data and how the team will verify that it arrived.
- Use supported instrumentation first. Check whether libraries or automatic instrumentation cover the service before writing custom instrumentation.
- Agree on resource identity and naming. Set service and resource attributes consistently, and use applicable semantic conventions so teams can interpret the data across services.
- Verify propagation and export. Confirm that context moves across the relevant service boundaries and that the chosen exporter or Collector route delivers telemetry to the expected destination.
- Review operational risks before scaling. Examine volume, metric cardinality, sensitive attributes, sampling choices, and what happens if an exporter, Collector, or backend is unavailable.
Application teams generally own what their services emit and whether manual, library-based, or zero-code instrumentation is appropriate. Infrastructure or platform teams can set shared configuration and operate Collector agents or gateways when centralized receiving, processing, or routing is needed. The topology need not be identical for every service, but shared naming and clear ownership make the resulting telemetry easier to use.
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Further reading
Learning OpenTelemetry by Ted Young and Austin Parker is an optional intermediate-to-advanced book for developers, operators, infrastructure teams, and engineering leaders. O’Reilly lists the March 2024 book as 170 pages and describes coverage of architecture, instrumentation, Collector pipelines, operation, troubleshooting, and rollout. Book details and availability may change; see the O’Reilly publisher page.
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