OpenTelemetry (OTel) is the open framework for generating, collecting, and exporting telemetry: traces, metrics, and logs. It is not a monitoring backend. It does not store your data, draw your dashboards, or fire your alerts. Those jobs stay with a separate observability backend, which is why application and infrastructure teams need a shared picture of where OTel stops and the backend begins.
What OpenTelemetry standardizes
OpenTelemetry provides vendor-neutral instrumentation APIs and SDKs, shared naming conventions, a wire protocol, libraries, and collection components. Its purpose is to let teams produce and move telemetry in a consistent way, whichever product eventually stores and displays it. The project’s own explainer puts the boundary in one sentence: OpenTelemetry is not an observability backend itself.
(OpenTelemetry project documentation, What is OpenTelemetry?)
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That boundary matters most when teams disagree about tooling. A team that instruments a service with OTel is choosing how its data is produced and shipped. It is not choosing a dashboard product, and it is not yet deciding retention, query language, or alerting rules.
The pieces of the project
OpenTelemetry is a set of interoperating parts rather than one installable program. The project’s specification overview describes how these fit together, including the OpenTelemetry Protocol (OTLP) used to transmit data (OpenTelemetry Specification, Overview). In practice, the parts are:
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- Specifications and OTLP: the agreed data model and the protocol that carries telemetry between components.
- Semantic conventions: standard attribute names, such as those describing a service, an HTTP request, or a database call, so that the same thing is named the same way across teams and languages.
- APIs and SDKs: the interfaces application code calls to create spans, metric instruments, and log records, and the SDK implementations that process and export them.
- Libraries and automatic instrumentation: prebuilt hooks for common frameworks and clients, which can produce telemetry with little or no code change.
- The Collector: a separate service that receives, processes, and exports telemetry. It is covered in its own section below.
Traces, metrics, and logs answer different questions
OpenTelemetry treats these three signals as distinct tools. The project’s observability primer frames them by the question each one answers (OpenTelemetry, Observability primer).
| Signal | What it records | Question it answers during an incident | Example |
|---|---|---|---|
| Metrics | Numeric measurements summarized over time | Did latency or error rate change, and when? | The checkout service’s p95 latency rose at 14:05 |
| Traces | A request’s path across components, as linked spans | Which service or dependency contributed to this one slow request? | One checkout call spent 1.8 s waiting on a payment API |
| Logs | Timestamped event messages | What exactly happened at that moment? | A payment client logged a TLS handshake timeout |
Used together, the signals move an investigation from “something changed” to “this dependency changed” to “this is the error it produced.” Shared context and attributes make that movement possible, and semantic conventions keep the names consistent. The signals are not interchangeable, and OTel instrumentation alone does not produce a correlated view inside a backend; the backend must receive the data and present it.
How telemetry moves from source to backend
The basic path is: application or infrastructure source, then an instrumentation point or receiver, then an SDK and/or Collector pipeline, then an exporter sending OTLP, then the observability backend. Not every deployment uses every hop. A service can export directly from its SDK to a backend, or it can send to a Collector that forwards onward.
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Do you need a Collector?
Not necessarily on day one. The Collector documentation describes it as a component that can receive, process, and export telemetry to one or more backends, and it also describes direct-to-backend export as a good way to get started (OpenTelemetry, Collector). The two paths trade different costs.
| Axis | Direct application-to-backend export | Collector-based routing (agent or gateway) |
|---|---|---|
| Operational footprint | Fewer moving parts; no additional service to run | An extra service to deploy, upgrade, and monitor |
| Backend coupling | Each application typically carries backend-specific export configuration | Applications send to one local endpoint; the Collector holds backend settings |
| Processing and scrubbing | Limited to what the SDK does in the application | Centralized filtering, attribute changes, and removal of sensitive values, depending on the components configured |
| Routing to several destinations | Requires separate export setup per destination | One pipeline can export to one or more backends |
| Resilience | Depends on each application’s export behavior when a backend is slow | Buffering and backpressure behavior must be designed and tested for the chosen components |
Collector behavior depends on which receivers, processors, and exporters a given configuration enables. Do not assume a specific processor is present by default; list the components you actually use.
Does OpenTelemetry replace your monitoring backend?
No. OTel produces and moves telemetry. Storage, query, dashboards, alert evaluation, and retention remain the job of your backend, whether that is a commercial product, an open-source system, or something your organization already runs. Adopting OTel usually means changing where data comes from and how it is shipped, not changing the place engineers look at it. The project documentation states that the ecosystem is broad: its documentation says more than 90 observability vendors support OpenTelemetry. That count is OpenTelemetry’s own statement, last modified August 29, 2025, and should be read as a project-reported figure from that date rather than an independent market measurement or a current count.
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How application and infrastructure teams divide the work
The two teams own different decisions, and the article’s framework works best when each side knows its share.
- Application teams decide which operations, errors, and business events a service should emit. They choose between manual instrumentation, library-based instrumentation, and zero-code automatic instrumentation where it is available for their language.
- Infrastructure and platform teams standardize configuration across services, operate Collector agents or gateways where they are used, decide routing and retention policy with the backend owner, and set the resource and deployment conventions that let data be filtered by environment, cluster, or region.
- Both teams agree on service naming, resource attributes, and which attributes count as sensitive and must not be exported.
A practical adoption sequence
This is editorial guidance for a first rollout rather than a project mandate. The order is designed to keep early choices reversible.
- Choose one service or infrastructure slice. Pick something with a clear owner and a known incident history so you can judge whether the new data helps.
- Pick a first signal and a backend. Traces or metrics are common starting points for request-path questions. Confirm the backend accepts OTLP or has a supported path for it.
- Use supported instrumentation before writing custom code. Libraries and automatic instrumentation already follow the semantic conventions, which saves rework.
- Settle service and resource naming and semantic conventions. Agree on names before dashboards depend on them.
- Verify propagation and destination. Confirm that trace context crosses service boundaries and that data arrives at the backend with the expected attributes.
- Review volume, cardinality, sensitive attributes, sampling, and failure behavior before scaling. Check what happens when the backend is slow or unreachable, and whether any high-cardinality attributes are inflating cost. Decide whether a Collector is needed for scrubbing or routing at this point.
Teams that follow this order usually find that the Collector question answers itself. If the first slice needs only one backend and little processing, direct export keeps the footprint small. If several services need the same scrubbing or routing rules, a shared Collector tier starts to pay for its operational cost.
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Version and currency notes
The Collector documentation page records a September 16, 2026 update that refers to release v0.161.0. Release numbers, component names, and configuration syntax change, so check the current release notes before copying any configuration or command into production. The core definitions in the project’s own documentation, covering what OTel is, its parts, and the three signals, are considerably more stable than individual release details.
Further reading
Learning OpenTelemetry by Ted Young and Austin Parker (O’Reilly Media, March 2024, 170 pages, ISBN 9781098147174) is an intermediate-to-advanced book covering OTel architecture, instrumentation, operating and troubleshooting, Collector pipelines, and rollout. It suits developers, operators, and engineering leaders who have finished this introduction. Check the publisher’s listing for the edition and release coverage before relying on any specific version detail (O’Reilly Media, Learning OpenTelemetry).
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