OpenTelemetry Architecture Diagram: Visualize Your Observability Stack (2026)

An OpenTelemetry architecture diagram maps how telemetry data — traces, metrics, and logs — flows from your services through the OpenTelemetry (OTel) collection pipeline to your observability backends. A clear OTel diagram shows the instrumentation layer in each service, how data reaches the OTel Collector, what processors and exporters transform it, and which backends (Jaeger, Prometheus, Grafana Tempo, Datadog, Honeycomb) ultimately store and analyze it.

OpenTelemetry has become the industry standard for observability instrumentation. But the collector pipeline — with its receivers, processors, batch exporters, and fan-out routing — can be surprisingly complex to communicate to other engineers, especially across instrumentation languages, deployment environments, and multi-backend setups. A well-structured architecture diagram makes that complexity legible.

What is OpenTelemetry?

OpenTelemetry (OTel) is a CNCF project providing a vendor-neutral framework for collecting observability data. It consists of:

• SDKs / auto-instrumentation: Language-specific libraries that instrument your code to emit traces, metrics, and logs in OTLP format (Java, Python, Node.js, Go, .NET, Ruby, and more)
• The OTel Collector: A standalone service that receives telemetry data, processes it (sampling, filtering, enrichment), and exports it to one or more backends
• OTLP (OpenTelemetry Protocol): The transport protocol for shipping telemetry from SDKs to the collector or directly to backends

Core components of an OTel architecture diagram

Instrumentation layer (services)

Each service in your system emits telemetry. Your diagram should show: which language runtime each service uses, whether instrumentation is automatic (auto-instrumentation agent) or manual (SDK), and whether data is sent to a local sidecar collector or directly to a central collector via OTLP/gRPC or OTLP/HTTP.

OTel Collector topology

The collector is often deployed in one of three patterns:

• Agent mode: A collector sidecar or DaemonSet runs on every node and batches data before forwarding to a central collector
• Gateway mode: A central collector cluster (scaled horizontally) receives from all agents and routes to backends
• Direct-to-backend: Services send OTLP directly to a managed backend (Honeycomb, Grafana Cloud, Datadog) without a self-hosted collector

Your diagram should make the topology explicit — a single node labeled "OTel Collector" obscures whether you have 50 agent collectors or one gateway.

Collector internals: pipeline configuration

Inside each collector, data flows through a pipeline: receivers → processors → exporters. Each signal type (traces, metrics, logs) has its own pipeline. Common processors include:

• Batch processor: Groups data into batches to reduce export overhead
• Probabilistic sampler: Drops a percentage of traces to control volume and cost
• Tail-based sampler: Keeps complete traces for errors/slow requests, drops others — more complex but much more useful
• Attribute processor: Adds, removes, or hashes span attributes (useful for PII scrubbing)
• Filter processor: Drops spans/metrics matching specific conditions (e.g., health check endpoints)

Observability backends

The exporters ship data to one or more backends. Your diagram should show what goes where — traces to Jaeger, metrics to Prometheus, everything to Grafana Cloud, or fan-out to multiple backends simultaneously.

Example: generating an OTel architecture diagram

Describe your observability stack in plain English:

From that description, ArchitectureDiagram.ai generates a complete OTel architecture diagram showing the instrumentation layer, sidecar topology, gateway, processor pipeline, and backend routing — ready for architecture review, runbook documentation, or onboarding.

Types of OTel diagrams to create

• Full observability pipeline diagram

  End-to-end view from service instrumentation through collectors to backends. Best for architecture reviews and onboarding documentation.

• Collector deployment topology

  How collectors are deployed across Kubernetes (DaemonSet agents vs. gateway Deployments vs. sidecar injection). Critical for capacity planning and cost control.

• Sampling strategy diagram

  Shows where sampling decisions happen (head vs. tail), what percentages are kept per service, and how this affects backend storage costs.

• Multi-backend fan-out diagram

  When different signals go to different backends (traces to Tempo, metrics to Datadog, logs to Splunk), this diagram maps which exporters route where — useful for cost and vendor audit.

• Instrumentation coverage map

  Which services are instrumented, which SDKs they use, and which have gaps. Useful for tracking observability coverage as part of a platform engineering initiative.

OTel backends at a glance

Best practices for OTel diagrams

• Separate the three signals: Traces, metrics, and logs often travel different paths. Don't collapse them into a single "telemetry" arrow — show each signal type explicitly.
• Label transport protocols: OTLP/gRPC and OTLP/HTTP behave differently under load. Label the edges.
• Show the failure modes: What happens when the collector gateway is unavailable? Does data buffer in the agent, or is it dropped? Include retry and buffer annotations.
• Annotate sampling rates: Sampling decisions are invisible in the running system but critical for cost and correctness. Make them explicit on the diagram.

Related guides: Kubernetes architecture diagram examples, microservice architecture patterns, data flow diagram guide, and MLOps pipeline diagrams.