Generate Observability Architecture Diagrams with AI

The challenge

Modern observability stacks span instrumentation in dozens of services, multiple collector layers, and several backend systems for traces, metrics, and logs. The three signals often travel different paths, use different protocols, and land in different tools. This complexity is invisible when everything works — and nearly impossible to reason about during an incident without a clear diagram. SREs and platform engineers struggle to document observability architecture because the tooling is spread across the codebase, Kubernetes manifests, and cloud provider configs.

The solution

Describe your observability stack the way you'd explain it to a new SRE:

From that description, you get a complete observability architecture diagram showing the instrumentation layer, collector topology, processor pipeline, backend routing, and alerting path. Use chat-based editing to add sampling rates, annotate signal types, or show failure modes.

Observability diagrams we support

• Full observability pipeline diagram

  End-to-end view from service instrumentation through collectors and processors to observability backends. Maps all three signals (traces, metrics, logs) in a single diagram.

• OpenTelemetry collector topology

  How OTel collectors are deployed — DaemonSet agents, sidecar containers, or gateway clusters — and how they route data to backends. Critical for capacity planning and cost optimization.

• Metrics collection and alerting diagram

  Prometheus scraping targets, recording rules, alert manager routing, and notification channels (PagerDuty, Slack, Opsgenie). Shows the full metrics-to-alert path.

• Distributed tracing architecture

  How trace context propagates across services, where sampling decisions are made (head vs. tail), and how spans are stored and queried in Jaeger, Tempo, or Honeycomb.

• Log aggregation pipeline

  How logs flow from services through collection agents (Fluentd, Fluent Bit, Vector) to centralized log storage (Loki, Elasticsearch, Splunk, Datadog Logs) and search interfaces.

• Multi-backend observability diagram

  When different signals go to different backends — traces to Honeycomb, metrics to Datadog, logs to Splunk — this diagram maps which exporters route where and why.

Perfect for

• SRE and platform team onboarding documentation
• Incident response runbooks and post-mortem action items
• Observability stack architecture reviews and vendor evaluations
• Cost attribution and telemetry volume analysis
• Compliance audits requiring evidence of monitoring controls
• Engineering leadership presentations on observability maturity