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The right tracing platform for Cloudflare Workers is the one that can ingest Workers’ OpenTelemetry data, show the runtime operations you actually need, fit your sampling and retention budget, and make limitations visible instead of implying complete end-to-end traces. Cloudflare identifies Honeycomb, Grafana Cloud, and Axiom as compatible examples when a provider offers the required OpenTelemetry endpoint; that list is not a ranking. Before committing, test a small deployment, confirm what arrives, and verify whether your backend traces can be correlated because Workers currently does not propagate trace IDs to services outside Cloudflare.
Start with the questions that determine whether a platform will work
A tracing product can advertise OpenTelemetry support and still be a poor fit if its endpoint, authentication method, retention policy, or query model does not match Cloudflare’s Workers export. Evaluate these questions before comparing dashboards:
- Ingestion: Can it accept the OpenTelemetry traces and logs that Workers exports through Cloudflare’s configured destination path?
- Correlation: Will the platform join a Worker trace to your origin, database, queue consumer, or other service trace, or will you need a separate investigation workflow?
- Coverage: Are handler invocations,
fetchcalls, binding operations, RPC calls, and your custom application spans represented clearly? - Sampling: Is head-based sampling adequate for your traffic, or do you need to retain slow and failed requests selected after completion?
- Retention and cost: How long will the provider retain data, what volume will your chosen rate create, and how does that interact with Cloudflare’s own seven-day Workers retention?
- Data control: Should Cloudflare retain a copy, or should an external destination be the only stored location?
- Stability: Can your queries survive changes to beta span names and attributes?
- Investigation workflow: Does the interface help you move from a Ray ID to cache, routing, Worker, and origin timing?
Understand what Workers traces contain
Cloudflare Workers includes tracing instrumentation without requiring an SDK or code changes. Enable it with observability.traces.enabled = true in Wrangler. The built-in instrumentation covers handler invocations, outbound fetch calls, binding operations, and RPC calls. You can add custom spans around application-specific work when those built-in boundaries are not enough.
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Workers exports OpenTelemetry traces and logs to configured destinations. Workers infrastructure metrics and custom metrics are not part of this OpenTelemetry export path, so choose a separate metrics route if those signals are required in the same operational system.
Confirm OpenTelemetry ingestion with a real deployment
Compatible examples are starting points, not guarantees
Cloudflare lists Honeycomb, Grafana Cloud, and Axiom as examples of destinations that can work when the provider has an OpenTelemetry endpoint. Compatibility depends on the particular account, endpoint, authentication requirements, and data type you configure. Do not treat the examples as a feature or price ranking.
Test the complete delivery path
Ask the provider for the exact OpenTelemetry endpoint and required credentials, then create the destination in Cloudflare and configure its name in Wrangler. Redeploy the Worker; Cloudflare says events can take several minutes to arrive. Generate a known request that exercises a handler, a fetch, and a binding or RPC operation. Confirm that the destination receives the expected trace and log records, not merely a successful configuration response.
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Do not assume end-to-end trace joins
Cloudflare’s documented Workers limitation is significant: trace IDs are not propagated to services outside Cloudflare. A Worker trace therefore does not automatically become one continuous trace with your origin API, database service, or another cloud service. A platform may show the Worker segment perfectly while the backend appears as a separate, unrelated trace.
Make this a release-test criterion. Send a request through the Worker to a backend, then inspect both systems. If your incident process depends on one-click cross-service navigation, verify what identifiers the backend receives and what correlation workflow your team can support. Do not buy on the assumption that OpenTelemetry format alone solves propagation; the format and the runtime’s propagation behavior are separate issues.
Choose a sampling policy deliberately
Workers tracing uses head-based sampling. The documented default is a head_sampling_rate of 1, meaning 100 percent of eligible requests are sampled when you do not specify a rate. The valid range is 0 through 1. A lower value reduces volume, but it is decided at the start of a request.
Head sampling cannot look at the outcome and then retain only errors or slow requests. Cloudflare documents that tail-based sampling is not currently supported. If you sample at 10 percent, an important failure that was not selected is absent; the platform cannot recover it afterward.
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Set a rate that reflects traffic and investigative needs, then validate the resulting volume in a deployed environment. A practical test should include normal traffic, an intentionally failed request, and a slow path. Record how many requests are sampled and whether the destination’s ingestion controls behave as expected.
[observability.traces]
enabled = true
head_sampling_rate = 0.1The exact rate should be chosen from measured volume rather than copied from an example. Cloudflare states that requests which are not sampled skip tracing entirely and that tracing has no measurable overhead on request processing; this is a vendor statement from its Traces FAQ, last updated October 9, 2026, not an independent benchmark.
Compare retention, persistence, and future charges
Workers traces are retained for seven days in Cloudflare. Ask the external provider whether its retention is shorter, longer, or tiered, and whether logs and traces have different terms. A longer external retention may be valuable for recurring incidents, while a short period can be sufficient for an on-call workflow and less expensive.
Cloudflare’s export configuration persists telemetry by default. Setting persist: false sends the data externally without storing it in Cloudflare for that telemetry. Use this when the external platform is intended to be the system of record, but verify that your incident and compliance procedures do not require a Cloudflare copy.
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Cloudflare states that persisted traces will begin contributing to account-level ingestion and storage usage on December 1, 2026. That date is future-dated relative to the documentation and may affect the cost of retaining a Cloudflare copy; recheck the current account terms before publishing a budget or making a purchase decision. Provider pricing, limits, and retention terms also change, so obtain current commercial details directly rather than inferring them from compatibility.
Map the coverage and the blind spots
| Area to inspect | What to verify | Why it matters |
|---|---|---|
| Worker handler | Invocation appears with useful timing and outcome fields | Establishes a reliable top-level unit for an incoming request |
| Outbound fetch | Each relevant call is represented and can be filtered by target | Separates Worker time from origin or API latency |
| Bindings | KV, Durable Objects, queues, service bindings, or other bindings used by your code are visible where supported | Shows which platform dependency is slowing or failing |
| RPC | RPC calls have usable spans and attributes | Important for service-to-service Worker designs |
| Custom spans | Application steps can be added without exceeding span limits | Exposes parsing, authorization, cache decisions, or business operations |
| Metrics | Infrastructure and custom metrics are collected through an intentional metrics path | They are not exported by the Workers OpenTelemetry route |
Some operations do not have dedicated spans. Cloudflare’s beta documentation also says attributes are incomplete and that span and attribute names may change. Treat saved queries and alerts that depend on exact names as version-sensitive assets. Prefer stable concepts and keep a small regression test that checks the fields your dashboards require.
Use custom spans within their limits
Custom spans extend the built-in runtime instrumentation around application-specific sections. Cloudflare documents a maximum span-name length of 64 bytes; longer names are truncated. Each custom span has approximately 64 KB for attributes and exception data. Keep attributes compact and avoid placing request or response bodies, tokens, or other large payloads in them.
The cited documentation does not provide manual parent-child wiring, spanContext(), or setStatus. Do not design an implementation that depends on those APIs unless your current runtime documentation explicitly adds them. Name spans for durable operations, such as authorize, read-session, or call-origin, rather than embedding unbounded user input in names.
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Distinguish Workers tracing from Cloudflare-wide Traces
Workers tracing and Cloudflare-wide Traces answer related but different questions. Workers tracing follows the Worker runtime and its instrumented operations. Cloudflare-wide Traces records production request paths through supported Cloudflare steps, including routing, cache, Workers, and origin connections, and supports filtering by Ray ID.
Some request-path features do not have dedicated spans. Cloudflare-wide Traces captures request and query-string values, while header values are not captured according to the documentation. That distinction affects both troubleshooting and privacy reviews: a missing header field is not necessarily a provider ingestion failure.
When evaluating a platform, inspect both views for the same Ray ID. Decide whether your responders need runtime detail, edge-path detail, or both, and ensure the chosen destination and retention policy preserve the information required by that workflow.
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A repeatable evaluation procedure
- Define the incident questions. Write down whether you need to locate Worker CPU time, identify a slow origin, diagnose cache behavior, follow an RPC call, or retain evidence for a later investigation.
- Enable a controlled Worker. Turn on
observability.traces.enabledand set an explicit temporary sampling rate appropriate for the test. Deploy a version with one predictable handler, fetch, and binding or RPC path. - Create and configure the destination. Use the provider’s OpenTelemetry endpoint and credentials, create the Cloudflare destination, and place its configured destination name in Wrangler. Redeploy after changing configuration.
- Generate test traffic. Exercise successful, failed, and slow requests. Include a request that reaches your backend so you can test correlation rather than only ingestion.
- Inspect raw records. Confirm trace and span IDs, timestamps, operation names, attributes, and exception data. Check whether logs arrive as expected and whether fields are transformed or dropped.
- Test the gap outside Cloudflare. Compare the Worker trace with the backend trace. Document exactly how responders will move between them because automatic outside-service propagation is not currently provided.
- Measure volume and retention. Compare sampled request counts with destination ingestion, examine retention expiration, and calculate the effect of keeping or disabling Cloudflare persistence.
- Run the workflow by Ray ID. Verify that an on-call engineer can find the request and distinguish routing, cache, Worker, and origin timing in the time available for an incident.
- Freeze only durable queries. Save dashboards using fields you have verified, and note that beta span names and attributes can change.
Decision framework for selecting a platform
| If your priority is… | Give the most weight to… | Reject a platform when… |
|---|---|---|
| Fast Worker debugging | Clear handler, fetch, binding, and RPC views; quick Ray ID lookup | The UI hides raw spans or cannot isolate edge and origin timing |
| Long-lived incident history | External retention, search, and export controls | Retention is shorter than your investigation cycle or pricing is opaque |
| Strict data minimization | persist: false, attribute controls, and access policies |
You cannot control where telemetry is stored or who can query it |
| Cross-service diagnosis | A documented, tested correlation method for backend traces | The sales or setup material implies automatic joins that your test cannot reproduce |
| High-volume traffic | Sampling controls, ingestion limits, and predictable billing | Only full-rate sampling is practical or sampled volume cannot be measured |
| Stable automation | Versioning information and resilient query fields | Dashboards require undocumented beta names that change without notice |
Operational and privacy checks before production
- Remove secrets and sensitive payloads from custom attributes; a trace is operational data that may be broadly searchable.
- Set access roles in the destination and decide who can view request and query-string values.
- Document the seven-day Cloudflare retention separately from the provider’s retention so responders know where historical evidence exists.
- Monitor sampled volume after every traffic or rate change, especially ahead of the December 1, 2026 persisted-data accounting date.
- Keep a fallback procedure using logs, Ray IDs, and provider request identifiers for requests that were not sampled.
- Revalidate compatibility after Workers instrumentation updates because the tracing feature is documented as an open beta.
Bottom line for a sound choice
Choose only after a deployed proof of delivery. The minimum acceptable result is an OpenTelemetry destination that receives the Worker traces and logs you expect, useful coverage for your actual bindings and fetches, a sampling rate you can afford, retention that matches your incident window, and a documented workaround for the missing trace-ID propagation beyond Cloudflare. Keep Cloudflare persistence only when its copy has a clear operational or compliance purpose; otherwise evaluate an external-only path with persist: false. Treat Honeycomb, Grafana Cloud, and Axiom as compatibility examples to test—not endorsements—and revisit every cost, beta limitation, and field name before production approval.
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