For cloudflare workers ai infrastructure and platform teams
Cloudflare Workers AI: access limit, input rejection or inference execution?
Connect the first useful exception with the relevant runtime evidence. Was the request rejected before inference could execute?
Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.
Replay a resolved Cloudflare Workers AI workflow incident
Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.
Before uploading, review cloud data handling and local options.
Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.
Try Denpex for 30 days. FREE.
No credit card. No automatic subscription. See whether Denpex helps with an incident you already understand.
Start your free 30-day Team evaluation
Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.
What a useful evaluation should establish
Compare one incident your team already resolved with the first failure, cited evidence and remaining questions. Was the request rejected before inference could execute? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.
Was the request rejected before inference could execute?
Evidence to compare: HTTP status, documented error category and redacted request identifier.
Quota and authorization failures differ from model execution errors. Preserve the actual category instead of inferring a failed device from a client-facing status.
Was time spent queueing or executing the model?
Evidence to compare: Request arrival, worker-start timestamp and execution completion or exception.
End-to-end latency combines several stages. A slow response or idle device alone cannot identify the bottleneck; compare the queue and execution boundaries.
What evidence is approved for the support handoff?
Evidence to compare: Redacted job metadata, first exception and the permitted log-sharing boundary.
A public workflow grants no permission to export private data. Review approved operational excerpts without customer payloads, credentials or research datasets.
Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.
Inside Denpex
See the evidence, not just the claim
Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.
Follow the failure through the cascade
Prepare the hardware escalation
Ask for the evidence that is missing
Keep the investigation in the editor
