For deepl infrastructure and model engineering teams
DeepL language-model job: precision, input delivery or communication?
Compare a resolved language-model runtime incident with device identity, worker errors and input progress. Keep training failure separate from translation quality.
Free 30-day Team evaluation. No card or automatic subscription. Work email verification required.
Replay a resolved DeepL 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 has already resolved with the first failure, quoted evidence and remaining questions. Begin with this question: Which system and precision path produced this exception? Only assign a process, device or recovery state when the submitted evidence establishes it.
Which system and precision path produced this exception?
Evidence to compare: Actual node architecture, framework build and selected numerical precision.
Do not transplant an older system remedy onto a newer deployment. Precision support and extension compatibility require the real hardware and software combination.
Was the job waiting for input or another worker?
Evidence to compare: Input-reader progress, per-worker timestamps and the first available exception.
Low useful progress can originate outside device compute. Compare input and worker evidence before attributing a collective timeout to the interconnect.
Did the model run fail or did translation quality regress?
Evidence to compare: Process completion, saved artifact identity and independently measured quality results.
A successfully completed run may still produce an unsuitable model. Keep runtime diagnosis and language evaluation separate, and do not approve recovery without inspecting the selected artifact.
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
