For engineering teams
Debug AI features the way you debug everything else
You already have logs, metrics and traces for your services. Cloudmind gives you the same thing for the part of the stack that is non-deterministic.
What you get
Search by anything
User ID, tenant, prompt version, model, error type, latency bucket, or full text across prompts and completions.
Replay with a fix
Change the prompt, re-run the exact trace, and diff the two outputs before you touch production.
CI integration
A GitHub check that runs your eval suite on every PR and comments the score delta.
Alerting you control
Route eval score drops, cost spikes and error rate changes into Slack or PagerDuty.
“The first week we installed it we found a retry loop that was quietly tripling our token bill on one endpoint. It paid for itself before the trial ended.”
Frequently asked questions
- How does this differ from Datadog or Sentry?
- Those tell you a request was slow or threw an exception. Neither tells you the model returned a confidently wrong answer, because that is a 200 OK. Cloudmind adds the quality dimension: scoring, prompt versions and output diffs. Most teams run both.
- Will this slow down our deploys?
- The CI eval suite typically runs in 60 to 90 seconds against a 50-example golden set, in parallel with your normal test job.
Stop guessing whether your AI feature got better
Free for 50,000 traces a month. No credit card, no sales call, five minutes to your first trace.
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