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Observability

Every model call, searchable

When someone says the AI gave a bad answer, you should be able to find that answer in under a minute. Cloudmind makes that the normal case rather than an archaeology project.

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What gets captured

  • Prompts and completions

    The full message array as sent, including system prompts, few-shot examples and the rendered template, plus the raw completion.

  • Cost and tokens

    Input and output token counts priced against current model rates, rolled up by feature, user, model and prompt version.

  • Latency breakdown

    Time to first token and total duration for every span, so you can tell a slow model from a slow retrieval step.

  • Errors and retries

    Rate limits, timeouts, content filter blocks and parse failures, with the retry chain attached to the parent trace.

  • Custom metadata

    Attach user ID, tenant, feature flag, experiment arm or anything else, then filter and group by it.

  • User feedback

    Thumbs up and down or a numeric score from your own UI, joined back to the trace that produced the response.

Adding metadata you can filter by

import { cloudmind } from "@cloudmind-ai/js";

await cloudmind.trace(
  { name: "support-summary", userId: user.id, metadata: { plan: user.plan, tenant: user.orgId } },
  async () => {
    return summarizeTicket(ticket);
  },
);

Frequently asked questions

How long are traces retained?
Seven days on the free tier, 90 days on Team, and configurable up to two years on Enterprise. You can export traces to your own warehouse at any time.
Does the SDK add latency to my requests?
No. Traces are batched and sent asynchronously on a background flush, so the SDK is not in the critical path of your model call. Typical overhead is under one millisecond of CPU time per call.
Can I redact sensitive data before it is sent?
Yes. Register a redaction function in the SDK and it runs client-side before anything leaves your process, so PII never reaches Cloudmind servers.

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