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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Monitor your Genkit features in production

**[Firebase](https://daily.dev/sources/firebase-blog)** · 5 min read · 0 upvotes · 0 comments

## Summary

Firebase Genkit Monitoring provides production-grade observability for Genkit-powered AI features, surfacing metrics like usage, success rates, p95 latency, and token counts in the Firebase console. Enabling it requires installing the @genkit-ai/firebase plugin and calling enableFirebaseTelemetry() in the Genkit config. Feature-specific dashboards, failed-path tables, and trace viewers help pinpoint where and why complex multi-step AI flows fail, with all trace, log, and metrics data also stored in GCP for deeper analysis.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://firebase.blog/posts/2025/03/monitor-genkit-features-in-production>

## Questions this post answers

### How do I enable telemetry monitoring for a Genkit AI feature in production?

Install the @genkit-ai/firebase plugin with npm install @genkit-ai/firebase, then import enableFirebaseTelemetry from that package into the Genkit configuration file where genkit(...) is initialized and call it. After redeploying, telemetry data is automatically written to GCP Logging, Trace, and Monitoring, and becomes viewable in the Genkit Monitoring dashboard in the Firebase console.

_daily.dev surfaces practical setup guides like this for teams instrumenting AI features for production observability._

### What does token count tell you about the health of an LLM feature in Genkit Monitoring?

Token count acts as a proxy for both cost and correctness of a Genkit feature. A lower than expected token count often signals the model is returning a generic fallback like 'I don't know how to answer this,' while a higher than expected count suggests the LLM inference needs a better stop sequence. The dashboard also supports models that count images as tokens versus as a discrete image metric.

_Debugging LLM cost and quality issues gets easier when developers share findings like this via daily.dev._

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---

Tags: [#llm](https://daily.dev/tags/llm), [#gcp](https://daily.dev/tags/gcp), [#observability](https://daily.dev/tags/observability), [#firebase](https://daily.dev/tags/firebase)

[View this post on daily.dev](https://daily.dev/posts/monitor-your-genkit-features-in-production-4atsdkidu)

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