Add Google AI Studio usage via Cloud Monitoring

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
55/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Quiet
Tech stack
google-cloud, swift
Domain
ai, cloud, observability

Research direction

Start by reading the existing Vertex AI ADC and Cloud Monitoring implementation, then review the proposed provider boundary and fixture-only testing approach. Done means a separate Google AI Studio provider uses explicit project selection and native generativelanguage.googleapis.com metrics without live credentials, cookies, private endpoints, or credential-value logging.

Written by the indexing model from the issue text.

Description

clawsweeper:needs-maintainer-review clawsweeper:needs-product-decision clawsweeper:no-new-fix-pr issue-rating: 🌊 off-meta tidepool P3

Summary

Could CodexBar add a separate Google AI Studio provider for project-level Gemini API usage through the official
Cloud Monitoring API and gcloud Application Default Credentials?

This is distinct from:

  • Gemini, which uses Gemini CLI OAuth and private quota APIs;
  • Vertex AI, which monitors aiplatform.googleapis.com;
  • #2377, which adds model pricing lookup rather than project quota telemetry.

Evidence

I ran a read-only seven-day spike against an active project. It made no Gemini generation calls, imported no cookies,
and changed no APIs, IAM, or project settings.

  • 14 populated native generativelanguage.googleapis.com/* series;
  • 6 unambiguous quota usage/limit pairs;
  • request, input/output token, and model-level data available;
  • about 48 minutes of observed reporting lag.

Native Gemini metrics were sufficient; generic Service Runtime quota metrics were not.

Proposed v1

  • Built-in provider using ADC, reusing/refactoring Vertex AI's Cloud Monitoring transport.
  • Explicit project selection; no automatic Google Cloud changes.
  • Primary window: highest unambiguous quota usage.
  • Details: project/model request and token totals plus update time.
  • No browser cookies, private AI Studio endpoints, per-key breakdown, billing/cost, or cross-provider fallback.
  • Never render or log project/credential label values.

A local provider plugin cannot support this safely today because plugins cannot access gcloud/ADC.

Would a separate built-in Google AI Studio provider fit CodexBar's provider boundary?

If accepted, I can prepare a focused implementation that reuses the existing Vertex AI ADC and Cloud Monitoring code
where practical, with fixture-only tests and no live credentials.

Dominant language
Swift
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Forks
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Avg merge
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Merged PRs (30d)
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