Persistent 503 Errors on Gemini Batch API GET Endpoint — Jobs Succeeded but Results Unretrievable

I’m encountering a critical issue with the Google Generative AI Batch API where batch jobs complete successfully, but the GET batch endpoint consistently returns 503, making it impossible to retrieve the results.

Summary

  • Service: Generative AI Batch API

  • Endpoint: generativelanguage.googleapis.com/v1beta/batches/{jobName}

  • Error: 503 Service Unavailable

  • Duration: Ongoing for ~2+ hours

  • Impact: 747 successfully generated images are inaccessible


API Behavior

:cross_mark: GET /v1beta/batches/{jobName}

Returns 503 Service Unavailable consistently.

Example:

{
  "error": {
    "code": 503,
    "message": "The service is currently unavailable.",
    "status": "UNAVAILABLE"
  }
}

This endpoint is required to access:

  • metadata.output.inlinedResponses

  • OR destUri / output file references

:white_check_mark: GET /v1beta/batches

  • Works correctly

  • Returns job metadata (state, stats)

  • Does not include output results


What I’ve Tried

  • Repeated retries with exponential backoff

  • Different endpoint paths (v1beta, v1)

  • Verified API key validity (other endpoints work)

  • Confirmed jobs are SUCCEEDED

  • Checked Google Cloud Status Dashboard (no incident reported)


Expected Behavior

The batch GET endpoint should return completed batch details including:

  • Inline responses or

  • Output file location (destUri)


Actual Behavior

The endpoint returns 503 indefinitely, blocking access to completed batch results.


  1. Is this a known issue with the Gemini Batch API?

  2. Are completed batch results recoverable once the endpoint is restored?

  3. Is this region-specific or a broader outage?

  4. Should we wait, or are results at risk of being lost?

  5. Any recommended workaround for retrieving outputs

Tried to do another batch 17 hours later, seems to generate the images but still throws a 503 when retrieving the results.

I was able to run a batch of 10 requests successfully just now. The failing batches were holding 500 and 100 requests each.

@Sonali_Kumari1 can you help?