I am seeing extreme, intermittent latency in OpenAI-managed vector-store ingestion.
I ran the same capability test twice using the same API project, credentials, endpoint, and ingestion configuration. Each run created a fresh vector store, uploaded an equivalent 145-byte text file, attached it as a one-file batch, and polled the batch status.
Fast run
- Started: 2026-07-24 15:42:52 UTC
- Vector-store creation: 0.621s
- File upload: 1.106s
- Indexing completed: 6.7s
- Entire test completed: 20.0s
Slow run
- Started: 2026-07-24 15:50:52 UTC
- Vector-store creation: 0.698s
- File upload: 0.500s
- Still
in_progresswithcompleted=0/1after 600 seconds - Direct vector-store file status remained
in_progress last_errorremained null
All authentication, permission, file upload, Responses API, and vector-store operations passed. All diagnostic requests returned HTTP 200. There were no 429 responses, rate-limit errors, or 5xx errors. The file itself reached processed; only the asynchronous vector-store ingestion remained stalled.
This appears to isolate the latency to the asynchronous vector-store parsing/chunking/embedding pipeline rather than uploads, permissions, request rate, or file complexity.
Is anyone else observing this degree of ingestion variance? Is there a known ingestion backlog or expected latency range for vector-store file processing?
I have full timestamps, OpenAI request IDs, vector-store IDs, file IDs, batch IDs, and diagnostic output available for a private support case.