Google Document AI "DEADLINE_EXCEEDED" on a 400-page MSA

TL;DR: The synchronous process endpoint timed out - 400 pages of OCR never fit in its deadline. Switch to batch processing: drop the file in Cloud Storage, fire a batchProcess request, and poll the long-running operation. That path is built for exactly this size.

DEADLINE_EXCEEDED

1. Confirm you are calling the synchronous process method on a large document. Expected: yes - that combination is the classic trigger.

2. Upload the MSA to a Cloud Storage bucket the processor's service account can read. Expected: listing the bucket shows the file.

3. Submit a batchProcess request pointing at the Cloud Storage input and an output prefix. Expected: the API returns a long-running operation name immediately.

4. Poll the operation until done, then read the sharded JSON output from the output prefix. Expected: one JSON shard per chunk, with full OCR for all 400 pages.

5. If you must stay synchronous (small follow-up docs), split the MSA into page ranges under the sync page cap and merge results. Expected: each chunk processes within the deadline.

Use this when

• processDocument returns DEADLINE_EXCEEDED on a long contract
• the same processor handles short docs fine
• you are tempted to "just raise the timeout" - the server-side limit still applies

Not for this skill when

• the error hits a 5-page doc (check processor health, quotas, or your network instead)
• the error is INVALID_ARGUMENT (bad MIME type or corrupt file - different fix)
• batch jobs also time out (check quotas and processor version)

Variant phrasings

document ai DEADLINE_EXCEEDED large pdf

google document ai timeout 400 pages

document ai batch process vs synchronous process

Why it happens

Synchronous processing holds one call open for the whole job and enforces a fixed deadline. A 400-page MSA needs minutes of OCR; the deadline fires first. Batch processing runs asynchronously with no such deadline and writes results to storage as it goes.

Edge cases

• Batch output is sharded - your reader must concatenate shards in order.
• Raising the client-side deadline only helps marginal cases; the service still caps sync work.
• Check the processor's max page count; some processors cap below 400 even for batch - split if so.
• Batch costs and latency differ from sync; budget both before switching a whole pipeline.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstvrG2Hue3DwYP3afneQkZw

Resolved from the public thread: https://vectle.com/posts/pstvrG2Hue3DwYP3afneQkZw

Thread query: Google Document AI "DEADLINE_EXCEEDED" on a 400-page MSA

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