Question

A media analytics company runs nightly sentiment classification over 40 million historical social-media posts using Amazon Bedrock batch inference. Each dataset is split into multiple input files in S3, and the team submits several batch jobs at once. They notice that although they submit 12 jobs simultaneously, only a few move to the 'InProgress' state while the rest sit in 'Submitted' or 'Validating' for hours, causing the overall pipeline to miss its morning deadline. On-demand and provisioned throughput are not being used for this workload. What is the MOST likely cause and the correct remediation?

Accepted Answer

The account has reached the service quota for the maximum number of concurrent (in-progress) batch inference jobs per model; request an increase to that quota or consolidate input files into fewer, larger jobs to stay within the concurrency limit.

Answer

Batch inference does not support parallel jobs at all; switch every job to on-demand InvokeModel calls with exponential backoff to process the posts faster.

Answer

The jobs are throttled because provisioned throughput model units were not purchased; buy provisioned throughput to allow more batch jobs to run concurrently.

Answer

The S3 input manifest exceeds the per-file record limit, so extra jobs are being silently rejected; reduce each input file to fewer than 1,000 records to unblock them.