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Batch inference complements the low-latency API when you need to score or enrich large portfolios.

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When to choose batch

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Workflow

1

Upload data

Request signed upload URLs with POST /v2/api/files/upload, declaring each file’s path, content type, and base64-encoded MD5 digest. Upload each file with a PUT to its upload_url, sending matching Content-Type and Content-MD5 headers.
2

Submit job

Request a batch prediction with POST /v2/api/batches, referencing the uploaded files, an available model, and an optional reference_date. Avra responds with a batch id so you can track the job.
3

Processing

Jobs run in prioritized queues with resource usage quotas. Expect minutes for thousands of records and hours for millions.
4

Waiting

Poll GET /v2/api/batches/{id} checking job status or subscribe to the batch-lifecycle webhook to be notified on status transitions.
5

Download results

Request a download link at GET /v2/api/batches/{id}/result to obtain a signed short-lived download_url.
Refer to the API Reference for endpoint schemas, payload examples, and error handling best practices when submitting batches.Migrating from the v1 batch-inputs flow? See Migrating to Batch v2.Workflow steps may vary when using SFTP Batch Inference. Please refer to the documentation for more details.
The v1 batch endpoints (/v1/api/batch-inputs/*, /v1/api/batches/*) are deprecated and maintained only for backward compatibility. They will be sunset soon. Build new integrations on the v2 endpoints above.

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Limits and Best Practices

  • No hard limits. The Files API does not impose size or row-count limits, but following good practices keeps job queues no longer than needed and jobs reliable.
  • Split large inputs across many files. A batch accepts many files at once, prefer uploading and referencing several medium files over one huge one.
  • Prefer Parquet over CSV. Parquet datasets have strong typing and compression, providing faster uploads and batch jobs while preserving data quality and precision.
  • Size Parquet datasets sensibly. A Parquet dataset can be split into row_groups (the minimal decompressed block in a read operation) and partitions (the segmentation of the dataset across multiple files). Aim for at most 100k rows per row_group and 5 GB per partition.
  • Very large jobs. These may require fine-tuning to avoid failures, reach out so we can size the run properly.

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Integrations

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Monitoring

Track batch throughput and failures in the dashboard under Batches.