What is Batch Processing?
Batch Processing — Processing a large volume of data or AI inferences all at once rather than in real-time.
Batch processing runs AI inference on large datasets all at once rather than in real-time. It is significantly cheaper than real-time inference because it can use spot instances, optimize GPU utilization, and queue requests efficiently. Ideal for reports, data enrichment, and scheduled analysis.
Frequently Asked Questions
When should I use batch vs. real-time processing?
Use batch for scheduled tasks like nightly reports, bulk data enrichment, and email categorization. Use real-time for interactive applications like chatbots and search.
How much cheaper is batch processing?
Typically 50-75% cheaper than real-time inference. OpenAI’s batch API offers 50% discounts. Self-hosted batch jobs can use cheaper spot GPU instances.
What tools support batch AI processing?
OpenAI’s Batch API, AWS Batch, Apache Spark with ML libraries, and custom job queues using tools like Celery or Ray. Most cloud AI services offer batch-specific endpoints.