TL;DR

Batch processing groups multiple AI requests together for efficiency. It reduces costs, improves throughput, and handles rate limits better than one-at-a-time processing.

What is batch processing?

Processing multiple items in groups rather than individually.

Example:

  • Instead of: 1000 separate API calls
  • Batch: 10 calls with 100 items each

Benefits

  • Lower cost (batch discounts)
  • Better throughput
  • Easier rate limit management
  • Reduced overhead

Batch strategies

API-level batching:

  • Some APIs support multi-item requests
  • Process 50-100 items per call
  • Check API docs for limits

Application-level batching:

  • Queue items
  • Process in groups
  • Handle errors per batch

Parallel processing:

  • Multiple concurrent batches
  • Respect rate limits
  • Use async/await or threading

Implementation patterns

Queue-based:

  1. Add items to queue
  2. Worker pulls batches
  3. Processes and stores results

Scheduled:

  • Run batch jobs hourly/daily
  • Good for non-urgent tasks
  • Cheaper off-peak pricing

Stream processing:

  • Process as items arrive
  • Mini-batches (10-100 items)
  • Balance latency and efficiency

Error handling

  • Retry failed items
  • Don't fail entire batch for one error
  • Log and alert on persistent failures

Monitoring

  • Track batch size
  • Monitor processing time
  • Alert on failures or slowdowns

What's next

  • AI Workflows
  • API Integration
  • Cost Optimization