AI Batch Processing: Best Practices for Scale

Processing documents one at a time is slow and expensive. Learn how to batch process with AI efficiently, handling thousands of items without breaking your workflow.

Why Batch Processing Matters

❌ Sequential Processing

Time: 1000 docs × 3s = 50+ min
Cost: High API overhead
Risk: One failure halts all

✓ Parallel Processing

Time: 1000 docs = ~1 min
Cost: Better token usage
Risk: Isolated failures

Batch Processing Architecture

The Iterator Pattern

Use Iterator nodes to split arrays into parallel execution threads. Each thread processes independently, then results are collected.

Input Array [100 items]
        ↓
Iterator (splits into 100 threads)
        ↓
AI Processing (runs 100× parallel)
        ↓
Collector (gathers all results)
        ↓
Output Array [100 results]

Chunking Strategies

For very large datasets, process in chunks:

Size-based

Process 100 items per batch

Time-based

Process for 5 minutes, then checkpoint

Resource-based

Limit concurrent API calls to 50

Error Handling

Retry Logic

Implement exponential backoff for transient failures:

1 First retry: 1 second delay

2 Second retry: 2 second delay

3 Third retry: 4 second delay

✕ Max retries: Move to dead-letter queue

Partial Failure Handling

💡 Key Principle: Don't let one bad document kill the entire batch.

Log failures with context for debugging
Continue processing remaining items
Return partial results with failure report

Optimization Techniques

Skip Already Processed

Track processed document IDs to avoid duplicate work:

// Check if document was processed
if (dataset.contains(document.id)) {
  skip() // Already processed
} else {
  process(document)
  dataset.save(document.id, result)
}

Smart Batching

Group similar documents for better AI context:

Same document type together
Similar length documents together
Same language documents together

Token Optimization

Truncate irrelevant content before processing
Use summaries instead of full documents when possible
Cache common prompts and instructions

Monitoring and Observability

📊

Items/minute

⚠️

Error rate

💰

Token usage

⏱️

Processing time

Getting Started with Batch Processing

Evaligo's visual workflow builder makes batch processing simple. Use Iterator nodes for parallelization, Dataset nodes for checkpointing, and built-in error handling for reliability.