Infrastructure

Batch Processing

Quick Answer: Running multiple AI model requests as a group rather than one at a time.

Batch Processing is running multiple AI model requests as a group rather than one at a time. Batch processing trades latency for throughput and cost savings, processing hundreds or thousands of prompts in a single job at significantly reduced per-token pricing (typically 50% off).

Example

Classifying 10,000 customer support tickets: instead of making 10,000 individual API calls at full price, you submit them as a batch job. OpenAI's Batch API processes them within 24 hours at 50% the normal cost. 10,000 tickets at $0.005 each = $25 instead of $50.

Why It Matters

Batch processing cuts AI costs in half for any workload that doesn't need real-time responses. Data processing, content generation, document analysis, and evaluation pipelines all benefit. It's the first optimization most teams implement at scale.

How It Works

Batch processing in AI sends multiple requests to a model simultaneously or in queued batches rather than one at a time. This approach trades latency for cost efficiency and throughput. Most model providers offer batch APIs with 50% discounts compared to real-time pricing.

Batch processing is ideal for tasks that don't need immediate results: analyzing a dataset of 10,000 customer reviews, classifying a backlog of support tickets, generating product descriptions for an entire catalog, or extracting structured data from a document archive.

Key considerations include: batch size limits (API providers cap batch sizes), error handling (some items in a batch may fail while others succeed), rate limiting (batch APIs still have rate limits, just higher ones), and result management (storing and reconciling results from potentially out-of-order batch completions).

Common Mistakes

Common mistake: Processing items one-by-one when a batch API is available

Check if your model provider offers a batch API. OpenAI's Batch API offers 50% cost reduction. For large jobs, the savings are substantial.

Common mistake: Not implementing retry logic for failed items within a batch

Batch processing will have partial failures. Track which items succeeded and which failed, then retry only the failures in subsequent batches.

Career Relevance

Batch processing skills are essential for data engineers and ML engineers working with AI at scale. Companies processing large datasets through AI models need engineers who can design efficient batch pipelines with proper error handling and cost optimization.

Related Terms

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