TL;DR

Batch processing groups multiple AI requests together instead of sending them one at a time. This reduces costs (often by 50% or more), improves throughput, handles rate limits more gracefully, and makes large-scale AI operations practical. If you are processing more than a few dozen items, batching is not optional — it is essential.

Why it matters

Imagine you need to classify 10,000 customer support tickets, generate descriptions for 5,000 products, or summarise 2,000 research papers. Sending these one at a time would take hours, cost a fortune, and almost certainly hit rate limits that bring your operation to a grinding halt.

Batch processing solves all three problems at once. Companies that integrate AI at scale — from e-commerce platforms generating product descriptions to media companies moderating user content — rely on batch processing as the backbone of their AI operations. Getting it right is the difference between a system that scales smoothly and one that collapses under its own weight.

What is batch processing?

Batch processing means collecting multiple items and processing them together as a group, rather than handling each one individually.

Think of it like doing laundry. You would not run the washing machine for a single sock. You wait until you have a full load, then wash everything at once. The machine runs the same cycle regardless of whether it contains 5 items or 50, so batching is dramatically more efficient.

In AI terms, instead of making 1,000 separate API calls (each with its own network overhead, authentication, and rate limit impact), you might make 10 calls with 100 items each, or use a dedicated batch endpoint that handles all 1,000 items in a single submission.

How batch processing reduces costs

Most AI providers now offer dedicated batch APIs with significant discounts. OpenAI's Batch API, for example, offers a 50% discount compared to synchronous requests. The trade-off is that results come back in hours rather than seconds, but for non-urgent tasks, this is an excellent deal.

Even without dedicated batch endpoints, batching reduces costs through:

  • Reduced overhead. Each API call carries fixed costs (network round trips, connection setup, authentication checks). Fewer calls means less overhead.
  • Better token efficiency. When you can include multiple items in a single prompt (like classifying 10 emails at once instead of one), you share the system prompt and instructions across all items.
  • Smarter model selection. Batch jobs are usually not time-sensitive, so you can use slower, cheaper models without affecting user experience.

Batch strategies for different scenarios

API-level batching works when the provider supports multi-item requests. You submit a file or array of requests and receive results asynchronously. OpenAI's Batch API and Google's Vertex AI batch prediction both work this way. You submit your data, get a job ID, and poll for results.

Application-level batching is something you build yourself. You collect items in a queue, group them into batches of a practical size (usually 10-100 items), process each batch, and store results. This works with any API, even those without native batch support.

Parallel processing means running multiple batches concurrently. Instead of processing batch 1, then batch 2, then batch 3 in sequence, you process all three at the same time using async/await patterns or worker threads. This dramatically reduces total processing time while still respecting per-request rate limits.

Stream processing handles items as they arrive rather than waiting to collect a full batch. You accumulate items into "mini-batches" of 10-50 items and process each mini-batch as soon as it fills up. This balances efficiency with lower latency, making it a good fit for near-real-time use cases.

Building a basic batch pipeline

A practical batch pipeline has four stages:

1. Collection. Items arrive from your application — user uploads, database records, incoming messages — and are placed into a queue. Redis, RabbitMQ, or even a simple database table can serve as the queue.

2. Batching. A worker process pulls items from the queue and groups them by size or type. You want batches large enough to be efficient but small enough that a single failure does not waste too much work. A batch size of 50-100 items is a good starting point.

3. Processing. Each batch is sent to the AI API. For parallel processing, you can run multiple batches concurrently, but always stay within the provider's rate limits. Track which items succeed and which fail.

4. Result handling. Store successful results, queue failed items for retry, and notify downstream systems that results are available.

Here is a simplified Python example:

import asyncio
from typing import List

async def process_batch(items: List[str], batch_size: int = 50):
    results = []
    for i in range(0, len(items), batch_size):
        batch = items[i:i + batch_size]
        batch_results = await call_ai_api(batch)
        results.extend(batch_results)
        await asyncio.sleep(1)  # Respect rate limits
    return results

Error handling for batch operations

The golden rule of batch error handling is: never let one failed item kill the entire batch. If item 47 out of 100 fails, process the other 99 and retry item 47 separately.

Implement these patterns:

  • Per-item error tracking. Record which items failed and why. Was it a rate limit (retry soon), a malformed input (fix and retry), or a server error (retry later)?
  • Dead letter queues. After 3-5 retries, move persistently failing items to a separate queue for manual review instead of retrying forever.
  • Partial result saving. Save results as each batch completes, not just at the end. If your process crashes halfway through 10,000 items, you do not want to start over from zero.
  • Idempotency. Design your pipeline so that reprocessing an item produces the same result. This makes retries safe and recovery straightforward.

Monitoring your batch jobs

Without monitoring, you are flying blind. Track these metrics for every batch job:

  • Progress: How many items processed out of total?
  • Success rate: What percentage of items succeeded?
  • Processing time: How long per item and per batch?
  • Cost: How much has this job spent so far?
  • Error distribution: Are failures random or concentrated on specific item types?

Set up alerts for unusual patterns. If your success rate drops below 95%, or if processing time per item doubles, something has changed and you need to investigate.

Scheduling and off-peak processing

If your batch jobs are not time-sensitive, schedule them during off-peak hours. Some providers offer lower pricing during off-peak times, and you are less likely to compete with your own real-time traffic for rate limit headroom.

Common scheduling patterns include nightly runs (process the day's accumulated items overnight), hourly micro-batches (good for items that need results within a few hours), and weekend processing for large historical backfills.

Common mistakes

Processing items one at a time when batching is available. This is the most expensive mistake. Even batching 10 items at a time is dramatically more efficient than processing individually.

Not respecting rate limits. Firing off hundreds of parallel requests will get you throttled or temporarily banned. Always include rate limiting in your batch logic.

Losing progress on failure. If your script crashes after processing 8,000 of 10,000 items and you have not saved incremental results, you have to start over. Always checkpoint your progress.

Using the same batch size for everything. Different tasks have different optimal batch sizes. Short classification tasks can handle larger batches. Long-form generation tasks need smaller batches. Experiment to find the sweet spot.

Ignoring cost until the bill arrives. Run a small test batch first, calculate the per-item cost, and multiply by your total item count before launching a full job. Surprises on your API bill are never fun.

What's next?

Build on your batch processing knowledge with these related guides: