Batching strategies group multiple AI requests into single API calls to reduce overhead costs. Instead of making 100 separate calls, you make one call with 100 items. Each API call has fixed overhead for authentication, connection setup, and parsing. Batching amortizes these costs across many items, reducing total costs by 80% or more while improving throughput.
Every customer inquiry triggers its own API call. Each one waits in line.
Your AI costs spike with volume. Latency climbs as requests pile up.
You are paying per-request overhead 1,000 times when you could pay it once.
The most expensive part of an AI call is often not the AI itself. It is the overhead around it.
OPTIMIZATION LAYER - Reduce costs and improve throughput by grouping work intelligently.
Batching strategies group multiple AI requests together and process them in a single operation. Instead of making 100 separate API calls, you make one call with 100 items. The work gets done, but with dramatically less overhead.
The key insight is that many AI operations have fixed costs per call - authentication, connection setup, prompt parsing, and response serialization. When you batch, you pay these costs once instead of repeatedly. The savings compound as volume increases.
Batching is not about making AI faster. It is about making AI cheaper and more predictable. A system that processes 10,000 items in 100 batches of 100 is fundamentally different from one that makes 10,000 individual calls.
Batching solves a universal efficiency problem: how do you reduce per-item overhead when processing many similar things? The same pattern appears anywhere volume creates repetitive costs.
Collect items until you have enough to justify a batch. Process the batch as a single operation. Distribute results back to their original requestors. Pay overhead once, benefit many times.
You need to enrich 50 leads. Select a batch size to see how overhead costs change.
Collect items for a fixed window
Accumulate requests for a set period (e.g., 5 seconds) then process everything collected. Simple to implement and provides predictable latency bounds.
Process when you have enough items
Wait until a minimum number of items accumulate (e.g., 50 requests) then process the batch. Maximizes efficiency per batch at the cost of variable timing.
Whichever threshold comes first
Process when either a size threshold OR a time limit is reached. Combines the benefits of both approaches with slightly more complexity.
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The marketing team uploaded a lead list that needs company data, role verification, and qualification scoring. Individual API calls would take 8 minutes and cost $25. Batching completes the work in 45 seconds for $3.
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This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You batch customer-facing chat responses to save costs. Now users wait 5 seconds for a response that should take 500ms. The savings are not worth the degraded experience.
Instead: Reserve batching for background tasks and async workflows where latency is acceptable. Real-time interactions should remain individual.
One malformed item in a batch of 100 causes the entire batch to fail. 99 valid items get dropped. You retry the whole batch, including the bad item. Infinite loop.
Instead: Design for partial success. Track which items succeeded, which failed, and why. Retry only failures, ideally in a separate batch.
You batch together simple classifications with complex analysis tasks. The simple ones wait for the slow ones. Or the prompt gets confusing because items need different treatment.
Instead: Group by task type. Batch similar requests together. Different complexity levels or different output formats should be separate batches.
Batching groups multiple AI requests into a single API call. Instead of sending 100 separate classification requests, you send one request containing 100 items. The AI processes all items together and returns all results at once. This reduces overhead costs dramatically because connection setup, authentication, and request parsing happen once instead of 100 times.
Use batching when you have high volumes of similar requests where latency is not critical. Background processing tasks like document classification, data enrichment, and report generation are ideal candidates. Avoid batching for real-time user interactions where adding even 2-3 seconds of latency would degrade experience.
The biggest mistake is batching latency-sensitive operations where users expect immediate responses. Another is failing to handle partial failures - when one item in a batch fails, you need to retry just that item, not the whole batch. Also avoid mixing different task types in one batch, as they may need different prompts or models.
Batching typically reduces costs by 70-90% for high-volume operations. The savings come from amortizing fixed per-call overhead across many items. If each call has 100ms of overhead, 100 individual calls add 10 seconds of overhead. One batched call adds just 100ms. Token costs stay the same, but infrastructure costs drop dramatically.
Time-based batching collects requests for a fixed window (e.g., 5 seconds) then processes whatever has accumulated. Size-based batching waits until a minimum count is reached (e.g., 50 items) before processing. Hybrid approaches trigger on whichever threshold comes first, combining predictable latency with efficient batch sizes.
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Choose the path that matches your current situation
You are making individual API calls for everything
You have some batching but it is not optimized
Batching is working but you want maximum efficiency
You have learned how to group AI requests to reduce overhead and improve efficiency. The natural next step is understanding how to track and attribute the costs you are optimizing.