How much can batching save on costs?

Savings vary by provider and use case. Embedding batching can save 50-80% versus individual calls. Some APIs offer batch discounts (OpenAI batch API: 50% off). The key savings come from reduced overhead and better rate limit usage.

What is the optimal batch size?

It depends on: API limits, memory constraints, latency requirements, and item sizes. Start with provider recommendations (often 50-100 items) and experiment. Monitor memory usage and adjust.

How does batching affect latency?

Batching increases latency for individual items (must wait for batch to fill) but increases throughput overall. For real-time applications, use smaller batches or time-based triggers.

Can I batch LLM completions?

Yes, some APIs support batch completion. OpenAI's Batch API processes multiple requests at 50% cost with 24-hour turnaround. For immediate needs, you can make parallel API calls instead of true batching.

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Batching

Processing multiple requests or data points together in a single operation rather than one at a time. This improves throughput and efficiency in AI systems.

In-Depth Explanation

Batching is the practice of grouping multiple items together for processing in a single operation, rather than handling each individually. This fundamental optimisation technique can dramatically improve AI system efficiency.

Why batching matters:

Cost reduction: API calls often have per-request overhead
Throughput increase: More items processed per unit time
Resource efficiency: Better GPU/CPU utilisation
Rate limit management: Fewer requests within limits

Types of batching:

Request batching: Multiple inputs in one API call
Embedding batching: Generate multiple embeddings together
Inference batching: Process multiple prompts simultaneously
Database batching: Bulk inserts/queries

Batching strategies:

Fixed size: Wait for N items, then process
Time-based: Process accumulated items every X seconds
Hybrid: Whichever threshold is reached first
Dynamic: Adjust batch size based on load

Trade-offs to consider:

Larger batches = better efficiency but higher latency
Smaller batches = lower latency but more overhead
Memory constraints limit maximum batch size

Business Context

Batching can reduce API costs by 50-80% and significantly speed up bulk processing tasks like document analysis or embedding generation.

How Clever Ops Uses This

We implement intelligent batching strategies for Australian businesses, optimising the balance between cost savings and response time for each use case.