anth-performance-tuning

name	anth-performance-tuning
description	Optimize Claude API performance with prompt caching, model selection, streaming, and latency reduction techniques. Use when experiencing slow responses, optimizing token usage, or reducing time-to-first-token in production. Trigger with phrases like "anthropic performance", "claude speed", "optimize claude latency", "anthropic caching", "faster claude responses".
allowed-tools	Read, Write, Edit, Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","ai","anthropic"]
compatible-with	claude-code

Anthropic Performance Tuning

Overview

Optimize Claude API latency and throughput via prompt caching, model selection, streaming, and request optimization. The biggest wins come from prompt caching (90% input cost reduction) and model selection (Haiku is 4x faster than Sonnet).

Prompt Caching (Biggest Win)

import anthropic

client = anthropic.Anthropic()

# Mark long, reusable content with cache_control
# Cached content: 90% cheaper on subsequent requests, near-zero latency for cached portion
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are an expert on the following 50-page document: ...<long document>...",
            "cache_control": {"type": "ephemeral"}  # Cache this block
        }
    ],
    messages=[{"role": "user", "content": "What does section 3.2 say?"}]
)

# Check cache performance
print(f"Cache read tokens: {message.usage.cache_read_input_tokens}")   # Free/cheap
print(f"Cache creation tokens: {message.usage.cache_creation_input_tokens}")  # First call only
print(f"Uncached input tokens: {message.usage.input_tokens}")

Cache requirements: Minimum 1,024 tokens for Sonnet/Opus, 2,048 for Haiku. Cache lives for 5 minutes (refreshed on each hit).

Model Selection for Speed

Model	Speed	Cost (per MTok in/out)	Best For
Claude Haiku	Fastest