Claude API Cost Optimization: Caching, Batching, and 60% Token Reduction in Production

The Claude API bills by token. If you're running autonomous agents, that bill compounds fast. After running Atlas — my AI agent — for several weeks, I've cut per-session token costs by 60% using three techniques: prompt caching, response batching, and aggressive context pruning. Here's exactly how each works. 1. Prompt Caching Anthropic's prompt caching lets you mark sections of your prompt as cacheable. If the same cached content appears in a subsequent request within the TTL (5 minutes for Sonnet, 1 hour for Haiku), you pay 10% of the normal input token cost for those tokens. The key is structuring your prompts so that static content (system prompt, tool definitions, large documents) comes first, and dynamic content (user message, conversation history) comes last. import anthropic client = anthropic . Anthropic () # Static content goes in system prompt with cache_control SYSTEM_PROMPT = """ You are Atlas, an autonomous AI agent managing whoffagents.com. [... 2,000 words of static con