Prompt Caching on Claude: Cut Input Costs 78% (The Math Nobody Writes Down)

2026-06-10 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Anthropic's Claude large language model offers an ephemeral prefix caching mechanism that can reduce input costs by 70-90% on prefix-heavy workloads, exemplified by a 78.5% cost cut in a 10-turn RAG session. This caching system stores a reusable prefix of a prompt, which is then replayed at 0.1 times the cost of uncached tokens. Writing a 5-minute TTL cache costs 1.25 times the base rate, breaking even after the first read, while a 1-hour TTL costs 2.0 times, breaking even on the second read. Users define cache breakpoints within their prompts, with up to four allowed per request, and must ensure prefixes exceed ~1,024 tokens. Effective caching requires ordering prompt elements by volatility, placing stable content first, and avoiding anti-patterns like volatile content above breakpoints or non-deterministic ordering. Hit rates can be monitored via the "usage" field in API responses.

Key takeaway

For AI Engineers building Claude API applications like agents or RAG services, prompt caching is a fundamental architectural decision, not a simple flag. You must design your prompts with a clear volatility gradient, placing stable context at the top and dynamic elements at the bottom, using up to four breakpoints. This approach, combined with selecting the correct TTL based on your traffic patterns, will significantly cut your input costs by 70-90%. Continuously monitor your cache hit rate to ensure optimal performance and avoid silent cost increases.

Key insights

Claude's ephemeral prefix caching drastically reduces LLM input costs by reusing stable prompt segments.

Principles

• Cache big, stable, repeated context for maximum payoff.
• Order prompt elements by volatility: stable first, dynamic last.
• Invalidation cascades from top-down changes.

Method

Define cache breakpoints in prompt structure, select TTL based on inter-request timing, and monitor hit rates via API "usage" data.

In practice

• Implement "cache_control" in "system" or "tools" fields.
• Ensure deterministic ordering for cached prompt sections.
• Place user-specific content below shared, cached prefixes.

Topics

• Prompt Caching
• Claude API
• LLM Cost Optimization
• Prefix Caching
• Cache Breakpoints
• RAG Systems
• AI Agents

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

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• 5 Tips to Cut Claude Code Token Usage by 30% — Optimizing Claude Code usage through structured context and model selection significantly reduces token costs.
• Why Care About Prompt Caching in LLMs? — Prompt caching reduces LLM cost and latency by reusing results from repeated input tokens.
• How an Unexpected Reddit Spike Forced Me to Learn Prompt Caching the Hard Way — Multi-agent LLM pipelines offer significant prompt caching benefits, especially when context is passed sequentially between agents.
• If You’re Still Hitting the Claude Code 5-Hour Wall, You’re Doing It Wrong — The Claude Code 5-hour rate limit is caused by four inefficient habits: routing, delegation, caching, and hygiene, not usage volume.
• Prompt caching - Mistral AI — Prompt caching reuses shared prompt prefixes to reduce LLM inference costs and latency.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.