Learn how prompt caching in Anthropic and OpenAI  models cut LLM costs and latency by reusing large, repeated prompt prefixes instead of reprocessing them on every request.

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Prompt caching is an optimization technique that stores repeated prompt segments (system instructions, tool schemas, RAG documents) so they aren't reprocessed on every LLM request. Both Anthropic and OpenAI support it, though differently: Anthropic uses explicit cache_control markers with configurable TTLs, while OpenAI handles caching automatically with optional prompt_cache_key and prompt_cache_retention parameters. Using a Kubernetes troubleshooting assistant as an example, the post shows that caching ~6,000 of 6,350 input tokens reduces per-request cost by ~68%, translating to over $200K/month in savings at 1M daily requests. Best practices include placing all static content (instructions, tool schemas, docs) at the prompt prefix and dynamic content (user queries, conversation history) at the end. DigitalOcean supports prompt caching for both providers using their standard per-token pricing.

Prompt Caching for Anthropic and OpenAI Models: Building Cost-Efficient AI Systems

Common Use Cases Where Prompt Caching Helps

A Realistic Production Prompt Caching Architecture

Prompt Caching with Anthropic Models (via DigitalOcean)

Prompt Caching with OpenAI Models (via DigitalOcean)

Cost efficient LLM deployment with DigitalOcean