Monitor prompt caching to optimize your token usage

Datadog’s 2026 State of AI Engineering report found that system prompts accounted for 69% of input tokens in customer traces in March 2026, highlighting the cost and latency impact of repeated agent instructions, tool definitions, guardrails, and reference material. Prompt caching reduces this overhead by storing the intermediate model state for an identical prompt prefix and reusing it on later requests, though provider implementations differ: Anthropic offers automatic and explicit cache breakpoints, while OpenAI automatically caches eligible prefixes and supports explicit breakpoints on newer models. Effective caching depends on keeping shared prompt prefixes stable, placing dynamic user messages and tool results after cache boundaries, and potentially caching prior conversation history separately in multi-turn workflows. Because cache writes can cost more than uncached inputs and minor changes can invalidate cached content, teams should track cache-hit rates, token usage, costs, and latency using API metrics and request traces. Common causes of cache misses include reordered tools, context compaction, changing summaries or memory, and timestamps or request-specific metadata inserted into otherwise static prompt sections.

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