How to Measure Real Savings from Prompt Caching Across AI Providers

Prompt caching reduces reprocessing costs by reusing unchanged portions of an AI model's input, such as system prompts or reference documents, across multiple requests. To accurately measure its impact, developers should run a controlled three-request experiment: one to create the cache, one to trigger a cache hit, and one to force a cache miss by altering the prefix. Key variables like model ID, token thresholds, time-to-live, and pricing must be recorded for results to be meaningful. OpenAI applies caching automatically to eligible prefixes, while Anthropic requires explicit cache boundary markers via cache_control, and the two systems use different usage field names and cost structures. Developers can verify their own cache behavior and token consumption using provider dashboards, API references, and tools like BetterToken alongside official documentation.