90% discount on cached tokens slashes OpenAI API expenses, while 1,024 tokens prefix threshold unlocks significant latency gains. This guide reveals how prompt caching works, its technical mechanics, and a practical Python implementation. The tutorial demonstrates achieving 80% faster responses and 90% cost reductions for repeated prompts, though pitfalls like cache key limitations exist.

Prompt caching operates at the token level, storing pre-fill computations to avoid redundant LLM processing. OpenAI introduced this feature in October 2024, offering 5-10 minute in-memory retention or extended 24-hour options. The Python example shows how a long system prompt (artificially boosted to 4,616 tokens) triggers caching, dramatically speeding subsequent requests for similar queries.

While the API automates cache reuse, explicit control via `prompt_cache_key` isn't yet available in the Python SDK. Success hinges on meeting the 1,024 token prefix requirement and avoiding cache misses due to prefix mismatches or retention limits. This technique is vital for high-traffic AI applications seeking efficiency gains.