Build a Versioned Cache Key for Model Responses

A hash of the prompt is rarely a sufficient cache key.
Two users may submit identical text under different permissions or product contexts. A prompt template, model alias, retrieved context, output schema, or application policy may have changed since the original result.
Build a canonical cache key from tenant scope, task ID, model configuration version, prompt version, input digest, context version, schema version, and policy version.
Do not place raw prompts or sensitive identifiers directly in the key or ordinary logs. Use an appropriate digest over canonical application inputs.
VectorNode is our product. Its public page describes one-key access to GPT, Claude, Gemini, DeepSeek, Qwen, Midjourney, Kling, and other models, with unified credits, transparent pricing, and usage logs. It can sit behind an application-owned response cache:

https://www.vectronode.com
Disclosure: VectorNode is our product, and this link is included for readers who want to explore it.
On a hit, return the stored response with provenance, creation time, and expiry metadata. On a miss, call the endpoint, validate the output, and store it only if task policy permits reuse.
Do not cache every workflow. Private, rapidly changing, or high-impact requests may require different handling.
Usage logs can help correlate model calls, but they do not replace cache isolation, invalidation, or retention policy.