The same question twice, paid for once: installing an LLM response cache

The tutorial explains how to install and configure a community LLM response-cache plugin in Tyk AI Studio to avoid paying for identical language-model requests more than once. The plugin is delivered as a digest-pinned container image, runs as a separate process communicating through gRPC, requests explicitly approved permissions, and provides a schema-generated configuration form with settings such as cache size, entry limits, prompt normalization, tenant-aware namespace isolation, TTL, and reporting intervals. A central operational point is that installation alone does not activate the plugin: it must be attached to a specific LLM provider and then pushed to edge gateways, which use independently cached configuration snapshots. Testing two identical OpenAI requests shows an initial cache miss followed by a cache hit, reducing latency from several seconds to milliseconds while returning the stored response and exposing cache-key and TTL information. The plugin also supplies its own dashboard for cache metrics, including hit rates, misses, bypasses, and tokens saved; because the community edition stores data in each gateway’s memory, caches are not shared across multiple edge instances, unlike the Redis-backed Enterprise version.

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