Production LLM applications see the same intents phrased in many ways. A support assistant might receive “Can I get a refund after buying the monthly plan?”, “Is the monthly subscription refundable?”, and “Can I cancel the plan and get my money back?”. The wording differs but the answer is identical. Without a semantic cache each variation triggers a full model call: input tokens are processed, output tokens are generated, and the user waits. Prefix caching only saves part of the work; the request still reaches the LLM and the full answer is decoded. This means every paraphrase costs the same as a brand‑new query, driving up latency and billable tokens.
Redis LangCache solves this by moving the cache outside the model. Before calling the LLM the application sends the prompt to the LangCache service. The service creates an embedding for the prompt and runs a vector search against previously stored prompt‑response pairs. If a stored entry exceeds the similarity threshold, the cached response is returned instantly and no LLM call occurs. On a miss the application queries its chosen LLM as usual, then stores the new prompt and response for future matches. Embedding generation is handled by the service, with default models or a bring‑your‑own option. Behavior is tuned with similarity thresholds, TTLs, and eviction policies, and hit rates are visible in the Redis Cloud console.
Teams using LangCache report up to 90% lower API costs and response times up to 15x faster for cache hits. The solution works with any LLM provider and any language, requires only a REST call or the provided SDKs, and fits into existing RAG or support‑assistant pipelines without rewriting core logic.
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