Glossary
Semantic caching
Reusing a previous answer when a new question means the same thing, not just when it matches exactly.
Exact-match caching misses paraphrases. Semantic caching embeds the query and reuses a cached answer when similarity clears a threshold.
It pays for itself immediately in support assistants and internal search, where the same question arrives in many phrasings.
It also needs an invalidation story. Cached answers about policies, prices or availability go stale, and a cache with no expiry eventually becomes a source of confidently wrong answers. Tie the cache lifetime to how fast the underlying facts actually move.
Scope the cache carefully in multi-tenant systems. A cached answer generated for one customer must never surface for another, so the tenant belongs in the cache key rather than being assumed away.
Commonly misunderstood: The similarity threshold needs care. Set it loosely and users get answers to a question they did not ask.
Related terms, in context
The concepts you almost always meet alongside semantic caching.
- Inference cost optimisation
- Reducing what AI features cost to run, usually 40 to 70% without measurable quality loss.
- Embedding
- A numeric vector representing text, images or audio, positioned so similar things sit close together.
Where this shows up in our work
Semantic caching is not an abstraction for us. It is a decision we make on live projects. It shows up most directly in llm cost optimisation, where getting it wrong has a cost someone can measure.
If you are evaluating a vendor on this, the useful question is not whether they can define the term. It is what they measure, what they would refuse to do, and what happens in their system when the assumption behind semantic caching stops holding.
Questions
What is Semantic caching?
Reusing a previous answer when a new question means the same thing, not just when it matches exactly.
What do people get wrong about semantic caching?
The similarity threshold needs care. Set it loosely and users get answers to a question they did not ask.
Does Orqent Labs build this?
Yes, LLM Cost Optimisation. We work across India, covering all 19,238 PIN codes remotely.
Building something that involves semantic caching?
We will tell you honestly whether it is the right approach for your problem.
Or email bd@dtrasglobal.com · call +91 74118 77878