Semantic cache vs vector database: they solve different problems
A vector database is built for large-scale retrieval. A semantic cache is built for safe answer reuse. Using one for the other's job is where teams get burned.
It's a common instinct: 'I'll just put my answers in a vector DB and reuse the closest match.' It works in a demo and leaks in production. The two tools optimize for different things, and the difference matters most exactly where it costs you money or trust.
A vector database is optimized for recall at scale, finding the most similar items across millions or billions of vectors. A semantic cache is optimized for safe reuse, deciding whether the closest match is actually the same question, and refusing when it isn't.
What a cache adds on top of similarity
Crowkis layers structural intent matching, per-intent adaptive confidence thresholds, freshness/TTL control, tenant isolation on every entry, and a confidence score returned with each hit, on top of vector search. Those are the checks that stop a cache from confidently serving the wrong answer to a slightly different question.
Dedicated vector databases still lead on general-purpose retrieval at scale, and Crowkis is honest about that. If your job is searching a huge corpus, use a vector DB. If your job is reusing LLM answers cheaply and safely, that's a semantic cache, and you can run both.
Use a vector DB to find things. Use a semantic cache to decide when finding something means you can skip the model.