Semantic Search Query Cost Calculator

Semantic search — finding results by meaning rather than exact keyword matching, using vector embeddings and similarity search — has become a standard feature in modern search products, and understanding its cost structure as genuinely distinct from a full RAG (retrieval-augmented generation) pipeline matters for accurate budgeting, since the two are easy to conflate but represent meaningfully different cost profiles.

Standalone semantic search — returning a ranked list of relevant results for a query, without any LLM generating a synthesized answer from those results — is a comparatively lightweight, cheap operation, which is exactly why many providers can offer it at a genuinely small per-query price. The computational work involved (embedding the incoming query, then finding the nearest vectors in an index) is far less expensive than generating fluent text token by token, the same fundamental reason embedding cost is so much lower than generation cost more broadly.

A full RAG pipeline, by contrast, uses semantic search as just one step in a larger process — the retrieved results become context fed into an LLM, which then generates a synthesized, conversational answer grounded in that retrieved information. This generation step is where the real cost concentrates in a RAG system, as covered in more depth by this site's RAG Pipeline Cost Calculator — and it's exactly why conflating 'semantic search cost' with 'RAG pipeline cost' produces a meaningfully understated budget if you're actually building the fuller, generation-augmented experience rather than a pure search feature.

Per-query pricing itself is a genuinely common billing model for search-specific services, distinct from the token-based pricing that dominates LLM billing — it reflects that a search query's cost is relatively fixed and predictable regardless of the query's specific length or complexity, unlike LLM generation, where output length (and therefore cost) varies considerably from request to request. This relative predictability is part of why per-query pricing remains popular for pure search products even as token-based pricing has become the near-universal standard for LLM services.

Worth knowing when evaluating providers: at sufficiently high, predictable query volume, some providers offer reserved-capacity or committed-use pricing that undercuts standard per-query rates meaningfully — genuinely worth asking about directly once your projected volume moves into a range where the savings would be substantial, rather than assuming the advertised per-query rate is the only pricing option realistically available for a high-traffic application.