Question 1

How is a live-search answer engine's cost structure different from a static RAG cost calculator?

Accepted Answer

A static RAG system retrieves from a corpus you already indexed — the retrieval step is a vector lookup against your own database, and once you've paid to build that index, running a query against it costs essentially nothing beyond the embedding of the question itself. An answer engine in the Perplexity / You.com / Bing Copilot mold works differently: instead of a pre-built index, every single query triggers a live call to a third-party web search API, and that API charges per query or per 1,000 queries, every time, forever. There's no equivalent line item in a static RAG cost model, because static RAG has no live search step to pay for. This calculator adds that recurring search-API fee on top of the usual input/output token cost of feeding the returned snippets into the LLM, so the two costs stack rather than substitute for each other — you're paying for a search engine call and an LLM completion on every query, not one or the other.

Question 2

Why isn't retrieving more snippets for a better answer free?

Accepted Answer

Every snippet you pull back from the search API and stuff into the LLM's context is input tokens, and input tokens are priced per token. Doubling the number of snippets you retrieve — going from, say, 8 to 16 — roughly doubles the token count contributed by search results, which increases the input side of the LLM bill in direct proportion. It doesn't change the per-query search API fee, which is typically charged per call regardless of how many results you ask for, but it does change everything downstream of that call. Teams tune top-K upward because more retrieved context tends to reduce missed citations and improve answer completeness, and that's a real, defensible reason to do it — but it's a token cost decision, not a free quality lever, and the cost scales linearly with however many snippets you decide to keep.

Question 3

Which providers charge per-query search fees, and roughly how much do they cost?

Accepted Answer

Brave Search API, Tavily, SerpApi and Bing Search (via Azure) are the commonly used options for wiring live web search into an LLM answer pipeline, and all of them price on a per-query or per-1,000-query basis rather than a flat subscription. Roughly speaking, per-1,000-query pricing across these providers tends to land somewhere in the low single digits to around ten dollars, depending on the provider, the tier you're on, and whether you need extras like fresher indexes or higher rate limits — treat any specific number here as illustrative rather than current, since search-API pricing changes and varies by plan more than LLM token pricing does. The default in this calculator, $5 per 1,000 queries, is a labeled illustrative assumption in that range, not a quote from any single provider — swap in your actual contracted rate before using this for a real budget.

Question 4

How does this differ from the RAG Cost Calculator and from a plain LLM pricing comparison?

Accepted Answer

The RAG Cost Calculator prices a system built around a corpus you control and have already indexed — documents you own, chunked and embedded once, retrieved by vector similarity with no per-query fee to any outside search provider. This calculator prices a different architecture: no pre-built corpus at all, just a live call to a third-party web search API on every single query, which is why it has a cost line — the search API fee — that the RAG calculator simply doesn't model, because static RAG doesn't have it. A plain LLM pricing comparison is one step further removed still: it prices only the model's per-token input/output rates with no retrieval step of any kind, static or live, so it's the right tool when you're just calling a model directly with a prompt and no external context. Use this calculator specifically when your architecture is 'search the live web on every query, then synthesize a cited answer from what comes back' — that's the Perplexity-style pattern with a cost structure distinct from both of the others.

原価ベース	コスト/クエリ	月額費用
静的 RAG 相当 (LLM 合成のみ、ライブ検索手数料なし)	—	—
実際のアンサーエンジンのコスト (LLM 合成 + ライブ検索 API)	—	—

AI検索/応答エンジンのコスト計算ツール

静的な RAG 相当コストと実際のアンサーエンジンコストの比較

取得したスニペットごとのコスト (上位 K)

これが他のツールにどのように接続されるか

この計算機の仕組み

静的な RAG 相当コストと実際のアンサー エンジン コストの比較

取得したスニペットごとのコスト (上位 K)

これが他のツールにどのように接続されるか

この計算機の仕組み

静的な RAG 相当コストと実際のアンサーエンジンコストの比較