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RAG Chunking & Retrieval Planner

Get a chunk size, overlap, and top-k with the reasoning behind each, priced end to end — and an honest check against just putting the whole corpus in context with caching.

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  • Prices the whole pipeline — embedding, ongoing re-indexing, and per-query cost — not just the chunk size, so you see the real monthly cost before you build anything.
  • Checks the RAG pipeline against the alternative of putting the whole corpus in context with caching, and says so plainly when that's the cheaper, simpler option for your corpus size.
  • Explains WHY a given chunk size, overlap, and top-k were chosen for your document and query type, not just what the numbers are.
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Frequently asked questions

The chunking plan and every dollar figure are computed deterministically — no model call. The optional "Write a chunking strategy note" button is powered by Claude and uses one of your daily allowance of AI actions on this platform.

RAG adds real engineering cost: a vector database, an embedding pipeline, and ongoing re-indexing. If your corpus comfortably fits in a model's context window, prompt caching can make repeatedly sending the whole thing cheaper AND simpler than building retrieval at all — this tool prices both and says so directly when the numbers support it.

From the same admin-editable price list as every other cost figure on this platform, using the cheapest currently-verified embedding model on file. If none is verified yet, embedding-related figures are shown as unavailable rather than guessed.

Pricing comes from Costiva's admin-editable price list, shown on this page as "pricing last verified". Always confirm against each provider's official pricing page before making a purchasing decision.