Practical guide
Managed RAG vs DIY: Which Costs Belong in the Comparison?
Compare managed and self-built RAG by separating embedding and generation tokens from vector search, reranking, operations, and quality.
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Use a common workload boundary
A managed RAG quote may bundle indexing, vector storage, retrieval, and generation, while a DIY estimate lists them separately. Normalize both to the same corpus size, monthly changed tokens, query volume, retrieved context, answer length, retention, and availability target.
For a product catalog that changes daily, compare two ingestion strategies. Full re-indexing of a 40-million-token corpus every week means about 160 million embedding tokens in a four-week model. Incremental indexing of 2% changed text per day means about 24 million embedding tokens over 30 days. Enter each embedding total with the same generation traffic in the RAG Cost Calculator. The token difference can justify engineering incremental updates, but only after adding the operational cost of that pipeline.
Compare quality before infrastructure
Measure retrieval recall and answer acceptance at the same latency target. A cheap configuration that returns weak passages can increase generation retries and human review. Vary chunk overlap and top-k retrieval; both affect prompt size and may affect answer quality.
The calculator covers Gemini Embedding input and Gemini 2.5 Pro short-context input and output. It excludes the vector database, reranking, cache storage, retrieval compute, grounding, networking, taxes, and discounts. Add those lines to a worksheet for each architecture. Include staffing and incident coverage if one option transfers operational work to your team.
FAQ
Is a managed RAG price directly comparable to this result?
Only if you separate the managed quote into the same components. Bundled storage, retrieval, or orchestration should not be compared with token charges alone.
What workload assumption changes cost most?
It depends on the system. Frequently changing corpora raise embedding volume; high query traffic and large retrieved contexts usually make generation input dominant. Measure both.