HyDE (Hypothetical Document Embeddings) is a retrieval technique where, instead of embedding the raw query, you first ask an LLM to write a hypothetical answer or document, then embed that. The richer text often matches relevant passages better, improving recall on hard queries.

Why does HyDE cost more?

HyDE adds an LLM generation step before every retrieval. Standard RAG only pays for one embedding call per query, while HyDE pays for an LLM completion plus the embedding of its longer output, so cost per query rises substantially.

Is HyDE worth the extra cost?

It depends on your retrieval quality gap. HyDE helps most when queries are short, ambiguous, or phrased very differently from your documents. If standard RAG already retrieves well, the LLM overhead may not pay for itself.

Can I reduce HyDE cost?

Use a cheap fast model for the hypothetical document, cap its length, apply HyDE only to queries that standard retrieval scores as low-confidence, and cache generations for repeated queries.

Does embedding cost change with HyDE?

Slightly. You embed the generated hypothetical document instead of the short query, and that text is usually longer, so embedding tokens rise a little. The dominant added cost is the LLM generation step itself.

HyDE Embedding Cost Calculator

HyDE improves retrieval by generating a hypothetical answer before embedding — but that LLM step is paid on every query. This calculator compares HyDE against standard RAG so you can decide whether the recall gain justifies the recurring cost.

How it works

Standard RAG embeds the raw query once:

standard cost/query = query_tokens × embed_price / 1e6

HyDE adds an LLM generation step, then embeds the generated document:

hyde cost/query = (gen_input × in_price + gen_output × out_price) / 1e6
                + (gen_output × embed_price) / 1e6

The tool scales both by your daily query volume to monthly figures and shows the absolute and percentage overhead HyDE introduces.

Worked example

10,000 queries/day, query 20 tokens, HyDE generation 120 input + 200 output tokens, LLM at $0.50/1M in and $1.50/1M out, embeddings at $0.13/1M:

Standard cost/query: 20 × $0.13 / 1e6 = $0.0000026
HyDE generation: (120 × $0.5 + 200 × $1.5)/1e6 = $0.00036
HyDE embedding: 200 × $0.13/1e6 = $0.000026
HyDE cost/query: ≈ $0.000386
Monthly overhead: ~$115/month for ~148× the per-query cost

The dollar amount is modest at this volume — the question is whether the recall lift moves a business metric.

Tips

Use a cheap small model for the hypothetical document; quality of retrieval rarely needs a frontier model here.
Apply HyDE selectively — only to queries where standard retrieval returns low-confidence matches.
Cap the generated length; you are embedding it, so longer is not always better.
Model overall RAG spend with the LLM API Cost Calculator.