What's the most cost-effective way to run semantic search using an LLM API provider at scale?

The most cost-effective approach is usually not to use the LLM API for every search query. Instead:

Best-practice architecture

1. Use an embedding model once per document/chunk

   • Generate embeddings for your corpus offline or during ingestion.
   • Store them in a vector index / vector database.

2. At query time, embed the user query

   • Use a small, cheap embedding model.
   • Compare the query embedding against your precomputed document embeddings.

3. Only use the LLM for reranking or answer generation when needed

   • Retrieve top-K candidates with vector search.
   • If needed, pass only those candidates to a more expensive LLM for reranking, summarization, or final response.

Why this is cheapest

• Embeddings are much cheaper than using a chat/completion model for retrieval.
• You pay the expensive model cost only on:
  • ambiguous queries,
  • reranking,
  • or final synthesis.
• Most search workloads are retrieval-dominant, so cheap embeddings + vector search give the best scale economics.

Cost-saving tactics

• Chunk documents intelligently: don’t embed huge blobs or tiny fragments.
• Deduplicate similar content before embedding.
• Cache query embeddings for repeated searches.
• Use a two-stage pipeline:
  • Stage 1: vector search over all content
  • Stage 2: cross-encoder/LLM rerank over top 20–100 results
• Batch embedding requests if your provider supports it.
• Choose the smallest embedding model that preserves acceptable recall.
• Re-embed only changed content instead of the whole corpus.

When to use the LLM directly

Use a full LLM search approach only if:

• your corpus is very small,
• semantic accuracy matters more than cost,
• or you need reasoning over retrieved text, not just retrieval.

Rule of thumb

For large-scale semantic search:

• Embeddings + ANN vector index = primary solution
• LLM = optional second-stage tool, not the retrieval engine

If you want, I can also give you:

1. a reference architecture,
2. a cost model formula, or
3. a provider-by-provider comparison.