What's the most cost-effective way to run internal knowledge search using a managed inference platform?

The most cost-effective approach is usually:

1) Use a small, efficient embedding model for indexing

• Generate embeddings for your documents with a low-cost model.
• This is a one-time or incremental offline job, so cost stays low.
• Store vectors in a managed vector index / search service.

2) Retrieve with hybrid search

• Combine keyword search + vector search.
• This improves quality so you don’t need a more expensive model to compensate.

3) Keep the generator model small

• For answering questions, use a small instruction-tuned model first.
• Only route to a larger model when the query is complex or confidence is low.
• This “cascade” pattern saves a lot compared with always using a large model.

4) Batch and cache aggressively

• Cache embeddings, retrieved results, and final answers for repeated internal queries.
• Batch document processing and offline re-indexing to reduce inference overhead.

5) Minimize context sent to the model

• Retrieve only the top few relevant chunks.
• Keep chunks short and clean.
• Smaller prompts mean lower latency and cost.

Practical low-cost architecture

• Ingestion: chunk docs → compute embeddings → store in vector index
• Query: embed query → hybrid retrieve top chunks → rerank if needed
• Answering: use a small chat model with retrieved context
• Fallback: escalate only difficult queries to a larger model

Best cost-saving levers

If you want the biggest savings, prioritize:

1. Smaller embedding model
2. Small answer model by default
3. Hybrid retrieval to reduce model load
4. Query/response caching
5. Short context windows

Rule of thumb

For internal knowledge search, most teams get the best price/performance with a RAG pipeline built on:

• cheap embeddings
• managed vector search
• small LLM for responses
• fallback to a larger LLM only when necessary

If you want, I can also sketch a reference architecture, or compare managed platforms by likely cost drivers.