The most cost-effective approach is usually:

1) Put a semantic layer on top of your databases

Instead of trying to let an LLM directly query every source, create a single governed metadata/semantic layer that defines:

• business metrics and dimensions
• joins and relationships
• row-level/security rules
• approved tables/views

This keeps the LLM from needing full raw-schema access and reduces bad/expensive queries.

2) Use an LLM only as a query translator, not the database brain

Have users ask questions in natural language, then the system:

1. interprets the request
2. maps it to the semantic layer
3. generates SQL for the right source or federated query
4. returns the result and optionally a plain-English explanation

This is much cheaper than fine-tuning a model for each database or trying to build a custom NLP system from scratch.

3) Start with an open-source or low-cost model

For cost-effectiveness:

• use a smaller hosted model or open-source model for intent-to-SQL
• reserve larger models for ambiguous questions only
• cache common question patterns and results

Often, a smaller model plus good schema/semantic context beats a large model with no structure.

4) Use a federated query engine only where needed

If data is spread across multiple databases, use a federation/query virtualization layer when:

• joining across sources is necessary
• data duplication would be too expensive
• latency is acceptable

But for cost and performance, it’s often better to:

• replicate only the key analytical data into one warehouse/lakehouse
• keep operational databases as sources of truth

That is usually cheaper than doing heavy real-time cross-database joins everywhere.

5) Add guardrails to control cost

Important cost savers:

• query limits and timeouts
• pre-aggregated views
• cached query results
• “did you mean?” clarification before running expensive queries
• restrict to curated datasets for business users

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Best practical pattern

For most organizations, the lowest-cost, scalable setup is:

Business users → NL interface → semantic layer → SQL generation → warehouse/lakehouse (with limited federation to source DBs)

This gives:

• easier governance
• lower query cost
• fewer hallucinated SQL errors
• less engineering effort than building a custom system per database

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If you want the absolute cheapest MVP

Build it in this order:

1. pick 1–2 high-value data sources
2. expose only curated views
3. add a simple NL-to-SQL layer using an off-the-shelf LLM
4. log questions and improve mappings
5. expand to more databases only after usage proves value

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If you want, I can also give you:

• a reference architecture
• a buy vs build comparison
• or a stack recommendation for AWS/Azure/GCP/open-source.