Assess the complexity and risk of building a natural language to SQL system over your enterprise data. Get a recommended architecture pattern and identify key risks before you build.
What the calculator actually models
Inputs:
- Schema size — table count, column count
- Join complexity — how many tables a typical query touches
- Data freshness requirements (real-time, batch, eventually consistent)
- Query diversity — narrow analytical workload vs. open-ended self-serve
- Query type mix — read-only analytics vs. transactional mutations
- Error tolerance — research dashboard vs. financial reporting
Outputs:
- Complexity score — Low / Medium / High / Critical
- Risk breakdown — retrieval errors, SQL injection via natural language, hallucinated columns
- Recommended architecture — naive prompting, RAG with schema filtering, few-shot prompting, agent-based validation, hybrid
- Estimated accuracy baseline for each pattern at your complexity
The most useful output is the risk breakdown. “Hallucinated columns” is the failure mode that turns into silent data corruption — the model invents a column name, the query somehow runs, and the dashboard now shows wrong numbers nobody can trace.
NL-to-SQL on a 4-Table Demo Is a Trick: How to Tell Whether You Need an Agent — SuperML.dev
The same models that score 86% on Spider 1.0 score 10-17% on real enterprise schemas. NL-to-SQL is an architecture problem, not a model problem — here's how to scope yours.
superml.dev
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