Text-to-SQL Tools Compared: What Works, What Doesn't

Short answer: General-purpose chat assistants write good SQL when you hand them the schema, but they don’t know your actual data unless connected to it. Dedicated text-to-SQL products and BI copilots connect directly to your warehouse and can execute and validate what they generate, which fixes correctness at the cost of an extra dependency and usually a subscription. Pick based on whether you need occasional help writing a query or an always-on interface for business users.

“Just ask the AI to write the SQL” hides a real range of tools with different strengths. Here’s how they actually differ.

What are the three categories?

General chat assistants — ChatGPT, Claude, GitHub Copilot Chat, used directly. You paste in your schema (or a description of it) and ask for a query. They’re flexible and available everywhere, but they know nothing about your actual database until you tell them, and they can’t run the query themselves unless you’re using a version with code execution or a connected tool.

BI-embedded copilots — assistants built into the reporting tool itself, like Power BI Copilot, that can see the model you’re working in and generate DAX or visuals directly inside the tool. They know your model’s structure but are scoped to that tool’s use cases.

Dedicated text-to-SQL / semantic-layer chat products — standalone tools built specifically to translate natural language into SQL against a connected warehouse, often layered on top of a semantic layer that defines your metrics once. They can execute, validate, and sometimes show their generated SQL directly in a business-user-facing interface.

What does each do well?

• Chat assistants are best for a one-off “help me write this query” moment, code review of SQL you already wrote, or explaining what an unfamiliar query does. Low friction, no setup, works with copy-paste.
• BI copilots are best when you’re already deep in a specific tool and want help staying inside it — asking for a DAX measure or a quick visual without leaving the report. See Power BI Copilot: what it can and can’t do today for where it actually holds up.
• Dedicated text-to-SQL products are best when the goal is letting non-technical stakeholders ask questions of the data directly, safely, and repeatedly — not a one-time favor for an analyst who already knows SQL.

Where does each break?

• Chat assistants don’t know your actual data. They can write syntactically correct SQL against a schema you describe, but they can’t tell you if a join will fan out on your real data, and they can’t catch a wrong assumption about what a column actually contains. See Prompting an LLM for SQL you can trust for how to close that gap.
• BI copilots are scoped to the tool. They generally can’t reach outside the model you built, and their quality depends heavily on how well your model is structured — a messy model produces messy Copilot output.
• Dedicated text-to-SQL products inherit whatever ambiguity exists in your metric definitions. If “active customer” isn’t defined consistently, the tool will confidently generate a query using its own guess, which may not match what a human on your team means by the term. A semantic layer that defines metrics once is what actually fixes this — see What is a semantic layer, and why AI analytics needs one.

What questions should I ask before adopting one?

1. Does it show the generated SQL, not just the answer? If a business user can’t see (or an analyst can’t audit) the actual query, you have no way to catch a wrong join or a misapplied filter.
2. Does it respect row-level security and existing permissions? A tool that bypasses your data’s access controls to answer a natural-language question is a governance problem waiting to surface.
3. Does it use your metric definitions, or invent its own? Ask it the same business question two different ways and see if the numbers agree. If they don’t, it’s guessing at definitions rather than using agreed ones.
4. What happens when it’s wrong? Every one of these tools will eventually generate an incorrect query. What matters is whether that’s easy to catch (visible SQL, sensible defaults, sanity checks) or invisible (a clean-looking chart built on a broken join).
5. Who’s accountable for output that reaches a decision-maker? A tool aimed at self-service for non-technical users needs a clearer answer to this than a tool used by an analyst who’s already checking their own work.

Which should I pick?

My take: if you’re an analyst who already writes SQL, a general chat assistant with good prompting habits gets you most of the value at no extra cost — the tool isn’t replacing your judgment, it’s speeding up the typing. If the goal is genuinely self-service for people who don’t write SQL, a dedicated product connected to a real semantic layer is worth the investment, but only after the metric definitions it will rely on are actually agreed and documented — otherwise you’ve automated the disagreement, not resolved it.

If you’re setting up your own workflow to work alongside an LLM on real analysis rather than adopting a packaged product, Claude Code for data analysis: a beginner setup is a practical starting point.

Thinking by Anthony, written with Claude.

Part of: SQL & Local Analytics, AI for Analysts