Text-to-SQL tools compared: the complete 2026 guide

The text-to-SQL market has gone from a research curiosity to a crowded product category in under two years. Every BI tool now has an AI feature. Every AI tool now claims it can query your database. Most of them are either vaporware, wrappers with thin accuracy, or tools that work only in narrow conditions.

This guide cuts through the noise. We tested six tools against the same 18-table OLTP Postgres schema with 30 business questions. We're the team behind iDBQuery, so we've stated the bias — we've flagged where every competitor wins.

The tools we tested

1. iDBQuery — multi-source AI assistant (databases, Excel, PDFs, BIM, ERP)
2. Vanna AI — open-source Python library + optional cloud UI
3. Outerbase — web SaaS with EZQL natural language feature
4. Metabase AI — BI platform with AI question answering (Pro plan)
5. Tableau Einstein Copilot — Salesforce AI on top of Tableau
6. Microsoft Fabric Copilot — AI layer on Microsoft's data platform

We excluded pure API libraries (like SQLAI.ai, AI2sql) that have no product UI, and tools in closed beta as of May 2026.

Master accuracy table

Same 30 questions, same schema, default settings, no fine-tuning:

Source support comparison

User experience comparison

Who can actually use it without training?

iDBQuery: yes — any user who can type a question in English can get a result on day one.

Vanna AI: no — you need to set it up (Python, LLM key, vector store) and train it on your schema before a non-technical user can approach it.

Outerbase: mostly — the web UI is polished and approachable, but the EZQL feature has a learning curve for complex questions.

Metabase AI: mixed — the point-and-click builder is friendly; the AI questions are an improvement but still produce surprising failures on complex queries.

Tableau Einstein: no — Tableau itself requires training; Einstein adds AI but doesn't remove Tableau's learning curve.

Microsoft Fabric Copilot: no — Fabric is an enterprise data platform. Copilot helps power users, not business users.

Time to first answer on a new schema

Dashboard building

Pricing at a glance (May 2026)

The decision framework

Choose iDBQuery if:

• You want the highest accuracy on complex multi-join queries out of the box
• Your data spans multiple sources (DB + Excel + PDFs + ERP)
• You want non-technical users to ask any question on day one
• You want AI-built dashboards from a prompt
• You need vertical connectors for BIM, ERP, or geospatial data
• You want a free tier with real AI features (not a 14-day trial)

Choose Vanna AI if:

• You're a developer who wants full control of the NL-to-SQL loop in Python
• You're embedding NL-to-SQL capability into another product as a library
• You need 100% on-prem, self-hosted, no cloud dependency
• Your team is comfortable operating LLM infrastructure

Choose Outerbase if:

• You value a polished web UI above everything else
• You're already adopted on Outerbase and switching cost is high
• You work exclusively with a single SQL database per workspace

Choose Metabase AI if:

• You already have Metabase deployed and want to add AI to it
• Your analysts need the point-and-click query builder more than NL
• You need Metabase's embedding SDK for customer-facing analytics

Choose Tableau Einstein if:

• Tableau is already your enterprise standard
• You need Tableau's visualisation depth and compliance model
• You have a BI team that can absorb the Tableau learning curve

Choose Microsoft Fabric Copilot if:

• You're fully invested in the Microsoft data ecosystem (Azure, Power BI, Teams)
• You have a data engineering team running Fabric Lakehouses
• You need Microsoft's enterprise compliance posture (SOC 2, ISO 27001 on Azure)

The multi-source verdict

If there is one dimension that most clearly separates these tools in 2026, it's multi-source support. When your data lives in more than one place — and for virtually every real organisation, it does — most of these tools can't help. You get one database at a time, with manual ETL required to bridge sources.

iDBQuery is the only tool in this comparison that natively federates across SQL databases, Excel files, PDFs, BIM models, and ERP systems in a single conversation. That's either not important to you (in which case pick based on other factors) or it's the entire ball game.

FAQ

What does "text-to-SQL" mean? Text-to-SQL (also called NL-to-SQL or natural language to SQL) is the capability to take a question typed in plain English — or any human language — and convert it into a valid SQL query that runs against a database. The result is returned as data, which the tool then renders as a chart, table, or summary.

How accurate is text-to-SQL in 2026? The best tools (including iDBQuery) hit 90–95% first-attempt accuracy on standard business queries against well-structured schemas. Accuracy drops on highly complex multi-join queries, schemas with unclear naming, or questions that require domain-specific knowledge. All tools in this comparison improve accuracy when you provide schema descriptions and example question-SQL pairs.

Do these tools write to my database? All tools in this comparison are read-only by default. iDBQuery has opt-in ERP write-back (Procore RFIs, Aconex transmittals) with a per-action audit log, but SQL databases are strictly read-only.

Can I self-host iDBQuery? iDBQuery is a cloud SaaS product. On-prem / self-hosted deployment is available as a Custom Enterprise option.

Which tool is best for small teams? iDBQuery (generous free tier, no IT setup), Vanna AI (free open-source if you have a Python developer), or Outerbase (polished UI at low per-seat cost). Tableau and Fabric are overkill for small teams.

Is Microsoft Fabric Copilot production-ready in 2026? It's in production and improving rapidly, but it requires the broader Fabric platform investment. The Copilot is most useful for teams already running data in Fabric Lakehouses; standalone text-to-SQL on arbitrary external databases isn't its strong suit.