Best text-to-SQL tools in 2026: the six kinds of chat-with-your-database tool

The six categories, in one place

• Open-source frameworks you run yourself — Vanna AI, Wren AI. You host it, you bring the model key, you own the semantics. Maximum control, maximum work.
• Open-source BI with a natural-language layer — Metabase (Metabot). An assistant inside a BI tool you may already run.
• Warehouse- and platform-native assistants — Snowflake Cortex Analyst, Databricks AI/BI Genie. Natural language on data already inside that platform, governed by that platform, billed on that platform's meter.
• Enterprise BI copilots and analytics agents — Copilot in Power BI, ThoughtSpot Spotter. Natural language over a curated, governed semantic model, sold with the wider BI suite.
• Dedicated chat-with-your-database products — AskYourDatabase, Intellrise. Standalone tools whose whole job is question in, answer out, on databases you connect.
• General AI assistants and file-first analysts — ChatGPT, Claude, Julius AI. Analysis of files you hand them; not built to sit on a live production database.

Every tool at a glance: where it runs, who holds the key, what it costs to start

The two questions above — who supplies the model key, and what you actually get billed for — separate these products faster than any feature list. Here they are side by side. Every figure is the entry point, not the full price list, and several of these carry a platform meter on top of the number shown.

How to judge a text-to-SQL tool: eight criteria that actually matter

Demos all look the same. These are the questions that decide whether a tool survives contact with your real schema.

• Reproducibility — ask the same question next Tuesday and do you get the same number? None of these vendors publish a run-to-run variance figure, so treat this as something to test rather than something to read off a table. What you can check up front is the mechanism: a tool that leaves you a saved query definition attached to a source gives you something to re-run and diff, whereas a tool that leaves you generated code inside a conversation gives you something to go and find. If a number will end up in a board pack, this is the first thing to test.
• What sits between you and your answers — a meter (messages, credits, questions per day, or capacity units) that runs out, or a flat plan with the token bill paid to the model provider. Know which one you are buying before your busiest week.
• Where the semantics live and who maintains them — a hand-written modelling layer (Wren's MDL, a Snowflake semantic view, a Power BI semantic model) is precise and auditable, but somebody has to write and maintain it. A tool that learns definitions from usage is lower effort but you should be able to inspect and correct what it learned. "No semantic layer at all" is the option that quietly produces wrong numbers.
• Does schema understanding persist — does a correction you make today survive into next month's conversation, or do you re-explain "net revenue excludes refunds" every session? This is the difference between building an asset and repeating yourself.
• Connector breadth, and whether it is the right breadth — a warehouse-native assistant covers one platform perfectly. A standalone tool covers many, sometimes shallowly. Count the sources you actually need, including the awkward Google Sheet nobody will migrate.
• Read-only safety — can the tool physically not write? Look for a SELECT-only guard in the product and, independently, connect with a read-only database role. Never rely on the prompt to keep an LLM away from DELETE.
• What you can do with the answer afterwards — a chart in a chat window is where most tools stop. Ask whether you get a persistent dashboard, an editable report, and a file you can email to someone who will never log in. This is often the gap between "impressive" and "used weekly".
• Deployment and data boundary — self-hosted, vendor cloud, or in-warehouse? For regulated or sensitive data, this often decides the shortlist before any feature comparison starts.

Category 1 — Open-source frameworks you run yourself

These are libraries and engines, not finished products. You get source code, full control of the data boundary, and no per-question meter — in exchange for hosting, wiring and maintenance. Pick this category if you are embedding natural-language querying into your own application, or if your data cannot leave your infrastructure.

Vanna AI — the embeddable text-to-SQL library

Vanna is an MIT-licensed Python package for building a SQL agent: you install it, give it context about your database, and it turns questions into SQL, runs them, and returns tables, charts and summaries. Its README advertises deliberately broad compatibility — OpenAI, Anthropic, Ollama, Azure, Google Gemini, AWS Bedrock and Mistral on the model side, and PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, SQLite, Oracle, SQL Server, DuckDB and ClickHouse on the data side.

Because it is a library rather than an application, the result depends heavily on the interface you build around it. On context, note that the current 2.0 docs claim the agent "learns your database schema, business logic, and query patterns without manual training", with Tool Memory saving successful interactions — so the older "you'll have to hand-curate a training set" characterisation of Vanna is out of date. Its pricing page also lists hosted plans (Explorer at $50/month with 20 questions/day, Team at $500/month with 300 questions/day, a 20% annual discount, and custom Enterprise pricing including on-premises deployment), which mainly buys you the admin layer rather than a different engine.

One thing to check before you build on it: as of 29 July 2026 the GitHub repository is marked archived, with its last push in February 2026, while the Vanna Cloud plans above are still being sold. An archived upstream changes the maintenance question from "will they fix this" to "can we fix this ourselves", and that belongs in the decision rather than in a footnote.

Typical setup: engineering teams putting natural-language querying inside their own product, and anyone who needs the whole stack to run in their own infrastructure with their own model key. Not the right pick if you want dashboards, reports and user management out of the box — that is your job here.

Wren AI — open-source GenBI with a modelling layer

Wren AI is a more complete open-source system: an engine that resolves a question against a semantic layer and returns SQL across 20+ data sources including BigQuery, Snowflake, PostgreSQL, ClickHouse, Redshift and Databricks. The repository is multi-licensed by path — Apache 2.0 for the core, SDK and examples, CC BY 4.0 for the docs — and is designed to be self-hosted and LLM-agnostic, with the pricing page listing bring-your-own-LLM on the open-source edition.

Check which Wren you are looking at before you evaluate it, because the project restructured in 2026. The engine merged into the main repository under core/ on 4 May 2026, and the open-source edition is now developer-first: its own pricing page describes it as "Built for developers; no UI." The older chat-first BI application many write-ups still describe is preserved on the legacy/v1 branch as Wren GenBI Classic, with no new features or security fixes. If you want charts and dashboards out of the box rather than an engine to build on, that difference matters more than any feature comparison.

Its distinguishing idea is the Modeling Definition Language, which Wren's own docs call "the semantic contract at the center of Wren AI" — an explicit definition of what Wren's docs list as its core MDL objects: models, columns, relationships, calculated fields, views and cubes. In Wren's framing, raw schemas describe storage and MDL describes meaning. The cost of entry is that somebody has to write that model and maintain it in version control.

The hosted editions are priced publicly: a free cloud tier with 20 credits a month, Essential Cloud at $179/month billed annually with 13,200 annual credits, Enterprise Cloud at $559/month billed annually with 24,000, and a self-hosted Enterprise Plus tier with no published number. Month-to-month those two tiers are $259 and $799 — the pricing page opens on the annual toggle, so the lower pair is what you see first. Overage is $0.10 per credit. The plan cards also advertise "Credits roll over up to 2x", but Wren's own FAQ on the same page limits that to monthly billing — "unused credits in an annual plan do not roll over across billing cycles" — so it does not apply to the annual prices quoted here. Wren figures re-read 3 August 2026.

Typical setup: data teams who want governed, self-hosted natural-language querying and are comfortable maintaining a modelling layer in version control.

Category 2 — Open-source BI with a natural-language layer

If a BI tool is already deployed, its built-in assistant works inside the permissions and charts that tool already has, and inside that tool's limits.

Metabase (Metabot) — an assistant inside the BI tool you may already run

Metabot is Metabase's AI assistant. Per Metabase's documentation it creates charts from natural language, generates and edits SQL in the native editor, fixes query errors, analyses existing visualizations, generates transforms and answers questions from Slack. Metabase's pricing page lists AI SQL generation in the free Open Source column, with some capabilities reserved for paid tiers — admins on Pro and Enterprise can restrict Metabot to verified models and metrics, and letting your own customers ask questions with AI, along with AI usage auditing and controls, sits on Pro.

Metabase's own feature page says you plug in the provider you already use — Anthropic, OpenAI, AWS Bedrock or Microsoft Azure — and on self-hosted deployments you configure your own provider. Its docs advise double-checking generated results.

Typical setup: teams already running Metabase, especially self-hosted.

Category 3 — Warehouse- and platform-native assistants

If all of your data already lives in one platform, that platform's assistant needs no new connector — and it is limited to that platform's data, that platform's models and that platform's meter.

Snowflake Cortex Analyst — natural language on Snowflake data

Cortex Analyst is Snowflake's managed natural-language-to-SQL feature for structured data in Snowflake, exposed as a REST API you embed in Streamlit, Slack, Teams or your own chat interface. Snowflake's documentation says schemas alone lack business context, so semantic models are strongly recommended, with Semantic Views now the preferred approach over the older YAML-on-a-stage method.

It runs on Snowflake-hosted models — the docs name Anthropic Claude Sonnet, OpenAI GPT-4.1, Arctic Text2SQL, and a Mistral Large 2 / Llama 3.1 70b combination — so there is no bring-your-own-key. Billing is credits per processed message, plus the warehouse compute for actually running the generated SQL. And it works on data in Snowflake, full stop.

Typical setup: Snowflake-centric organisations willing to invest in semantic models. If half your data sits outside Snowflake, this answers half your questions.

Databricks AI/BI Genie — natural language on the lakehouse

Genie is Databricks' natural-language experience: one place to ask data questions, explore AI/BI dashboards and run Databricks Apps, with answers grounded in your organisation's data and governed through Unity Catalog. Rather than a hand-written modelling language, you shape accuracy by configuring datasets, sample queries and instructions, and by adding metrics, business rules and verified answers to build a trusted environment.

Commercial terms are in motion and worth checking directly. Databricks' documentation describes Genie Code moving to pay-as-you-go from 8 July 2026 with a per-user monthly free allowance, and Genie One and Genie Agents usage free through 31 July 2026, with budget and cost controls for account admins. Note the date on that second one: it lapses on 31 July 2026, and the docs do not say what replaces it, so read the current pricing page rather than assuming either that it stays free or that it becomes paid.

Typical setup: teams already on Databricks with Unity Catalog governance in place.

Copilot in Power BI — natural language over a curated semantic model

Copilot in Power BI covers both ends: business users chatting with a report or across items they have access to, and report authors generating reports, DAX queries and measure descriptions. When a question relates to data in the semantic model, Copilot answers from that model; otherwise it falls back to the language model's general knowledge — a distinction worth understanding before you trust an answer.

The requirements are the main gate. Microsoft's documentation is explicit that Copilot needs a paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher); a Power BI Pro or Premium Per User licence alone is not sufficient, and trial capacities are unsupported. Consumption shows up as capacity units in the Fabric Capacity Metrics app. Microsoft also states plainly that model owners must prepare their semantic models for AI, warning that without that preparation Copilot can produce generic, inaccurate or misleading output.

Typical setup: organisations already standardised on Power BI or Fabric with well-modelled semantic models and capacity to spare. It is the wrong starting point for a small team with a raw Postgres database and no BI estate.

ThoughtSpot Spotter — the enterprise analytics agent

Spotter is ThoughtSpot's analytics agent, aimed at enterprises that want self-service analytics under governance. Notably, it does not emit raw SQL directly — ThoughtSpot's own wording is that "instead of direct text-to-SQL, Spotter translates questions into search tokens grounded in your governed semantic layer—producing fully traceable, auditable queries." That is a different architecture from most of this list; it appears here because it answers the same question for the buyer. It also reaches beyond answering, triggering actions in systems such as Jira, Salesforce and Slack.

It assumes the enterprise prerequisites: a maintained semantic layer, connected cloud sources, and a governed analytics platform underneath. Published list prices: Essentials starts as low as $25 per user per month billed annually for 5 to 50 users and up to 25M rows, and a usage-based Pro tier starts as low as $0.10 per credit for up to 1,000 users and 250M rows, with Enterprise and Embedded Enterprise quoted rather than listed. Spotter itself is listed as a Pro-tier feature.

ThoughtSpot states that LLM tokens are unlimited and that it does not meter or charge for them, and it supports pointing Spotter at your own LLM provider, in which case that provider's fees apply.

Typical setup: larger organisations that already have — or intend to build — a governed semantic layer and want auditable, action-taking analytics across many business users.

Category 4 — Dedicated chat-with-your-database products

These are standalone products whose entire purpose is question in, answer out, on databases you connect. They do not assume you own a warehouse platform or run a BI estate, which makes them the practical middle ground for small teams, consultants and individual analysts.

AskYourDatabase — desktop app and embeddable database chatbot

AskYourDatabase positions itself as a SQL chatbot for chatting with a database. Its connect documentation lists MySQL, PostgreSQL, SQL Server, BigQuery, Snowflake and Vertica, and documents MongoDB and ClickHouse as well. Its home page also shows Oracle, though that documentation page does not mention it, so confirm Oracle directly if it is your deciding factor. It ships in two shapes: a desktop application that keeps data local to the device, and a website chatbot you can embed for customers, with on-premise deployment offered to enterprise customers.

Pricing is published and reflects those two shapes. The desktop Ultimate plan is $49 per seat per month, billed monthly (read 26 September 2026). It lists unlimited access to specific hosted models. The embeddable chatbot starts at $149/month for 1,000 questions and up to 6 chatbots, $329/month for 1,500 questions with unlimited chatbots and custom branding, and custom Enterprise pricing with on-premise hosting. The pricing page lists neither a free tier nor a bring-your-own-key option, so plan on the vendor's models and the vendor's meter — though the site does mention a free quota on a small model elsewhere.

Typical setup: embedding a data chatbot for your own customers rather than running internal analysis.

Intellrise — from the question to the chart and the report, with a semantic layer that learns your schema

Intellrise is a standalone AI data analyst in the same category as the entry above, with two things it leads on. First, one question goes all the way to a deliverable: a query, a chart, and then a persistent dashboard or an editable report you can export to PDF, DOCX, PPTX, Excel or CSV — rather than a chart that lives only in a chat window. Second, the semantic layer annotates your schema and keeps the corrections you make: the AI describes your schema on connect, learns column and table meanings from your conversations and keeps them across sessions and schema refreshes, with a dbt mode and a manual editor. That is the same accuracy problem Wren solves with a hand-written modelling language and Snowflake solves with semantic views, approached from the other end — lower effort to start, and you inspect and correct rather than author from scratch.

Mechanics: six sources (PostgreSQL, MySQL, Redshift, Google Sheets, CSV, Excel) that one question can combine — your Postgres orders table joined to the Google Sheet of targets nobody will ever migrate — instead of each source only being able to see its own tables; a readable query plan in business terms before complex queries run, with the SQL one click away; a read-only guard that refuses write statements such as DROP, DELETE, UPDATE and INSERT; credentials and keys encrypted with AES-256-GCM; models from Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint, with no credit meter — the token bill goes to that provider. Pricing is Free $0 (one database source plus Google Sheets, three dashboards, unlimited reports and exports), Pro $29/month and Team $100/month for five seats. Pro is about $24/month billed annually. Every new account gets a 14-day Pro trial with no card, and there is a public demo at intellrise.com/demo showing the dataset, the SQL and the resulting charts without any signup.

Best for: analysts, analytics engineers, dbt users, data consultants and technical founders who need a saved query definition they can re-run and diff on live databases, a flat price with no credit meter, and an exportable artifact at the end — without standing up a warehouse platform or maintaining a modelling layer by hand.

Text-to-SQL tools for a team: one semantic layer for five people, $100 not $450

Intellrise Team gives five people one shared semantic layer and a shared workspace for $100 a month. Julius includes its semantic layer and team collaboration only on Business, at $450 a month ($375 billed yearly), so on monthly billing five people pay $350 a month less with us.

On a team of two to fifteen, the costly mistake is not a slow query. It is two people asking the same question and getting two revenue numbers, because one counted gmv with tax and the other without. A shared semantic layer settles that once.

One person connects the database. When whoever connected it fixes a column definition, it is saved with that database and every teammate's next question uses it. Members work on the owner's AI key once the owner has added one, and each seat after five is $20 a month, so a team of eight pays $160. The full cost for teams is in Julius AI alternative for teams.

Category 5 — General AI assistants and file-first analysts

These are the tools most people reach for first. The line that matters is where they stop: a live database you query every week.

ChatGPT and Claude — general assistants, with directory connectors to some databases

A general assistant analyses a file you upload: type the question, get a chart and an explanation, and follow up with questions that mix analysis, writing and code.

Both can now reach some databases through their directories. Checked 15 August 2026: ChatGPT's own app directory at chatgpt.com/plugins lists Neon Postgres, Supabase and BigQuery — Snowflake, MySQL and Databricks were not in it on that date. Claude's connector directory lists Snowflake and a Supabase connector that describes executing SQL against PostgreSQL. Check the directory for the assistant you use.

What has not changed is the sandbox and what persists. OpenAI's help documentation states plainly that "the Python environment used for data analysis cannot make external web requests or API calls" (checked 29 July 2026), so analysis still runs on what you upload or what a connector hands over. ChatGPT's memory is about you, not a model of your schema, and what persists from an analysis is generated code in a chat rather than a query definition attached to a source.

Typical setup: one-off analysis of files you already have. The ChatGPT for data analysis page linked below goes further into this comparison.

Julius AI — file- and spreadsheet-first analysis

Julius is an AI data analyst built around uploading a file. Like the general assistants, it answers by generating and running code; it meters usage in credits on the platform's own models rather than a key you supply, and database connectivity sits on paid plans.

Typical setup: one-off analysis of a spreadsheet or CSV where re-running the same query next week is not the point. The Julius AI comparison page linked below covers it in full.

How to choose: start from where your data lives

• Your data is in a live Postgres, MySQL or Redshift database, you ask the same questions every week, and the answer has to reach a stakeholder → a dedicated chat-with-your-database product. This is the category Intellrise is in: connect a read-only source, get a reviewable query plan, keep a semantic layer that learns, and export the result. Free to start; Pro is $29 a month.

What none of these tools do for you

Three things apply across the entire list, whichever tool you pick.

• Semantics are still your responsibility. Whether you write a modelling language, curate a semantic view, or review what a self-learning layer inferred, somebody has to confirm that "revenue" means what the business thinks it means. Every accurate deployment in this space has a human who did that.
• Nothing here removes the need to verify. Metabase says it in its own docs; Microsoft warns that unprepared models produce misleading output. Test with a set of questions whose correct answers you already know, and re-test after a schema change.
• Read-only is on you at the database level. A product-side SQL guard is necessary and not sufficient. Create a dedicated SELECT-only role for any tool you connect, no matter what its marketing says.