Vanna AI alternative: open-source text-to-SQL vs a finished product

Vanna AI vs Intellrise at a glance

• What it is — Vanna: an open-source (MIT) Python framework you install with pip and embed in your own application; the repo describes it as "Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval". Intellrise: a hosted product you log into and connect a data source to.
• Who operates it — Vanna: you. You host the agent, wire it into FastAPI or Flask, and run it in your own infrastructure. Intellrise: we do; you connect a read-only user and start asking questions.
• How it learns your data — Vanna: as of the current 2.0 docs, automatically. Vanna states the agent "learns your database schema, business logic, and query patterns without manual training", and documents Tool Memory, where "every successful interaction is saved" and reused on similar questions. The older `vn.train()` workflow with DDL and question–SQL pairs is presented in Vanna's migration guide as the 0.x way, replaced by system prompts, context enrichers and RAG tools. Intellrise: the AI auto-annotates your schema and learns column and table meanings from ordinary chat, and saves each one as a definition you can read, edit or override, with a dbt mode.
• Model choice — Vanna: bring your own; documented support includes OpenAI, Anthropic, Ollama, Azure, Google Gemini, AWS Bedrock and Mistral. Intellrise: bring your own key too — Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint. Neither locks you to a house model.
• Databases — Vanna: PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, SQLite, Oracle, SQL Server, DuckDB and ClickHouse, per its README. Intellrise: 10 sources (PostgreSQL, MySQL, SQL Server, Redshift, BigQuery, Snowflake, Databricks, Google Sheets, CSV, Excel) plus cross-source JOIN and UNION through a DuckDB federation layer.
• Interface — Vanna: a pre-built <vanna-chat> web component that works with React, Vue or plain HTML, streaming SQL, tables, Plotly charts and summaries — a strong starting point you then shape into your own app. Intellrise: a finished analyst UI with persistent dashboards, editable reports and read-only share links.
• Deliverables — Vanna: the answer arrives in your chat surface, and anything beyond that you build. Intellrise: editable reports and artifacts exported to PDF, DOCX, PPTX, Excel and CSV, plus one-off email delivery on Pro.
• Cost — Vanna: the framework is free and MIT-licensed; Vanna's pricing page lists Explorer at $50/mo with 20 questions/day, Team at $500/mo with 300 questions/day, and Enterprise custom with unlimited questions, plus a stated 20% annual discount. Daily limits reset every 24 hours and requests are rate-limited until the next reset. Intellrise: Free $0, Pro $29/mo (about $24/mo annual), no per-question meter — you pay your AI provider directly on your own key.
• Best for — Vanna: teams who want to embed text-to-SQL inside their own product, keep everything in their own infrastructure, and have the engineering time to own it. Intellrise: analysts, analytics engineers, dbt users and data consultants who want the capability finished, without giving up model or data control.

1. Bring-your-own-key: keeping what open source already gave you

The usual trade when you move from an open-source library to a hosted product is that you hand over the model. The vendor picks the provider, meters your usage, and your data now flows through their key. For someone who deliberately ran Vanna with their own OpenAI, Anthropic, Ollama or Bedrock credentials, that is a real downgrade, and it is the single most common reason technical teams stay on the library even when maintaining it costs them more than it is worth.

Intellrise does not ask for that trade. It is bring-your-own-key on every tier, including the free one: you connect your own Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint, choose the model per conversation, tune context window, output tokens, temperature and reasoning level per key, and pay your provider directly for tokens. There is no per-question meter between you and your model, so a heavy analysis week does not become a billing event. Your business data is never stored or used to train a model, and connection details and keys are encrypted at rest with AES-256-GCM. If you are self-hosting mainly to keep control of cost and data rather than because you want to maintain a service, this is the part worth checking first.

2. Two layers that both learn — one of them is editable

This is the section where an alternative page usually cheats, so here is the correction first. Vanna used to require curation: the 0.x workflow was `vn.train()` with DDL, documentation and question–SQL pairs. That is no longer how Vanna describes itself. The current docs say the agent "learns your database schema, business logic, and query patterns without manual training", and Tool Memory saves every successful interaction and reuses it on similar questions. Vanna's migration guide lists the old training call as the 0.x approach, replaced by system prompts, context enrichers and RAG tools. If you read an older comparison claiming you must hand-build a training set for Vanna, it is out of date — and so was an earlier version of this page.

So the difference is not learning versus not learning. It is what the learned thing looks like and whether you can correct it. Tool Memory, as documented, accumulates successful question-and-tool examples that get retrieved for similar questions. Intellrise accumulates something different in shape: named definitions bound to specific tables and columns. The AI auto-annotates them when you connect a source, and when you clarify a field in ordinary conversation — "net revenue excludes refunds" — it proposes saving that as an Accept or Reject card. Accepted definitions persist, apply to every future query, and can be read and rewritten by hand. You can point it at a dbt target schema instead, where it recognises fact_, dim_ and stg_ conventions.

Which of those you want is a genuine preference rather than a scoreboard. Retrieval over known-good examples is strong exactly where you have a body of past questions; a written definition is strong when a term is contested and someone needs to see what the tool believes and change it. If your problem is "three people define active customer differently", the editable definition is the thing that settles it.

The other half of trust is being able to see the working. For anything non-trivial Intellrise shows a readable query plan first — sources, fields, joins, filters, groupings, aggregation, in business terms with the raw SQL one click away — and you Execute or Adjust. A read-only guard blocks anything that is not a SELECT. Same question, same answer, and an audit trail you can hand to whoever asks where the number came from.

3. From question to deliverable, without assembling the pieces

Vanna ships a genuinely useful <vanna-chat> component that streams SQL, a data table, a Plotly chart and a summary, and drops into React, Vue or plain HTML. It is a strong starting point. But it is a starting point: the persistent dashboard, the saved report your finance lead can edit, the PDF that goes into the board pack, the share link for someone without a login — those are things you design and build on top, and they are usually where the sprint estimate quietly triples.

Intellrise is end-to-end by construction. The same conversation produces inline charts for exploratory questions, Metabase-style dashboards you can drag, resize, recolour and pin to from chat, and report artifacts by type — a data grid for Excel and CSV, a 16:9 deck for PPTX, a document for PDF and DOCX — rendered in an in-chat side panel, kept as a draft until you save it, then editable in place. Read-only share links and one-off email delivery are available on Pro. It is the difference between having the engine and having the car, and for a small data team the honest question is whether building the rest of the car is the best use of the next month.

When Vanna is the better choice

Plenty of the time, it is. If you are embedding natural-language querying inside your own product, Vanna is the right shape and Intellrise is not — a hosted analyst app is not a component you can ship to your customers. If your security posture requires that nothing, including metadata, leaves your own infrastructure, self-hosting is the answer and no SaaS will match it. If you need per-user row-level filtering enforced inside the agent, Vanna 2.0 describes itself as "a complete rewrite focused on user-aware agents and production deployments", and separately documents queries being "automatically filtered per user permissions" — it addresses that directly, and Intellrise does not.

It is also better whenever control is the point. You can read every line, fork it, swap the retrieval layer, run a fully local model through Ollama, connect a database nobody else supports, and change the prompt when you disagree with it. MIT licensing means none of that is anyone's decision but yours. And if you already have curated question–SQL pairs, that investment is real accuracy that a general product has to earn from scratch. Choosing open source is a reasonable, often correct engineering position, and having the engineering time to back it is a legitimate advantage — not something to apologise for.

The build-versus-buy question, honestly

The cost of the library is not the cost of the system. Running Vanna in production means hosting the agent, standing up and backing up the store its knowledge lives in, owning the retrieval quality, building the interface, and then maintaining all of it while the model landscape moves underneath you. Teams who want exactly that get a lot in return. Teams who wanted a working analyst and ended up with an internal platform to maintain usually feel it about three months in.

One factual point worth knowing if maintenance matters to your decision: the main vanna-ai/vanna repository on GitHub is marked as a public archive. Its own banner reads "This repository was archived by the owner on Mar 29, 2026. It is now read-only", and the last push recorded by the GitHub API is 2 February 2026. The code remains MIT-licensed, freely usable and forkable, the commercial product and enterprise support are actively offered, and a large body of production deployments and community integrations exists — so this is not a reason to avoid it. What it changes is the shape of the maintenance question: it moves from "will they fix it" to "can we fix it ourselves", which for a team that chose open source on purpose may be an acceptable answer rather than a problem.

Intellrise takes the other side of that trade on purpose. There is nothing to host, nothing to train and no UI to build, and the two things you would most hate to lose — your model key and your data — stay yours.

Who Intellrise is for

Intellrise fits the same technical audience Vanna attracts, at the point where they would rather use the capability than operate it. Analysts, analytics engineers, dbt users, data consultants and technical founders tend to feel the difference first: a semantic layer that learns their schema without a curated training set, a readable query plan they can audit, cross-source joins over 10 connectors, bring-your-own-key economics, and reports they can actually hand to a stakeholder. If your data lives in a warehouse, the same question needs to return the same answer, and the deliverable matters as much as the answer, that is the case for buying rather than building.

Try it on your own data

The honest way to decide is to run one of your real questions and see what comes back. Every new Intellrise account starts on a 14-day Pro trial with no card, and there is a public demo at intellrise.com/demo — a real dataset, the queries verbatim, and the charts they return — that needs no signup and no AI key, so you can judge the query plan and the output before connecting anything of your own. After the trial the free tier stays free: one database source plus Google Sheets, three dashboards, and unlimited reports and exports. Pro is $29/mo, or about $24/mo billed annually.