Vanna AI alternative: $29 a month, not $50 for 20 questions a day

Vanna AI alternative at a glance: Vanna vs Intellrise

• 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: Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint, switched any time from the model picker in chat.
• Databases — Vanna: PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, SQLite, Oracle, SQL Server, DuckDB and ClickHouse, per its README. Intellrise: six sources (PostgreSQL, MySQL, Redshift, 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, which you then build your own app around. Intellrise: a finished analyst UI with persistent dashboards, editable reports and read-only share links on Pro.
• 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 email delivery on Pro, sent now or a report put on a daily or weekly schedule.
• 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 — your AI provider bills the tokens directly.
• Who it is for — Intellrise: small data teams, analysts, analytics engineers, dbt users and data consultants who want the capability finished rather than built. Pro is $29 a month.

Vanna AI is archived — what that actually means, and what to do next

What is true: github.com/vanna-ai/vanna is a public archive. The banner reads "This repository was archived by the owner on Mar 29, 2026. It is now read-only." The GitHub API confirms archived: true, with the last code push on 2 February 2026 and the last release, v2.0.2, published the same day — a changelog consisting of one ChromaDB fix. There were 289 open issues and pull requests when we checked — 227 of them issues — and a read-only repository can neither accept new ones nor merge a pull request. Nothing in the vanna-ai organisation's other 23 repositories has been pushed since 14 February 2026, and none of them is described as a successor to Vanna. Of the ten most-starred of the 2,496 forks, the top one has 4 stars, so no community fork has visibly taken over either. Fork and star counts re-read first-hand on 2 September 2026.

The hosted product is still sold, and it is priced by the question: vanna.ai/pricing, read 26 September 2026, lists Explorer at $50 a month for 20 questions a day and Team at $500 a month for 300 questions a day, rate-limited until the next reset once you go over.

• Where an archive actually bites is not the part you would guess, and it splits in two. The code half is the easy half: the text-to-SQL logic is pinned MIT source, and it will keep doing exactly what it did on 2 February 2026. What breaks around it is the perimeter — a database driver that publishes a new major version, a model provider changing its SDK, a Python release your pinned dependency set will not build against, or a CVE in something transitive. Every one of those announces itself with a stack trace. Under active maintenance they are somebody else's afternoon. Now they are yours.
• The other half does not announce itself, and it is the half a pinned commit cannot protect. Vanna 2.0 is a RAG-and-LLM agent, so an answer is not produced by the frozen code alone — it comes back from a model endpoint you configure, and that endpoint moves on the provider's release schedule rather than on yours. OpenAI's deprecations page, read 31 August 2026, says it regularly retires older models and that software relying on them “may need occasional updates to keep working”; Anthropic's model-deprecations page, read the same day, says deprecated models stay functional but “are likely to be less reliable than active models”. A retired model is loud — the call fails and you find out immediately. A model that has merely got worse at your schema is silent: you get a different query and a confident number, and nothing errors. Under maintenance, somebody re-tunes prompts and retrieval when a model moves. On an archive, nobody does, and nothing tells you it happened.
• The way out: Intellrise is a finished product, so nobody on your team operates a service. You keep a choice of Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint, and Pro is $29 a month with no daily question limit.

1. No question meter, and your choice of model

Vanna Cloud prices by the question: Explorer is $50 a month for 20 questions a day and Team $500 a month for 300, and requests are rate-limited until the next reset once you go over. Intellrise Pro is $29 a month with no daily question limit, and the free tier has no platform question cap either, so a heavy analysis week does not become a billing event.

You still choose the model. Intellrise works with Gemini, OpenAI, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible endpoint; you switch the model any time from the model picker in chat and, on Pro, tune context window, output tokens, temperature and reasoning level per key, and the token bill goes straight to that provider.

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

Vanna's 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.

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, are put in front of the model on every future question, 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.

Tool Memory reuses successful past interactions on similar questions. An Intellrise definition is a named statement bound to a table or column that you can open, read and rewrite, so a contested term gets settled once instead of re-derived each time. 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 refuses write statements such as DROP, DELETE, UPDATE and INSERT. The plan appears before multi-table, cross-source and multi-step questions run. A chart you pin re-runs its stored SQL rather than asking the model again, so you have the query in front of you, 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 <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 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.

In Intellrise one conversation goes from question to deliverable. The same conversation produces inline charts for exploratory questions, Metabase-style dashboards you can pin to from chat and — on Pro — drag, resize and recolour, 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 email delivery are available on Pro — sent now, or a report put on a daily or weekly schedule. It is the difference between having the engine and having the car, and for a small data team the question is whether building the rest of the car is the best use of the next month.

The build-versus-buy question

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 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. What it changes is the shape of the maintenance question: it moves from "will they fix it" to "can we fix it ourselves" — and the engineer-days behind that second answer are what build versus buy is actually costing you.

Intellrise takes the other side of that trade on purpose. There is nothing to host, nothing to train and no UI to build, and no per-question meter on any tier, including the free one.

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 six connectors, no per-question meter, and reports they can actually hand to a stakeholder. If your data lives in a warehouse, a recurring question needs to become a saved query you can re-run and diff rather than code inside a chat, 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 fastest 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 that needs no signup and no AI key — a synthetic sample dataset, the queries verbatim, and the charts they return — 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.