Wren AI alternative: a finished AI data analyst for $29 a month, no credits to buy

Which Wren are you comparing? The two editions at a glance

This is the distinction that decides whether anything else here applies to you. Both are Wren AI, both are current, and they answer to different people.

• Wren AI Open Source — an engine, not an app. Installed with pip install wrenai, Apache 2.0, driven through a CLI (wren query, wren ask) and an MCP server, with an MDL semantic layer you keep as YAML in a project directory and version in git. Its card reads "a CLI and Rust context engine that give your own agents governed, hallucination-free access to data. Built for developers; no UI." Free, and free of any credit meter, because it runs on your machine and your model.
• Wren AI Cloud — the hosted product with the web interface, dashboards, spreadsheets and the Modeling AI Assistant. Free, Essential Cloud and Enterprise Cloud are managed on Wren's infrastructure; the pricing FAQ states "All organizations are hosted in GCP's US-EAST-4 region by default". Billed in credits.
• Enterprise Plus — self-hosted, and priced unlike everything above: "Licensed by concurrent sessions, not monthly credits", starting at 10 concurrent sessions, with the note that "1 concurrent session typically supports around 5 active users." Its deployment row reads "Self-hosted; incl. on-prem & air-gapped". Price is Contact Us.
• A trap worth naming, because most third-party tutorials still describe it: the old Docker-based chat app with a web UI is no longer what you install. The README states it "is preserved on the legacy/v1 branch (tag v1-final) and is now Wren GenBI Classic" — and, separately, that the branch gets "no new features or security fixes". If you follow a 2025 walkthrough and docker-compose up, you are standing up code its own maintainers have said they will not patch.
• One place Wren's own pages do not line up: the open-source pricing card says "no UI", and the open-source-versus-commercial table in the documentation marks "Web UI for non-technical users" with a cross for open source, which agrees. The row immediately below marks "GenBI dashboards" with a tick for open source, which does not. We could not tell from the published material what a dashboard with no UI looks like.

What each one asks of your database — the section that decides it for some readers

Every hosted analytics tool has to reach your data somehow, and the useful way to compare is to put the requirement each vendor publishes side by side rather than to discuss security in the abstract. We would read this first, because it can rule an option out before price ever comes up.

What that table does and does not settle

On inbound connections, Wren AI Cloud and Intellrise work the same way: a database behind a firewall needs an allowlist rule for either.

Two things your DBA will ask. The ranges to allowlist: as of 15 August 2026 our servers connect from 74.220.52.0/24 and 74.220.60.0/24, and our database requirements page carries the current reading. And TLS: our connection form builds the Postgres URL with sslmode=require, so a Postgres source is always connected over TLS.

On privileges: Wren AI Cloud asks for CREATE TEMPORARY VIEW and DROP VIEW on top of SELECT, for the temporary views it uses in analytics and modelling, so its grant cannot be SELECT-only. Intellrise asks for SELECT and nothing else — the grant a database owner working from a read-only policy can sign off without an exception.

One option removes the question instead of answering it. If your data is in Google Sheets, it connects to Intellrise on the free plan with no inbound rule to write, because there is no port; a CSV or Excel file does the same on the 14-day Pro trial. That is the fastest way to evaluate without booking time with whoever owns your firewall.

The semantic layer: hand-authored, generated, or corrected

Both products have a modelling layer and both will generate a first pass, so the difference is not that one learns and one does not. It is the form the knowledge takes, and who is expected to maintain it.

Wren's open-source layer is MDL. Its modelling guide says the agent "introspects schema, normalizes types, detects relationships, and writes one model per table", then adds "The first pass is rough but functional" and "The hard meaning lives outside the database. Scaffolding cannot tell you" which of three similarly named tables is canonical, or why a status code means refunded.

On the Cloud side, the Modeling AI Assistant "automates the creation of semantics and relationships" and generates both — with a step worth noticing: the docs instruct you to click Save "to retain the generated semantics" and warn "Important: Deploy your changes to activate the semantics." Generating and activating are separate deliberate actions.

Intellrise accumulates something different in shape. The AI auto-annotates your schema when you connect a source, and when you clarify a field in ordinary conversation — "net revenue excludes refunds" — it offers to save that as a named definition bound to that column, on an Accept or Reject card. Accepted definitions persist across conversations, are put in front of the model on every future question, and can be read and rewritten by hand. There is also a dbt mode that recognises fact_, dim_ and stg_ conventions. It is a layer you correct once: the meaning you fix is the one every later question uses, and nobody writes YAML to get there.

One maintenance difference is worth planning for. Wren Cloud documents what happens when your schema changes: you click a Refresh icon, it compares the current schema against your models, and "Wren AI automatically removes affected models, relationships, and calculated fields where automatic resolution is supported" — but for a changed column type, "Wren AI doesn't provide an automatic resolution. You need to manually reselect the changed column" and redefine relationships and calculated fields. Whether definitions survive being pointed at a different data source altogether, Wren does not say on any page we read.

Where Intellrise wins

Start from the thing the open-source edition does not have, because for a good number of readers it is the whole argument: it ships with no UI, the Docker app that used to provide one now sits on a branch its own maintainers say gets no new features or security fixes, and the edition that does have an interface — Cloud — is the one billed in credits. If you are on the open-source side and you eventually want something your colleagues can open, Wren's own supported road out of the terminal ends at the meter. That is the fork this page is really about, and it is where the rest of this section applies.

It is built for the analyst, analytics engineer or data consultant who wants the capability finished, with no credit meter and nothing to operate.

Pro is a flat $29 a month, or about $24 a month billed annually. Model tokens are billed by your AI provider at its published per-token rate, on an invoice you can already read, so the total is $29 plus that model spend, against Wren's plan plus its credits. Wren's credit cost moves with the answer mode, which of six models, which side of a 272K context break your schema lands on, and how much of your input was cached; Intellrise adds no dial of its own on top of the provider's rate.

You also get the finished parts: six connectors with cross-source joins over a DuckDB federation layer, a readable query plan you can inspect before executing anything the model judges non-trivial — multi-table, cross-source or multi-step — dashboards you can drag, resize and pin to from chat, editable reports exported to PDF, DOCX, PPTX, Excel and CSV, read-only share links, and email delivery on Pro. The free tier stays free after the trial: one database source plus unlimited Google Sheets, three dashboards, unlimited reports and exports.

How to actually decide, in an afternoon

Three questions, in order.

First: what credential will your database owner sign off? If it is read-only, note that Wren Cloud's documented requirement includes CREATE TEMPORARY VIEW and DROP VIEW and ours does not, and take that to whoever owns the database before you evaluate features. If your data is in Sheets or files, skip the question entirely — no port is needed.

Second: do you want to operate a semantic layer, or use one? A YAML project in git is a job somebody has to own. If nobody on your team wants that job, the answer is a layer the AI builds and you correct.

Third: run the same three real questions through both. Wren's free tier gives you 20 credits a month plus 80 for the first 14 days, no card; ours gives you 14 days of Pro with no card, and a public demo at intellrise.com/demo that needs no signup and no AI key at all, so you can read a query plan and the charts it produces before connecting anything. Three questions you already know the right answer to will tell you more than any comparison table.