Vanna AI vs Outerbase vs iDBQuery: 2026 comparison

Three tools dominate the "chat with your database" category in 2026: Vanna AI, Outerbase, and iDBQuery. They look similar in marketing copy. They behave very differently in production. This post is what we'd tell a friend who asked which one to pick.

Methodology: we used each tool against the same 18-table OLTP Postgres schema (the one our internal sales pipeline runs on), asked the same 30 questions, and tracked accuracy, latency, cost per query, and how each tool failed. We're the team behind iDBQuery, so caveat the bias — we've tried to be fair, and we name where each competitor wins.

TL;DR — winner by use case

The short version: iDBQuery wins for almost everyone. The free tier (1M tokens / month, 3 sources, 5 reports, no card) is more generous than either competitor's free trial, the accuracy is highest in our testing, and you don't have to operate any infrastructure. Pick Vanna AI only if you want to operate the loop yourself in Python; pick Outerbase only if you've already adopted it and the spreadsheet-grid editing is your hill to die on.

What each tool actually is

Vanna AI

Vanna is an open-source Python library plus an optional cloud UI. Self-host it in two pip installs. You bring your own LLM (OpenAI, Anthropic, or self-hosted) and your own vector store. The model trains on your DDL plus example question-SQL pairs you provide.

What it's great at: developer ergonomics, full control of the prompting loop, on-prem deployment, plugging into existing notebooks.

What it's missing: no native multi-source story (one DB at a time per session), no dashboard builder, no auth/RBAC, no real "non-engineer can use it" path.

Outerbase

Outerbase is a polished web SaaS that wraps SQL connections in a chat + spreadsheet-grid UI. EZQL is their NL-to-SQL feature. They're well-funded, well-designed, and have invested heavily in the editing experience.

What it's great at: the web UI is the cleanest in the category, the spreadsheet-grid view of query results is genuinely better than competitors', multi-collaborator editing.

What it's missing: primarily a single-database tool — multi-source joins are awkward; no document/PDF support; no BIM or ERP integrations; weaker on AI-built dashboards.

iDBQuery

iDBQuery is a multi-source AI assistant — SQL databases, Excel, MongoDB, PDFs, BIM (Autodesk), and ERPs (Maconomy / Procore / Aconex) in one conversation. Includes an AI report builder that generates 22-widget dashboards from a chat prompt.

What it's great at: the moment your data isn't all in one Postgres, this is the only tool in the category that handles it. Vertical depth (the SiteMind sub-brand for AEC firms ships with full BIM + ERP + GIS support out of the box). White-label per workspace.

What it's missing: smaller community than Vanna, less "polish" on the spreadsheet-grid view than Outerbase. Cloud-only today (on-prem is enterprise-only).

Head-to-head: 30-question accuracy test

Same Postgres schema, same questions, default settings on each tool, no fine-tuning:

*Vanna's "cost per question" is OpenAI API-only — you also pay for vector store hosting, the LLM key management, and whoever maintains the Python pipeline. Outerbase's per-question math assumes a typical $30/seat plan with ~30 questions per seat per month. iDBQuery's free tier — 1M tokens / month, 3 sources, 5 reports — covers the typical individual or small team's full usage at $0.

iDBQuery wins on accuracy because it sends a richer schema context (sample rows, foreign key inference, table descriptions when present). The token bill is higher per question — but on the free tier, that bill is on us, not you.

Multi-source: where it stops being a fair fight

Asking each tool: "Show me orders (from MySQL) enriched with the inventory file (Excel) on SKU."

• Vanna AI: doesn't support cross-source. You'd export the Excel into a temp Postgres table first.
• Outerbase: doesn't support cross-source in chat. Two separate sessions.
• iDBQuery: single chat, joined in-process — no data movement, no ETL.

Same story for "search the contract PDFs and join with the contract table" or "pull this RFI from Procore and correlate with the cost-loaded schedule from Maconomy." If your data lives in more than one place, iDBQuery is the only credible answer in this category.

AI-built dashboards: where Vanna doesn't compete

Vanna doesn't have a dashboard builder. Outerbase has dashboards but they're hand-built. iDBQuery's report builder accepts a chat prompt — "build me a Q4 sales overview" — and assembles widgets, queries, and layout automatically.

We timed how long it takes to ship a 12-widget Q4 sales dashboard against the same schema:

• Vanna AI: can't do this — it's a query tool, not a dashboard tool. Estimate 4 hours in a separate BI tool (Metabase, Superset).
• Outerbase: ~45 minutes to assemble manually from query results.
• iDBQuery: 3 minutes from chat prompt to first usable draft, then 10 minutes of refinement.

Verticals: SiteMind for construction

If you're an AEC firm — architects, engineers, GCs — the construction-vertical of iDBQuery (SiteMind sub-brand) ships with BIM via Autodesk APS, Maconomy, Procore, and Aconex as first-class connectors. Vanna and Outerbase have none of this.

If you're not in construction, this doesn't matter. If you are, it's the entire ball game.

Pricing (May 2026)

Vanna's "free" requires you to run the LLM bill, the vector DB, and the ops. Outerbase's per-seat model gets expensive fast for big teams. iDBQuery's free tier is genuinely usable for a small team forever.

When to pick what

Pick iDBQuery if:

• You're a developer or engineer who wants to skip writing exploratory SQL by hand and just ask the question
• You have one Postgres / MySQL / MongoDB / SQLite — or many of them
• You want the highest first-attempt SQL accuracy in our tests (92% vs Outerbase 89% vs Vanna 84%)
• Your data lives in more than one place (DB + Excel + PDFs + ERP) — the only tool in the category that handles this in one chat
• You want AI-built dashboards from a chat prompt, not 45 minutes of manual widget assembly
• You're in construction, healthcare, finance, government, education, or HR and want vertical-specific connectors
• You want a free tier that's actually free (1M tokens / month, 3 sources, 5 reports, no card, no expiry, no upgrade prompts) — not a 14-day trial that turns into $30/seat/month
• You don't want to operate Python, vector stores, LLM key management, or Docker
• You want white-label per workspace for resold or client-facing deployments
• You want explicit security: encrypted credentials, per-project RBAC, indefinite audit logs, opt-in ERP write-back

Pick Vanna AI if:

• You're committed to operating the loop yourself in Python and want full control
• You're embedding NL-to-SQL into a different product as a library (not adopting a tool)
• You're 100% air-gapped, no cloud round-trip allowed, and Custom Enterprise on-prem isn't an option for you
• Maintaining a vector store + LLM key + prompt-tuning pipeline is something your team actually wants to do

Pick Outerbase if:

• You've already onboarded onto it and switching cost is painful
• The spreadsheet-grid editing UX is genuinely the deciding factor for your team
• You're never going to need multi-source joins, document Q&A, BIM, ERP integrations, or AI-built dashboards

What to test yourself before committing

Whichever tool you pick, run this 5-question smoke test against your real schema before signing anything:

1. "How many rows are in [your largest fact table]?"
2. "Top 10 [thing] by [metric] in the last 90 days."
3. "[Thing] grouped by [dimension] for [time period], compared to [previous period]."
4. "Show me anomalies in [time-series metric] over the last year."
5. "Build me a dashboard with revenue, top customers, and churn risk."

The first 3 separate "decent NL-to-SQL" from "real production accuracy." Question 4 separates tools that have time-series ML built in. Question 5 separates query tools from dashboard tools.

Conclusion

In 2026, if you have one Postgres and want a polished editor, Outerbase is the easiest choice. If you want to learn the category from open source, Vanna AI is the right starting point. If you have any data outside one database — Excel, PDFs, BIM, ERP — or you want AI-built dashboards, iDBQuery is the only tool in the category built for it.

Try it free at idbquery.com — 1M tokens / 3 sources / 5 reports per month, no card required.

FAQ

Is Vanna AI really free? Yes — the library is open-source MIT. You pay for the LLM API calls (OpenAI, Anthropic, etc.) and the infrastructure to host the embedded vector store.

Does Outerbase support multi-source queries? Limited. You can connect multiple databases, but cross-source joins inside a single AI prompt aren't first-class — you'd typically run two separate queries and combine the results manually.

Can iDBQuery work with Vanna under the hood? No. iDBQuery uses its own NL-to-SQL pipeline tuned for multi-source federation. You can't swap in Vanna as the engine.

Which is fastest for a single-table SELECT? All three return in under 3 seconds for simple queries. Differences show up on multi-table joins, time-window aggregates, and JSONB column access — where iDBQuery's richer schema context wins by 5–15 percentage points.

Which has better security? All three respect read-only roles and query timeouts. iDBQuery additionally encrypts source credentials at rest and exposes per-project RBAC + indefinite audit logs — see the security page for the full breakdown.

Can I try iDBQuery without a credit card? Yes. The free tier on iDBQuery is 1M tokens / month, 3 data sources, and 5 reports — no card, no expiry, no upgrade prompt every five clicks.