OWOX Model Canvas vs DrawSQL: ERD Tools Compared
Polished collaborative ERDs vs a portable, AI-readable data model (OKF export). Both free — which fits your team?

DrawSQL and OWOX Model Canvas are both free, browser-based ways to draw a data model – and both are pleasant to use. The difference is what you're optimizing for.
DrawSQL makes beautiful, shareable diagrams.
OWOX Model Canvas makes a portable, AI-readable model that happens to also render as a diagram.
If you want a picture for a doc, DrawSQL is a fine choice. If you want the model to travel – into git, into another tool, into an LLM, be the source that drives you report – read on and use it, it’s free. For the wider field, see our best free ERD tools roundup; this is the head-to-head.
TL;DR
- Pick DrawSQL if your main goal is a polished, collaborative ERD you'll drop into documentation or share with stakeholders.
- Pick OWOX Model Canvas if you want the same visual model plus an open export (OKF) you can version, hand to an AI, or push into a warehouse – free, open-source, and with no sign-up.
Side by side
Where DrawSQL wins
DrawSQL's strength is presentation. The diagrams look good out of the box, sharing is easy, and for teams that mostly need to communicate a schema – in a design doc, an onboarding wiki, or a stakeholder review – that polish is genuinely valuable.
If your deliverable is a clean picture of the schema and collaboration on that picture, DrawSQL does it well. OWOX Model Canvas isn't trying to out-pretty it.
Where OWOX Model Canvas wins
OWOX Model Canvas adds three things DrawSQL doesn't (as of July 2026):
- AI drafting – describe the model in plain language and let AI draft the tables and joins, then refine visually.
- A portable, AI-readable output – it exports OKF (Google's Open Knowledge Format), plain markdown you can commit to git, review in a pull request, or feed to an LLM as context so text-to-SQL stops guessing your schema.
- Open and free – Apache-2.0, no sign-up, opens Google's official GA4, Stack Overflow, and Bitcoin samples, and can push a model into governed OWOX Data Marts.

The canvas with tables, keys, and join paths – the same model exports to OKF (portable markdown) or an image.
The real difference: a picture vs a model
Both tools draw the same kind of ERD. The distinction is the artifact you walk away with. DrawSQL's output is a diagram – for humans.
OWOX Model Canvas's output is a model – an open spec a machine can read.
That matters more in 2026 than it did a year ago: AI assistants now need a trustworthy, machine-readable description of your data, and a PNG can't be one. An OKF file can. If your diagram only needs to be looked at, DrawSQL is enough; if it needs to do work downstream, the portable model wins.
Which should you choose?
- You need a beautiful, collaborative diagram for a doc → DrawSQL.
- You want a model that's portable, versionable, and AI-readable → OWOX Model Canvas.
- You don't want to sign up or pay → OWOX Model Canvas (free, anonymous, open-source).
New to modeling? Start with our guide to data modeling or grab a template; prefer a code-first tool instead? See OWOX Model Canvas vs dbdiagram.io.
Frequently asked questions
DrawSQL has a free tier with paid plans for private and team diagrams. OWOX Model Canvas is free and open-source with no sign-up required.
DrawSQL focuses on polished, collaborative diagrams; its output is an image or SQL. OWOX Model Canvas focuses on the model itself and exports OKF — a portable, machine-readable spec you can version and hand to an LLM.
OWOX Model Canvas, because it exports OKF, a spec an agent can read as context. A diagram image from any tool doesn't give an LLM a model to reason over.
DrawSQL exports images and SQL, which are great for people and databases but aren't a tool-agnostic, machine-readable model. OKF (from OWOX Model Canvas) is designed to be exactly that.
DrawSQL requires an account. OWOX Model Canvas runs anonymously in the browser — nothing to sign up for.
You can share models and, because the export is plain-text OKF, collaborate the way you collaborate on code — in git, with pull requests and diffs — which is durable beyond any single tool's sharing feature.



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