Both of you say dashboards as code. What is the actual difference?

What the code is. Embeddable dashboards are defined in their model — data models and dashboard definitions kept in version control and rendered by their components. vibedasher hosts the React your agent wrote, in whatever shape it wrote it. Theirs buys you consistency and a component library that was designed to be embedded; ours buys you no translation step between the agent and production.

Which has the better embedding story?

On the rendering mechanics, theirs is the more opinionated and arguably the better engineered: native web components rather than iframes, which removes a class of styling and sizing problems. We serve an embed and the dashboard is your own React, so the integration is yours to shape. If you want the vendor to have solved embedding for you, that is a point for them.

Can I self-host Embeddable?

Their site presents a cloud-hosted product that connects to your own database rather than copying your data out — we found no self-hosting offer, but check their docs rather than take our reading of a homepage. vibedasher documents docker compose and a Helm chart, with the honest caveat that no customer runs the self-hosted path in production yet.

What is each licensed under?

Checked on 2026-10-06, from their published site and terms. Embeddable is a commercial product; we found no public source licence, so treat it as proprietary and check their terms. vibedasher is also proprietary, with a self-host option. Neither of us is open source, and a reader for whom that is the deciding factor should look at Metabase, Superset, Lightdash or Evidence instead — the other four pages in this set.

Can an agent build an Embeddable dashboard?

They describe AI-assisted development workflows for scaffolding dashboards, and an end-user AI chat grounded in the semantic layer with row-level security enforced. So an agent helps author within their model. The distinction we draw is narrower than it sounds: in their product the agent helps you produce their artifact, and in ours the agent's artifact is the one that ships.

vibedasher vs Embeddable

Who Embeddable is for

Embeddable is "built for developers, used by product and data teams", and its subject is customer-facing analytics inside a SaaS product. Its positioning phrase is "dashboards as code" — analytics experiences defined, versioned and managed in a developer workflow — with a no-code builder and AI-assisted scaffolding available on top. It is cloud-hosted and connects to your own database rather than copying your customers' data into a separate warehouse. Embedding is via native web components rather than iframes, with multi-tenant support, row-level security and role-aware access, and there is an end-user AI chat grounded in the semantic layer. Of the six tools here it is the one aimed most precisely at the same job we are, and on the embedding mechanics specifically it is the more opinionated product.

The comparison

Every statement about Embeddable on this page describes what their own documentation, security page, changelog and pricing page said as of 2026-10-09, read directly rather than from a search summary. This category moves fast — check their site for the current state. Where their own sources disagree with each other, we say so instead of picking the reading that suits us.

	vibedasher	Embeddable
Who writes the dashboard	Your coding agent, as React components over SQL. We host that tree as a static, client-rendered bundle — React only, with none of your server code in the request path and no `npm install` at build time, because the libraries come from a pinned list.	Developers, as data models and dashboard definitions in version control, rendered by their components. A no-code builder and AI scaffolding sit on top.
Hosting and governance	A URL behind your login, or an embed inside your product. Per-viewer row filters are the embed lane and only the embed lane — enforced server-side from a signed token. A dashboard you publish publicly is the other case: its credential-free data lane serves one Parquet extract per dataset and snapshot, so every visitor gets every row of it.	Cloud-hosted by them, reading your own database. Multi-tenant with row-level security and role-aware access; embedding via web components, not iframes.
MCP / agent control plane	An MCP server covering the whole loop: connect a source, push dashboard code, build, publish, schedule a refresh, eject. `pip install vibedasher-mcp` then `vibedasher login`. A refresh needs a dataset backed by a live connection — an uploaded file cannot be scheduled — and every cron is read in UTC.	A REST surface, `/ai/v1/`, plus example* MCP server code in Python/FastMCP that you run yourself: "A ready-to-use MCP (Model Context Protocol) server (Python / FastMCP) implementing all tools above is available below." The page is marked "still in Beta. APIs and behaviour may change." So the protocol surface exists as sample glue you operate, not as an endpoint they operate.
Eject-to-code	Yes. React source in your repo, still querying our API. A real ejected dashboard runs on this site.	The definition is already yours and that is a real strength: "Add a file like `my-dashboard.embeddable.yml` anywhere in your repo", "The repo is the source of truth." What is theirs is the runtime — the embed loads `embed.embeddable.com` and a server-side token. Their only export is a rendered picture: the Dashboard Export API "returns it as a print-quality PDF", PDF being the one supported format. Nothing in their docs states what remains runnable if you stop paying.
Self-host	`docker compose` for one host, a Helm chart for Kubernetes, the same engine as the managed cloud. No production customer yet.	Their own sources contradict each other, so ask them. Their docs describe no self-hosted deployment of the Embeddable platform — the security page says "Embeddable's production infrastructure runs on AWS" with a choice of EU or US hosting, and the self-hosting it does describe is of Cube's open-source layer wired in as a data provider. Their comparison blog says the opposite: "Cloud and on-premise (self hosted) options are available." We could not find a docs page supporting the blog.
Managed cloud region	Data plane in the European Union, Frankfurt. Sign-in identity — email address and account membership — is held in the United States for every account; there is no region selector. The self-hosted build keeps both in your network.	Two regions: "We currently support `US` and `EU` deployment regions (more coming soon)." EU is Frankfurt with disaster recovery in Ireland. They also state they "does not store a copy of your database" and connect with a read-only account, which is a genuine architectural difference from us.
Connectors	Warehouses, databases and SaaS sources, plus CSV and Excel upload. The count is on pricing; the full list, including what is not available yet, is in the documentation.	Your existing database, connected in place — see their documentation.
Licence	Proprietary, with a self-host option.	Commercial; no public source licence found. Their semantic modelling language is the open-source one from Cube, which they say themselves: "using the open-source Cube modeling language".
Pricing model	See /pricing.	Quote-only, deliberately: "a flat monthly subscription — with unlimited usage, every feature, and complete price-predictability. Speak to us to get a custom quote." No figure is published, and usage is explicitly not the pricing driver. Their pricing page.

Choose Embeddable if

You want the vendor to have solved embedding: web components, theming, sizing and multi-tenant access as a designed surface rather than your integration work.
A semantic layer that both your charts and an end-user AI chat answer from is part of what you are buying.
Your developers want to define dashboards once, in a reviewed format, and have non-developers assemble from those parts.
Having your data stay in your own database while someone else runs the analytics layer is exactly the trade you want.

Choose vibedasher if

Your coding agent is already producing the dashboard and you want that artifact in production without re-expressing it in a vendor's model.
You want the exit in writing: eject-to-code returns React source, and there is a real ejected dashboard on this site to inspect.
The agent should run the whole loop — connect the source, publish, schedule the refresh — from a terminal over MCP.
European residency or your own network is a constraint; see self-hosted BI, caveat included.

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Frequently asked questions

Both of you say dashboards as code. What is the actual difference?: What the code is. Embeddable dashboards are defined in their model — data models and dashboard definitions kept in version control and rendered by their components. vibedasher hosts the React your agent wrote, in whatever shape it wrote it. Theirs buys you consistency and a component library that was designed to be embedded; ours buys you no translation step between the agent and production.
Which has the better embedding story?: On the rendering mechanics, theirs is the more opinionated and arguably the better engineered: native web components rather than iframes, which removes a class of styling and sizing problems. We serve an embed and the dashboard is your own React, so the integration is yours to shape. If you want the vendor to have solved embedding for you, that is a point for them.
Can I self-host Embeddable?: Their site presents a cloud-hosted product that connects to your own database rather than copying your data out — we found no self-hosting offer, but check their docs rather than take our reading of a homepage. vibedasher documents docker compose and a Helm chart, with the honest caveat that no customer runs the self-hosted path in production yet.
What is each licensed under?: Checked on 2026-10-06, from their published site and terms. Embeddable is a commercial product; we found no public source licence, so treat it as proprietary and check their terms. vibedasher is also proprietary, with a self-host option. Neither of us is open source, and a reader for whom that is the deciding factor should look at Metabase, Superset, Lightdash or Evidence instead — the other four pages in this set.
Can an agent build an Embeddable dashboard?: They describe AI-assisted development workflows for scaffolding dashboards, and an end-user AI chat grounded in the semantic layer with row-level security enforced. So an agent helps author within their model. The distinction we draw is narrower than it sounds: in their product the agent helps you produce their artifact, and in ours the agent's artifact is the one that ships.