How Bee Flow compares, without the marketing arithmetic

Compare

Including where we lose

Ten comparisons, and the same two questions underneath every one of them: where does your data physically go, and can you prove it. Each page below names what the other product does better — not as a courtesy, but because you have used at least one of them and would catch us inside a paragraph. What you get for reading them is a straight answer about which one to buy, including the cases where that is not us.

Open the app How sovereignty works

Pick your comparison

Whatever you are replacing, start here

Four categories, because the question is different in each. Against Microsoft it is governance. Against the automation tools it is what runs where. Against the AI subscriptions it is whether the model is the product or a part you choose. Against the other AI workspaces it is who operates the infrastructure and what you can prove about it. One answer runs through all four: 44 integrations ship built in, and anything else reaches Bee Flow through the Model Context Protocol — including a server you write yourself, which is why the connector count is the wrong thing to compare on.

Microsoft Power Platform

Agents, flows and internal apps — the same three things, on a stack you host, against a model you pick, with a report naming the country each prompt reached. One comparison, because Copilot Studio, Power Automate and Power Apps are one licence, one admin centre and one decision. Their Microsoft 365 integration is deeper than ours and will stay that way.

n8n

The closest comparison here, and not a hosting argument — you can already run n8n yourself. It is about what comes in the box beside the engine: assistants, cited retrieval, meeting notes and a PII layer, all on one audit trail.

Zapier

The same automations on your own hardware, with personal data stripped before any model call. Zapier is quicker to start with, and you take on a stack in exchange.

Make

Keep the canvas — conditions, loops, branches, approval gates — and move it onto hardware you own. Make is genuinely nice to use; what it is not is something you can run.

ChatGPT Teams

The same GPT models, in a workspace you hold: conversations under your own key, personal data removed before the provider sees it, and automations and knowledge bases around the chat.

Gemini for Workspace

The same Google Workspace connected — our deepest integration — with the model left as a setting, Gemini included. Inside Docs and Gmail, Google keeps the convenience win.

Claude Team

The same Claude, in a building you own: your key on the conversations, Anthropic never receiving the customer name, and a local model for the work that should not leave.

Langdock

The fairest fight on this page: the other European answer. Certified and hosted where we are self-hostable and auditable — two kinds of assurance, and which one you need decides it.

Dust

Excellent hosted agents over Slack and Notion — and theirs are deeper there than ours. Our answer runs on infrastructure you hold, with automations and audit under the same roof.

Open WebUI & LibreChat

You already self-host the chat, on the same local models we speak to. This comparison is about the rest: identity, redaction, automations and the audit trail around the conversation.

When Bee Flow is the wrong choice

If your organisation lives entirely inside Microsoft 365 and your governance already accepts Microsoft as a processor, Copilot Studio starts closer to finished than we do — we have no Teams and no SharePoint integration. If you want to connect two SaaS products in ten minutes and never think about it again, a hosted automation tool is less work than running a stack. And if nobody in your organisation has ever asked where a prompt went, you are paying for an answer you do not need. We would rather you read that here than discover it in week three of a pilot.

What every one of these comparisons comes down to

Two questions. Where does the data physically go, and can you prove it? Every product on this page can automate work, connect to your systems and put a language model behind it. What differs is whether the answer to those two questions is a policy document or a fact you can check by reading the source and watching the network. That is not a better feature. It is a different category of answer, and it only matters to some organisations — the ones where somebody is accountable for it.

Before you read further

How these pages are written

Do you name competitor prices?

Never. Pricing and packaging change constantly, and a comparison page quoting a stale figure is wrong in public and looks like it is trying to mislead. Check their pricing page — it is authoritative and ours is not. A test in our build fails the site if a competitor price appears on any of these pages.

Are these written by someone who has used the other tools?

They are written from public documentation and hands-on evaluation, and they stick to what is checkable. Where we are not certain of a detail, we leave it out rather than guess. If you find something wrong on one of these pages, tell us and we will correct it — being wrong about a competitor is worse for us than being quiet about them.

Why do the pages admit where you lose?

Because the reader can tell. Anyone comparing platforms has used at least one of them and knows its strengths; a page that pretends otherwise loses credibility on the first paragraph and takes the rest of the site with it. Naming the gap is also how you find out quickly whether we are wrong for you.

Can you help us run the comparison ourselves?

Yes, and the honest version is that you should. The whole stack starts with one command and no licence key, so you can rebuild one real workflow in your own environment and judge it on your own data rather than on our description of it.

Bring the question nobody will answer in writing

If the answer is no, or not yet, you will get that instead.

Start a conversation Run it yourself first