amaiko vs Dust: Built Agents vs a Memory That Learns (2026)

What Dust does genuinely well

Most of the comparisons on this site put amaiko next to tools that solve a different problem and call it a contest. Dust is not one of those. Built in France by ex-OpenAI and ex-Stripe founders and backed by Sequoia, Dust is the most technically comparable competitor on our list — a serious agent platform that has earned the right to be evaluated as a peer. So let’s do exactly that.

The no-code agent builder is genuinely good. Dust lets a department build its own AI assistants and connect them to the data they actually work with — Slack, Google Drive, Notion, Confluence, GitHub. A sales team can build a sales agent, support can build a support agent, and nobody has to write code to do it. That is a real capability, not a demo.

The security posture is serious. SOC 2, zero data retention agreements with the model providers, and a selectable hosting region — EU or US, your choice. That is a more honest answer to the European data question than most US competitors even attempt.

Model-agnostic, like us. Dust does not lock you into one vendor’s model stack. When a stronger model ships, you can use it. amaiko works the same way — your requests are routed to the best available state-of-the-art models — so call this one a draw, and a point of genuine architectural agreement between the two platforms.

Native Teams integration exists. Dust connects to Microsoft Teams out of the box. No custom development, no consulting project.

And at roughly $29 per user per month for the Pro plan, none of this is overpriced. So why does this page exist? Because being able to build good agents and having good agents already working for you are two different states of the world — and the distance between them is where this comparison is actually decided.

Five structural differences

The amaiko vs Dust question is not about whether agents are a good idea — both platforms agree they are. It is about who builds them, what they remember, and where they live.

Dust’s agents answer. amaiko’s act.

A Dust agent is a capable respondent: ask it something, and it answers from the data sources it is connected to. But it answers when asked — there is no proactive push. Nothing arrives that you did not request. amaiko works the other way around: your morning briefing is ready before you open the laptop, your inbox is triaged before you read it, meeting follow-ups appear without anyone asking. The questions you never think to ask are precisely the ones a reactive agent never answers — and over a working year, those unasked questions are where the real productivity sits.

Configured is not learned

A Dust agent knows what its builder configured it to know. It is a snapshot: the data sources someone connected, the instructions someone wrote, frozen at configuration time. When your company changes — new decisions, new people, new context — someone has to go back and update the agent. There is no persistent, self-learning corporate memory underneath. amaiko’s memory is the opposite of a snapshot: it grows with every interaction — decisions, context, who knows what, why things were done the way they were — and nobody maintains it, because learning is what it does. The measurable effect for amaiko teams: 35% less time spent searching and onboarding up to 57% faster, because new hires inherit a living memory instead of a well-configured but static assistant.

Someone has to build the agents

No-code does not mean no work. Dust’s model assumes a builder: a person in each department who designs the agents, connects the data sources, writes the instructions, tests the results and maintains all of it as the company evolves. In a tech company with an enthusiastic AI champion on every team, that works. In a normal company, the builder role quietly becomes a bottleneck — or stays unfilled, and the platform underdelivers. amaiko ships the other way around: a network of 24 specialized AI agents — for meetings, email, research, knowledge linkage and more — arrives pre-orchestrated. The agent network is the product, not a kit for making one. Onboarding is a single Teams chat: install, say hello, it starts working.

Teams is a channel. For amaiko, it’s home.

Dust connects to Teams — credit where due, see above. But Dust’s center of gravity is its own workspace, and its connector list reads Slack-first: Slack, Google Drive, Notion, Confluence, GitHub. Teams is one delivery channel among several. amaiko was built for Teams from the first line of code: Teams is not where amaiko forwards its answers, it is where amaiko lives — alongside your calendar, your mail and your meetings. If your company runs on Microsoft Teams, the difference between a tool that posts into Teams and a colleague that works in Teams shows up every single day.

An EU region is not German hosting

Dust’s selectable EU hosting region is real, and we credited it above — most US competitors do not offer even that. But an EU region is not 100% German hosting, and for a German Mittelstand company whose legal or works-council requirements name Germany specifically, the distinction is not pedantic. amaiko’s answer needs no map: 100% German hosting and ISO 42001 certification — the management standard for AI systems. The data question, settled rather than regionalized.

The pricing reality

Here, refreshingly, there is no trap to expose. Dust’s Pro plan runs about $29 per user per month; amaiko starts at €19.92 per user per month, billed annually. amaiko is cheaper, but both are honestly priced platforms in the same bracket — no compulsory base license underneath, no five-figure enterprise floor. Which means the decision does not happen on the pricing page. It happens on architecture: built agents vs a learning memory, answers vs initiative, an EU region vs German hosting.

Who should choose which

Honest segmentation — Dust has earned it.

Choose Dust if your company lives in Slack, Google Drive and Notion rather than Microsoft Teams, you have people who genuinely enjoy designing and maintaining department agents, and a selectable EU region clears your compliance bar. In that world, Dust is an excellent choice — arguably the best of the builder platforms.

Choose amaiko if Teams is where your company actually works, you want AI that acts before you ask and permanently remembers what your organization learns, nobody on your team wants “agent builder” as a side job, and your data question is only settled by hosting in Germany.

Unlike most match-ups on this site, these two genuinely overlap — both are agent platforms, both model-agnostic, both seriously priced. Running both makes little sense; pick the one that matches your stack and your appetite for building. If you are surveying the wider field, our roundup of Dust alternatives covers the other contenders. And if you would rather see a memory that learns than read about it: book a demo — it takes one Teams chat to show you.