Top 5 Dust Alternatives (2026)

Dust is what happens when ex-OpenAI and ex-Stripe people build an agent platform: a serious, Sequoia-backed French product for deploying department-level AI assistants connected to your company data — Slack, Google Drive, Notion, Confluence, GitHub — with a native Teams integration and a security posture that takes European buyers seriously: SOC 2, zero data retention with model providers, and a selectable EU hosting region. Among the agent platforms, it is one of the most credible. So why do so many teams end up searching for a Dust alternative in 2026?

Five reasons come up in almost every evaluation:

• Someone has to build the agents. Dust is a platform for constructing assistants, not an assistant. Every agent needs designing, connecting to data sources, prompting and maintaining. For tech-forward teams with a motivated builder, that model works brilliantly. In companies without one, the platform stalls after the pilot — and the licence keeps billing.
• The assistants only answer. A Dust agent waits to be invoked. Nothing watches your company’s signals, surfaces the decision you are about to miss, or prepares work before someone asks. There is no proactive intelligence anywhere in the model.
• No persistent self-learning memory. Assistants know what they are connected to, but they do not accumulate organizational knowledge on their own. The understanding lives in the configuration someone built — not in a memory that grows from how your company actually works.
• EU is a region setting, not a residence. Dust lets you select EU or US hosting. A selectable EU region is real, but it is not German hosting by default — and for buyers with strict data-residency requirements, that difference matters.
• Teams is an integration, not the home. Dust’s centre of gravity is Slack, Google Drive and Notion. The Teams integration is genuine, but a Teams-centric company is adopting a platform that was built around someone else’s stack.

None of this makes Dust a bad product — it makes Dust a particular product, built for a particular kind of team. If yours is a different kind, here are the five dust.tt alternatives worth shortlisting in 2026, each with its genuine strengths and honest limitations. For a direct head-to-head, see amaiko vs Dust.

1. amaiko — the AI that works before anyone builds anything

The cleanest way to put the difference: Dust gives you a workshop; amaiko gives you a colleague. Where Dust asks your team to construct assistants, amaiko works out of the box — zero onboarding, zero agent construction, zero maintenance backlog.

It starts with where it lives. amaiko is Teams-native — not an integration into Teams, but built for it. One Teams chat: no separate builder interface, no rollout project, no training sessions. You add it, you start typing, and onboarding is done. For companies that live in Microsoft Teams, the adoption question Dust struggles with simply never comes up.

The structural difference, though, is memory and initiative.

amaiko builds a persistent corporate memory. It learns how your company works — projects, customers, decisions, preferences — and keeps that knowledge across every conversation. Nobody configures it; it accumulates on its own, and every interaction makes the next one more useful. This is precisely what Dust’s model cannot do: a Dust assistant knows what its builder connected, while amaiko knows what your company has taught it by simply working.

That memory feeds a self-learning agent network: specialized AI agents for email, meetings, research and company systems. Here is the answer to Dust’s builder problem — the agents exist, they are already specialized, and they improve from how your organization actually operates. Nobody on your team designs them, prompts them or maintains them. Underneath run state-of-the-art models, not a single fixed model quietly ageing behind the brand.

And amaiko acts proactively. It does not wait to be invoked. It surfaces what needs your attention, prepares your day, and follows up on what would otherwise slip — the entire category of value that an invoke-only assistant platform structurally leaves on the table.

On integration, amaiko orchestrates all company-internal systems — SAP, your CRM, internal tools, industry software. If your business runs on it, amaiko can work with it.

Compliance is where the contrast with “selectable EU” is sharpest: amaiko hosts 100% in Germany — not as a region you pick, but as the only way it runs — with ISO 42001-compliant AI management. Details on the security page. Pricing starts at €19.92 per user per month, billed annually — see pricing.

One honest caveat: if your team genuinely wants to design custom department-level assistants — full control over prompts, data sources and behaviour — and has the people to sustain that, Dust’s builder is genuinely good, and amaiko deliberately does not offer one. And if your company lives in Slack and Google Drive rather than Microsoft Teams, you are not amaiko’s audience: it is built for Teams-centric companies, without apology.

Best for: Teams-centric companies that want the outcome Dust promises — AI working on company knowledge — without staffing an agent-building function to get it. You can book a demo to see it in your own tenant.

2. Glean — enterprise search instead of agent building

Glean removes Dust’s central burden by answering a different question entirely: not “build an assistant” but “find it for me.” It indexes more than 100 workplace tools — including a Teams connector — and for large organizations drowning in scattered knowledge, its enterprise search is genuinely powerful. Nothing to design, nothing to maintain: connect the tools and search.

The trade-offs are equally structural. Pricing starts at $50+ per user per month with enterprise-only commitments that run into six figures annually — the mid-sized buyer is priced out before the pilot. The platform runs on US cloud infrastructure, which puts the CLOUD Act question on your data protection officer’s desk. And search is not assistance: Glean finds documents; it does not act on what it finds, prepare your morning, or draft your follow-ups.

Full comparison: amaiko vs Glean.

3. Cassidy — the US agent platform with a Teams add-in

Cassidy is the closest thing on this list to Dust’s own model: a US agent and workflow platform that connects company knowledge bases to assistants, with a dedicated Microsoft Teams Add-In — and SOC 2 Type II, GDPR and HIPAA claims on the compliance page. For teams that like Dust’s build-your-assistant approach but want a tighter Teams story, it is a fair candidate.

Two gaps stand out. First, there is no EU data residency — Cassidy runs on US infrastructure, so the CLOUD Act question that a selectable EU region at least addresses goes entirely unanswered here. Second, the paradigm is the same as Dust’s: assistants are constructed and invoked. No proactive push, no persistent corporate memory that grows on its own — you are trading one builder’s workbench for another.

Full comparison: amaiko vs Cassidy.

4. Langdock — the EU enterprise AI platform with the references

Berlin-based Langdock holds the strongest enterprise references in the European market: over 7,000 companies, including Merck with 33,000 monthly active users. The platform is genuinely model-agnostic — 40+ models from GPT-5 to Claude to Gemini — with custom agents, mature workflow automation, and hosting in German and EU data centers. For a buyer leaving Dust over the EU-as-a-setting question, that hosting posture is a real upgrade.

The limitations: Langdock is a chat-and-agent layer, and the agent half carries the same structural cost as Dust — agents are built and invoked, every interaction user-initiated, with no proactive intelligence and no persistent corporate memory that grows on its own. There is no Teams-native presence; your team works in a separate app. And per-seat pricing of €25–99 per user per month compounds: a 100-person organization pays €2,500 a month before workflow add-ons.

Full comparison: amaiko vs Langdock.

5. Onyx — open source, your infrastructure, your rules

Onyx (formerly Danswer) takes the sovereignty question to its logical conclusion: an open-source enterprise AI search and assistant platform, MIT-licensed, self-hostable, with 40+ connectors including Microsoft Teams and a model-agnostic architecture. Free if you self-host, $20 per user per month in the cloud. Where Dust offers a selectable EU region, Onyx offers something stricter — your own infrastructure, so data residency is wherever you put the servers.

The catch is in the word “self-host.” Onyx requires real engineering effort to deploy and maintain — you trade Dust’s agent-building work for infrastructure work, which is not obviously a smaller job. And the paradigm remains pull-based: search and assistants that answer when asked, no proactive delivery, no persistent memory accumulating on its own, and no dedicated compliance support when your auditor calls.

Full comparison: amaiko vs Onyx.

How to choose

Match the tool to the team you actually have, not the team the vendor imagines:

• You want AI that works on company knowledge without building or maintaining anything, and your company lives in Microsoft Teams: choose amaiko. Out of the box, proactive, persistent self-learning memory, 100% German hosting — from €19.92 per user per month.
• Your core problem is finding information across 100+ tools and you have an enterprise budget: Glean — if US cloud infrastructure passes your data protection review.
• You like the build-your-assistant model and want a dedicated Teams add-in: Cassidy — accepting that your data is processed in the US.
• You want a model-agnostic AI workbench with German and EU hosting and proven enterprise scale: Langdock has the references.
• You want full data sovereignty and have the engineers to run it: Onyx — free self-hosted, $20 per user per month in the cloud.

And in fairness to Dust: if you have a motivated builder, full control over department-level assistants is a genuine capability, and Dust executes it as well as anyone in Europe. The real question is the one this whole list keeps asking — do you want to build your company’s AI, or do you want your company to have one that already works?