Dust vs GoSearch, Dust vs Zapier and Dust vs Google Agentspace compared

Buy GoSearch if the sentence that describes your problem starts with the words nobody can find. Buy Dust if it starts with we want to build. That sounds glib, but it holds up: GoSearch indexes your apps and answers questions about what is in them, while Dust gives you a workbench for constructing agents that do work against that data. They overlap in the middle, where both will answer a question with a citation, and they diverge completely at the edges.

The practical decision usually comes down to who owns the tool after the trial ends. GoSearch, like any search product, is bought once and used by everyone; nobody has to maintain it for the sales team to keep searching. Dust rewards a person Dust itself calls an AI Operator, someone who builds agents, tunes them and keeps them working. If you have that person and they are excited, Dust is a good buy. If you were planning to roll it out and walk away, the agents will drift and the seats will go quiet, and you will have paid for credits nobody spent.

Price is the other honest difference. GoSearch publishes 20 dollars per user per month for Pro and you can budget from that immediately. Dust publishes 30 euros a month for a Pro seat, or 24 euros billed yearly, plus a credit allowance of 8,000 a month that a heavy user can exhaust. Two things follow. The euro denomination means a US buyer carries an exchange-rate line item on a SaaS invoice, which finance teams notice. And the credit meter means the number on the contract is a floor, not a ceiling.

No, and the clearest evidence is on Dust's own pricing page, which lists Zapier under automation platforms alongside Make, n8n and Power Automate. Dust treats Zapier as something you connect to, not something you replace. If you are comparing them, you have probably been sold both under the heading of automation and are trying to work out which one to cut.

Here is the split. Zapier moves data and fires triggers between applications on rules you define: when a form is submitted, create a row, notify a channel. It is deterministic and it does not reason. Dust runs language models against your company data and can decide what to do inside a task, which is more capable and much less predictable. A Zap either fires or it does not. An agent produces an answer whose quality varies with the prompt, the model and what it retrieved.

Most companies that run both use them for exactly those jobs, and the real question is not which to cut but whether either of them is the thing that finds answers. Neither is, particularly. Automation assumes you already know which document matters. Agent platforms assume someone will build the agent that goes and gets it. Search assumes nothing and just answers the question, which is why it usually gets deployed first and kept longest.

Google renamed Agentspace to Gemini Enterprise in October 2025. It was a rename, not a discontinuation, and that distinction matters because a lot of comparison content still treats them as two products you can choose between. You cannot. If a vendor page or a consultant deck offers you Agentspace as a live alternative to Gemini Enterprise in 2026, that page has not been updated.

Against Dust, the comparison is really about procurement shape. Gemini Enterprise is a Google Cloud product, not a Microsoft-365-style add-on you switch on per user, so buying it means going through Google Cloud with the billing account and the commit conversation that implies. Google documents the seat model in terms of daily proration, describing a monthly seat fee of 30 dollars billed at approximately one dollar per day, with consumption charged above quota. Dust, by contrast, you can start on a card the same afternoon, at least until you hit the connector cap.

The second real difference is identity. Gemini Enterprise syncs identity on a schedule that can be set anywhere from every 30 minutes to every 7 days, and that interval is your revocation window: it defines how long someone who left the company or moved teams can still surface content they should no longer see. That is the single most important question to ask in a Gemini Enterprise evaluation, and it is worth asking of any tool in this category, including Dust and including us. Our own answer is on the permission-aware AI search page.

Give Google real credit where it is due: if your company already runs on Google Cloud and Workspace, the integration story is strong and the model quality is excellent. The question is whether you want your internal search tied to one cloud vendor's roadmap.

These two are the only comparisons on this page where Dust is being measured against something of the same species. Relevance AI is an agent and AI-workforce platform, and as of August 11, 2026 its pricing page shows an Enterprise tier only with no figures at all, so any head-to-head on cost has to happen inside a sales process. That is worth knowing before you invest evaluation time: with Dust you can model a budget from published numbers, and with Relevance AI you cannot until someone calls you back.

Langdock is the European entrant, and for a US buyer that is the whole story. Its positioning leans heavily on EU data residency and GDPR posture, which is a genuine advantage for a company with a German or French works council and largely irrelevant to a company in Chicago. Dust also offers EU residency, and offers US residency too, with single-tenant deployment available on Enterprise. If you are US-based and comparing Dust with Langdock, the residency argument that Langdock leads with is not an argument in your favor.

The reason all three keep appearing on search-tool shortlists is that agent platforms describe themselves in terms of outcomes, and one of the outcomes they describe is finding information. They can do that. They are simply an expensive and indirect way to do it, because you have to build the thing that does it before you get an answer.

Three things, and they are worth being precise about because this is where a lot of budget gets wasted. First, connected company data: Dust indexes Slack, Notion, GitHub, Drive and around twenty other sources, so an agent can answer from your documents rather than from the public internet. Second, persistence: agents are objects you build once, share with a team and run on schedules and triggers, rather than a conversation that dies with the tab. Third, model choice, since Dust exposes more than twenty frontier models across OpenAI, Anthropic, Google, Mistral and DeepSeek, and lets you pick per agent.

What it does not add is a permissions model you get for free. Any tool that indexes company content has to decide what each person is allowed to see, and that is a harder problem than connecting the source. When you evaluate, ask what happens the day somebody moves from sales to finance: does the index reflect that immediately, or at the next sync? The answer is the whole security story.

If your team is mostly using ChatGPT to ask questions about internal documents, an agent platform is one way forward and enterprise search is the other. The dividing line is whether you want to build the answer path or just have one.

Dust prices in euros, in seat types inside a Business plan, with usage metered in credits. Taken verbatim from Dust's own pricing FAQ on August 11, 2026: a Free seat is 0 euros with 500 credits lifetime, a Pro seat is 30 euros per month or 24 euros billed yearly with 8,000 credits per month, and a Max seat is 150 euros per month or 120 euros billed yearly with 40,000 credits per month. You can mix seat types across a workspace. Enterprise pricing is not published.

Three details do more to your budget than the headline seat price. Credits do not roll over: in Dust's own words, each seat's monthly credit allocation resets at the start of every billing period. Programmatic usage on Business is billed at one cent per credit. And credit consumption is not flat, because a simple chat with a token-efficient model costs a few credits while a deep research task with multi-step tool use costs many more. That is a defensible way to price a platform where usage genuinely varies, but it is not a number you can put in a budget line and forget.

The cliff that actually ends the self-serve plan is structural, not financial. Business is for teams up to 100 people and allows up to 3 connectors, 5 Spaces and 5 remote MCP servers, with email-only support. Slack, Google Drive and Notion is three connectors. The moment somebody asks for Confluence, Jira or GitHub as well, you are in an Enterprise conversation with no published price. Budget for that call, because for most companies it is not an edge case, it is week two.

It depends which half of Dust you are replacing. If you want the agent-building half, the closest peers are Relevance AI, Langdock and the agent layers inside the big clouds, meaning Gemini Enterprise on Google and Copilot Studio on Microsoft. If you want the find-answers half, you are shopping for enterprise search, and the shortlist looks completely different: Glean, GoSearch, Guru, Onyx and us.

Splitting the requirement that way tends to save money, because the two halves have different buying patterns. Search is bought for everyone and used daily by people who will never configure anything. Agent platforms are bought for a handful of builders and pay off through what those builders ship. Buying one product to do both usually means over-licensing the many to serve the few.

Our full sixteen-product breakdown is on the enterprise search tools page, and the single-vendor view is on Dust alternatives. If you want to see what the search half feels like before talking to anyone, the console at the top of this page runs against real content.