Dust vs Toolhouse
Side-by-side comparison of features, pricing, and ratings
Dust or Toolhouse: which should you choose?
| Dimension | Dust | Toolhouse |
|---|---|---|
| Who builds the automation | You configure agents: instructions, skills, knowledge, permissions | You brief a worker in plain language; it plans and executes |
| Integration breadth | 70+ connectors plus native/remote MCP servers | 1,000+ integrations |
| Model choice | 20+ models selectable per agent (GPT, Claude, Gemini, Mistral, DeepSeek) | Not specified in provided data |
| Team/admin controls | SSO (Okta, Entra ID, Jumpcloud), SCIM, Spaces data segmentation, audit logs, usage analytics | Audit logs per worker action; data encrypted in transit and at rest |
| Documented weak spot | Requires real configuration; needs an ops owner | No free tier; not for developers needing fine-grained control or conditional branching |
Our Dust vs Toolhouse verdict written Sep 21, 2026 quotes $500, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Dust builds multiplayer AI agents that pull company context from 70+ connectors and act inside Slack, CRM and docs.
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Toolhouse runs plain-language task delegation to AI workers — email a brief, get finished work back in your inbox.
Visit WebsiteWhat do real users say about Dust vs Toolhouse?
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
Dust
86 mentions across 6 sources · 49% positive — mixed (weighted across 6 sources)
Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, Lemmy
What users praise
- • 70+ native connectors (Slack, Notion, Salesforce, Zendesk, GitHub) let agents act on real company systems
- • Per-agent model choice across 20+ frontier and open-source models enables cost/quality tuning
- • Dual-layer permission model separates what agents access from who can use them
- • US or EU data residency plus SSO, SCIM and audit logs address enterprise governance needs
What frustrates them
- • No authentic community feedback exists in the scraped data to validate the tool's real-world reliability
- • Credits reset each period with no rollover, risking mid-month exhaustion for busy teams
- • Enterprise-only features (unlimited connectors, pooled credits, single-tenant) leave smaller tiers constrained
- • Name collision with games, novels and a messaging app makes independent research genuinely hard
Researched Sep 29, 2026
Toolhouse
38 mentions across 3 sources · 65% positive (weighted across 3 sources)
Hacker News, YouTube, Product Hunt
What users praise
- • Saves developers real hours wiring LLM tool use and integrations into products
- • Instantly adds LLM memory, webpage-to-Markdown parsing, and Google search capabilities
- • Plain-language email and Slack task briefing suits non-technical teams well
- • Model-agnostic design avoids locking you into a single provider's ecosystem
What frustrates them
- • Almost all feedback is launch-day hype; little independent long-term evidence
- • Not built for developers needing branch logic and step-level debugging
- • Some users want a broader portfolio of tools and integrations
- • Credits-based consumption makes runaway agent costs hard to predict
Researched Oct 16, 2026
Who should pick Dust, and who should pick Toolhouse?
- Ops lead at a 200-person SaaS with SSO and audit requirementsPick: Dust
Okta/Entra ID SSO, SCIM, audit logs and Spaces data segmentation are prerequisites before rolling agents across departments — Toolhouse's controls are thinner.
- Non-technical sales manager automating lead qualificationPick: Toolhouse
Lead qualification with outbound SMS and cold calls is a named capability, and workers are briefed by chat rather than configured — no ops owner needed.
- Insurance or finance team processing policy documentsPick: Toolhouse
Policy abstraction, summarization and instant policy queries through knowledge assistants match the job directly, with audit logs for every worker action.
- IT team building a scheduled incident-response or ticket-triage agentPick: Dust
Event-driven and scheduled triggers, GitHub/Zendesk connectors and MCP reach into systems Toolhouse's task-delegation model does not target, with per-agent model choice.
- Startup with no budget yet and no ops ownerPick: Dust
Freemium lets you build agents before paying, where Toolhouse has no free tier and limits itself to a $500+/month segment.
Frequently Asked Questions
Can I reach a system that isn't in either product's integration list?
On Dust, potentially yes — native and remote MCP servers let you call external tools beyond the 70+ connectors. Toolhouse's 1,000+ integrations are listed but no MCP-style extension path is described in the provided data.
Which one lets me choose the underlying model?
Dust documents per-agent selection from 20+ models including GPT, Claude, Gemini, Mistral and DeepSeek, and multi-modal input across frontier models. The provided Toolhouse data does not describe model selection.
Do unused credits carry over?
Dust's credits vary by model and tool use and do not roll over. Toolhouse paid plans include unlimited tokens, so credit expiry is a Dust-specific budgeting concern.
Which is better for a team that wants no setup at all?
Neither is zero-config, but Toolhouse is closer: you brief a worker by email or chat instead of defining instructions, skills and permissions. Dust explicitly needs real configuration and an ops owner.
Is there a free way to try them?
Dust is freemium, so you can build and test agents before spending. Toolhouse is paid-only with no free tier, and its own not-for list flags users needing a low-cost alternative at $500+/month.
How do the two handle audit and compliance review?
Both log activity — Dust offers audit logs plus usage analytics across adoption, cost and ROI, while Toolhouse logs every worker action. Dust adds SSO via Okta, Entra ID and Jumpcloud, SCIM provisioning and per-team data segmentation, which Toolhouse's feature list does not claim.
Which suits a developer who wants precise control over logic?
Dust, on the strength of per-agent instructions, skills, triggers and MCP tooling. Toolhouse states it is not for developers needing fine-grained control or multi-step workflows with conditional branching.
What changed most recently on the Dust side?
As of July 2026 Dust shipped self-improving skills — reusable instruction sets that encode team expertise and adapt from usage — and added European consultancy Niji as an official partner, alongside published work on measuring AI deployment ROI.
What else are Dust and Toolhouse compared with?
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Where can you read more about Dust and Toolhouse?
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Analysis generated by our model pipeline from live data: