# Dust vs Datascale

Dust records 547,217 monthly visits with +1.8% month-over-month growth and Contact for Pricing pricing. Datascale records 1,304 monthly visits with +9.4% growth and Free Trial pricing.

| Metric | [Dust](https://openaitools.app/tool/dust) | [Datascale](https://openaitools.app/tool/sql-notes) |
| :--- | :--- | :--- |
| Website URL | https://dust.tt | https://getdatascale.com |
| Categories | AI Assistant, Large Language Models (LLMs), AI Workflow, AI Knowledge Management | AI Knowledge Management, AI For Data Analytics, AI Diagram Generator, AI Search Engine |
| Pricing Model | Contact for Pricing | Free Trial |
| Monthly Visits | 547,217 | 1,304 |
| MoM Growth Rate | +1.8% | +9.4% |
| Primary Market | France | India |
| Direct Visits | 456,919 | 504 |
| Review Score | 4.4 | 4.3 |
| Saved Count | 5 | 1 |
| Supported Platforms | Website | Website |

## Dust

Dust positions itself as 'multiplayer AI' for human-agent collaboration. Rather than giving each employee a private chatbot, it lets teams build agents that have shared access to company knowledge, connected tools, conversations and notifications, so work can move back and forth between people and AI. Agents can be reused across departments, and human review stays part of the loop where it matters.

The platform's differentiator is context. According to Dust, most AI tools can retrieve a single Slack message or CRM record, whereas its agents pull context together across Slack, CRM, documents and other company systems to answer with the full picture and then act on it. Over 70 connectors cover messaging, calendars, CRM, file storage, project management, data and finance tools, and agents can reach MCP servers through the same gateway.

Governance is a first-class feature. Dust offers a dual-layer permission model that separates what agents can access from who can use them, SCIM-synced groups, admin-gated overrides, audit logs and analytics for monitoring adoption, usage, cost and ROI. It is SOC 2 Type II certified, offers US or EU data residency and supports single-tenant deployment. Teams can build agents with 20+ frontier and open-source models from OpenAI, Anthropic, Google, Mistral and others, choosing the right model per workflow. The company says more than 3,000 organizations and 300,000+ agents run on the platform.

**Pricing & Plans:** Contact for pricing. Dust offers a free trial after signup, but its plans are oriented toward teams and enterprises with usage-based credits, volume pricing and dedicated support; you need to talk to sales for exact figures.

- **Agents with company context:** Connect 70+ tools including Slack, Notion, Google Drive, GitHub, Salesforce and Zendesk so agents answer with real company knowledge instead of generic guesses.
- **70+ connectors and MCP support:** Reach messaging, CRM, docs, project management, data, finance and MCP servers through one platform, so agents can act across systems rather than just chat.
- **Model flexibility:** Build agents with 20+ frontier and open-source models from OpenAI, Anthropic, Google and Mistral, and pick the best model for each task.
- **AI-native governance:** A dual-layer permission model, SCIM-synced groups, admin-gated overrides, audit logs and usage analytics keep company data locked down while teams move fast.
- **Multiplayer collaboration:** People and agents share context, tools and notifications, so cross-functional workflows run with reusable skills and human review built in.

**Strengths:** Deep context gathering across 70+ enterprise connectors; Strong enterprise governance with permissions, audit logs and SOC 2 Type II; Model-agnostic: choose among 20+ frontier and open-source models; Shared, reusable agents that scale across departments

**Limitations:** Pricing is sales-led, so costs are hard to estimate before a demo; Setting up connectors and permissions takes real admin work; Best value comes with broad rollout, which is a heavier commitment for small teams

[Profile (Markdown)](https://openaitools.app/tool/dust.md)

## Datascale

Datascale describes itself as "Cursor meets Miro for databases" — a visual canvas where you design, document and discuss data models, with an AI copilot embedded rather than bolted on. The core trick is reverse engineering: point it at DDL, CTAS (CREATE TABLE AS SELECT), views and INSERT INTO statements and it draws out the primary and foreign keys, including implicit joins where no declared keys exist. That makes it useful for teams inheriting a warehouse they did not build.

Beyond visualization, Datascale is positioned as a writing and thinking surface. You can drag-select nodes or tables and chat with the AI about them without leaving the board, ask it to expand a rough idea into a system design or analytics workflow, turn notes into polished design docs and PRDs, and get feedback on architecture trade-offs such as normalization versus denormalization. Data lineage and downstream impact tracing help you see what a change would affect before you make it.

Boards can be made public and embedded elsewhere, which supports sharing architecture with stakeholders or writing public documentation. Pricing is per user with a 7-day free trial that does not require a credit card. The team behind it describes itself as engineers building tools for other engineers, and there are named customer references from analytics consultancies.

**Pricing & Plans:** Per-user subscription with a 7-day free trial and no credit card required. Monthly is $12 per user per month; annual billing drops it to $8 per user per month. Both tiers include unlimited team members, unlimited boards and wikis, data lineage and ER diagrams, the AI Copilot and shared AI conversations. An enterprise/education discussion path is also referenced via a sales contact.

- **SQL reverse engineering:** Reads DDL, CTAS, views and INSERT INTO statements to rebuild your data architecture without migrations or framework changes.
- **Implicit join detection:** Surfaces primary and foreign key relationships even when no keys are declared, revealing the true structure of your data.
- **On-canvas AI copilot:** Drag-select nodes and tables, then ask questions, refactor models or co-design in context without switching tools.
- **Data lineage and impact analysis:** Maps dependencies and cross-table relationships grounded in your actual SQL so you can trace downstream effects of a change.
- **Docs, wikis and flowcharts in one place:** Combine diagrams, notes and flowcharts on a single canvas and turn rough ideas into design docs, PRDs and data specs.

**Strengths:** Rebuilds lineage and ER diagrams from SQL you already have, with no migrations; Detects implicit joins that tools relying on declared keys would miss; AI chat works directly on selected canvas nodes rather than in a separate window; Boards can be published and embedded, which helps with stakeholder communication

**Limitations:** Focused on SQL-based stacks; it is not a general-purpose diagramming suite; Smaller product and team than established BI catalog vendors, so governance features are lighter; The public pricing FAQ entries appear partially unfinished on the site

[Profile (Markdown)](https://openaitools.app/tool/sql-notes.md)

## Frequently Asked Questions

### How do Dust and Datascale compare in monthly traffic?

Dust has 547,217 monthly visits, while Datascale has 1,304. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Dust has a month-over-month growth rate of +1.8%; Datascale has +9.4%.

### How does pricing compare?

Dust: Contact for Pricing. Contact for pricing. Dust offers a free trial after signup, but its plans are oriented toward teams and enterprises with usage-based credits, volume pricing and dedicated support; you need to talk to sales for exact figures. Datascale: Free Trial. Per-user subscription with a 7-day free trial and no credit card required. Monthly is $12 per user per month; annual billing drops it to $8 per user per month. Both tiers include unlimited team members, unlimited boards and wikis, data lineage and ER diagrams, the AI Copilot and shared AI conversations. An enterprise/education discussion path is also referenced via a sales contact.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | Provides a unified connected workspace for team documents and databases, but lacks Dust's advanced multi-source enterprise knowledge indexing. | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Salesforce Agentforce (formerly Einstein)](https://openaitools.app/tool/salesforce-einstein) | 111,910,743 | +2.2% | Freemium | Integrates deeply with CRM ecosystems to automate enterprise workflows, but is tightly bound to the Salesforce software suite. | [.md](https://openaitools.app/tool/salesforce-einstein.md) |
| 3 | [Glean](https://openaitools.app/tool/glean) | 3,301,875 | +0.1% | Contact for Pricing | Offers robust enterprise-wide permission-aware search and knowledge management, but with less focus on custom multi-model agent workflows. | [.md](https://openaitools.app/tool/glean.md) |
| 4 | [Abacus.AI](https://openaitools.app/tool/abacus-ai) | 3,278,636 | -1.7% | Contact for Pricing | Combines multi-model chat assistants and automated machine learning optimization, but with a heavier focus on data science pipelines. | [.md](https://openaitools.app/tool/abacus-ai.md) |
| 5 | [Taskade](https://openaitools.app/tool/taskade) | 539,714 | -9.4% | Freemium | Turn one prompt into live business apps, AI agents and automated workflows | [.md](https://openaitools.app/tool/taskade.md) |
| 6 | [Eraser](https://openaitools.app/tool/eraser) | 534,396 | -23.6% | Freemium, Paid, Contact for Pricing | AI diagrams and technical design docs built from your code and systems | [.md](https://openaitools.app/tool/eraser.md) |
| 7 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Excels at visual no-code web scraping and data automation pipelines, but lacks Dust's dedicated internal knowledge base architecture. | [.md](https://openaitools.app/tool/gumloop.md) |
| 8 | [Tines](https://openaitools.app/tool/tines) | 273,260 | +24.4% | Freemium, Contact for Pricing | Focuses on secure enterprise workflow automation and security orchestration, but requires more manual setup for custom AI agents. | [.md](https://openaitools.app/tool/tines.md) |
| 9 | [Kore.ai](https://openaitools.app/tool/kore-ai) | 143,328 | -18.2% | Contact for Pricing | Delivers enterprise-grade conversational AI and workflow automation, but focuses more heavily on customer service and enterprise contact centers. | [.md](https://openaitools.app/tool/kore-ai.md) |
| 10 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) |

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Source: https://openaitools.app/compare/dust-vs-sql-notes

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