AACFlow против Dust

AACFlow is an AI workspace where teams build, deploy, and manage AI agents visually, conversationally, or with code. Dust is an enterprise AI agent platform where teams build no-code agents connected to company data and tools in a shared, multiplayer workspace, then deploy them to chat, Slack, and other surfaces. Teams considering a switch typically weigh pricing model, and how AI-native the platform's agent-building experience is.

AACFlow is built specifically as an AI agent workspace, with native multi-LLM support, an in-editor AI Copilot, and a knowledge base with hybrid vector + keyword search. Dust's standout capability is zero visual/flow layer by design, building only through forms, text, and conversation: Dust's Agent Builder is entirely form and text based, name, description, instructions, model, tools, knowledge, guided by a conversational 'Sidekick' assistant, with no visual canvas at all (its earlier block-based 'Dust Apps' product is deprecated). Agents deploy natively into a shared, multiplayer workspace and out to Slack, Teams, and other chat surfaces. A team that wants agents assembled purely from plain-language instructions and templates, with no drag-and-drop layer to learn or maintain, gets that directly. Teams that do want infrastructure-as-code can also define Skills and agent configurations as files in a Git repository and sync them via an official GitHub Action, with the same PR review and rollback workflow as application code. One documented limitation of Dust is no visual, node-based canvas: agents are configured through forms and text: Dust's agent builder is a form-based, instruction-driven interface (name, description, instructions, model, tools, knowledge), not a drag-and-drop node/flow canvas. Its earlier block-based visual orchestration product, 'Dust Apps', is deprecated: only apps created before October 2025 remain accessible, and creating new ones is disabled.

AACFlow is a managed cloud service operated by AACFlow. Dust's deployment options: Multi-tenant hosted cloud by default; an EU data-hosting option for databases, files, and vectors is available, documented as exclusive to Enterprise customers rather than a self-serve region choice on every tier.

AACFlow uses credit-based usage billing (Stripe), with bring-your-own-key exemption from metered caps. Dust uses per-seat subscription with a monthly AI-usage credit allocation per seat.

Security is a like-for-like comparison, not a one-line verdict. AACFlow: none claimed. Dust: GDPR compliant, HIPAA-capable, SOC 2 Type II; no ISO 27001, PCI, or FedRAMP. Check the Security & compliance rows above for the full breakdown, including SSO, audit logging, and data residency.

AACFlow: 21 provider integrations (OpenAI, Anthropic, Google/Gemini, Azure OpenAI, Azure Anthropic, Groq, Cerebras, Mistral, xAI, Bedrock, Vertex, Ollama, OpenRouter, and more), with OpenRouter, LiteLLM, vLLM, and Ollama resolving models dynamically at runtime rather than from a fixed list, so effective model reach extends well beyond the 21 named providers. Dust: agents can be configured with a choice of model (e. AACFlow also ships native human-in-the-loop approvals, a hybrid vector-plus-keyword knowledge base, and an in-editor AI Copilot that can read execution logs and directly edit the workflow to fix a failed run.

Documented limitations of Dust include no visual, node-based canvas: agents are configured through forms and text: Dust's agent builder is a form-based, instruction-driven interface (name, description, instructions, model, tools, knowledge), not a drag-and-drop node/flow canvas. Its earlier block-based visual orchestration product, 'Dust Apps', is deprecated: only apps created before October 2025 remain accessible, and creating new ones is disabled.; no dedicated pre-deployment evaluation/dataset-testing framework, a gap shared with most agent builders: Dust explicitly says it is 'not a pre-deployment evaluation platform': dataset-based regression testing belongs in CI/CD pipelines and specialized testing tools, and Dust builds observability signals into the agent-builder workflow instead of a formal eval-suite feature. This is a gap most agent builders share, including AACFlow, whose own Evaluator and Guardrails blocks are per-call scoring/validation primitives rather than a batch golden-dataset eval-suite runner.; self-hosting is not a supported deployment path despite an MIT-licensed core: The core dust-tt/dust repository is MIT-licensed on GitHub, but Dust is sold and operated only as hosted SaaS. There is no documented, supported way to self-host a production Dust workspace on customer infrastructure.; no native spreadsheet-like data table; structured data is queried, not edited: Dust's Query Tables tool lets an agent generate and run SQL over structured sources (CSVs, Notion databases, Google Sheets, Snowflake, BigQuery), but Dust has no native, editable spreadsheet-grid feature with arrow-key navigation and copy-paste, unlike a dedicated data-table product.

There is no automated one-click migration tool between Dust and AACFlow. Workflows and automations need to be rebuilt in AACFlow's visual builder, natural-language Chat surface, or API. Most teams start by recreating their highest-value automation first to validate the switch before migrating the rest.