# Dify vs Ouro

Dify records 819,691 monthly visits with -15% month-over-month growth and Freemium pricing. Ouro records 3,971 monthly visits with -15.7% growth and Freemium pricing.

| Metric | [Dify](https://openaitools.app/tool/dify-ai) | [Ouro](https://openaitools.app/tool/ouro) |
| :--- | :--- | :--- |
| Website URL | https://dify.ai | https://ouro.foundation |
| Categories | AI App Builder, AI Agent, AI Workflow, AI API | AI Agent, AI API, AI Assistant, AI Developer Tools |
| Pricing Model | Freemium | Freemium |
| Monthly Visits | 819,691 | 3,971 |
| MoM Growth Rate | -15% | -15.7% |
| Primary Market | China | United States |
| Direct Visits | 470,604 | 1,570 |
| Review Score | 4.8 | 4.2 |
| Saved Count | 22 | 0 |
| Supported Platforms | Website | Website |

## Dify

Dify pairs a visual canvas with code-level control so both technical and non-technical teams can assemble AI applications and agents. Workflow Studio turns prompt logic into an inspectable execution graph of building blocks: retrieval, branching, tool calls, human-in-the-loop steps, and model calls. Agent builder lets you create agents by chatting or by configuring them directly, then run them as a standalone app, embed them into a website, or reuse them as a node inside a larger workflow.

A major part of the platform is context engineering. The Knowledge Pipeline lets you define repeatable ingestion flows for files, websites, online documents, and cloud drives, handling extraction, cleaning, chunking, indexing, and retrieval testing before a knowledge base is attached to agents or workflows. The Marketplace supplies model providers, tools, data sources, and MCP integrations that teams can install once and then reuse across many apps as approved plugins.

Dify ships in three deployment shapes: Dify Cloud (fully managed SaaS with workspace, logs, and scaling handled for you), Dify Enterprise (self-hosted or VPC deployment with SSO/SAML, RBAC, audit logs, Helm charts for Kubernetes, and enterprise support), and the open-source Community Edition, a source-available single-command Docker deployment with a large GitHub community. Published apps can be delivered as hosted web apps, API endpoints, embeds, or MCP-compatible tools, with logs, feedback, annotations, latency, and usage analytics to keep improving them after launch.

**Pricing & Plans:** Freemium model with a free cloud sandbox and free trial credits, paid Cloud plans that scale with team size, storage, logs, and usage, and a free open-source Community Edition. Dify Enterprise is quoted per deployment via sales.

- **Workflow Studio:** A visual canvas where retrieval, branching, tool calls, human review, and model steps form a visible, testable execution path you can debug and hand off.
- **Agent Builder:** Create agents through chat or manual configuration, give them skills, tools, and knowledge, then ship them as apps, embeds, or reusable nodes inside workflows.
- **Knowledge Pipeline:** Build repeatable ingestion pipelines for documents, sites, and drives, with extraction, cleaning, chunking, indexing, and retrieval testing before you go live.
- **Plugin Marketplace and MCP:** Install model providers, tools, data sources, and MCP integrations once and reuse approved plugins across every app in the workspace.
- **Flexible Deployment and Observability:** Run on managed Cloud, in your VPC, or self-hosted with Docker, and monitor published apps through logs, annotations, feedback, latency, and usage data.

**Strengths:** Combines a visual builder with code-level extensibility in one product; Three deployment options, including a free self-hosted Community Edition; Strong retrieval and knowledge pipeline tooling for RAG applications; Large plugin marketplace plus MCP support reduces integration work

**Limitations:** Self-hosted deployments require real infrastructure and maintenance effort; Complex multi-step workflows take time to learn and tune; Advanced enterprise governance features sit behind a sales-quoted plan

[Profile (Markdown)](https://openaitools.app/tool/dify-ai.md)

## Ouro

Ouro reframes scientific publishing around reusable assets rather than one-way papers. Researchers and autonomous agents publish structures, datasets, and models, and anyone can call those models as an API, log the run, and publish a result that links back to every input it used. The platform hosts open research teams organized around concrete problems, from rare-earth-free permanent magnets to superconductors, thermoelectrics, solid-state batteries, and 2D materials, with thousands of assets already published.

The workbench is deliberately practical. A browser sandbox lets you run a live model without a cluster or install, every run is recorded with its inputs and outputs, and results are written up in the open so the next person can pick up where you stopped. Contributions are credited through the same asset graph that drives discovery.

The commercial model is equally direct: publishing is free, private assets are supported, and Ouro only takes a cut when assets are sold. Authors price their own datasets and paid API routes, and can bill in USD or Bitcoin. Organizations add private workspaces for labs and companies that want to work internally before going public.

**Pricing & Plans:** Free to join and free to publish public work, with 1 GB storage, 100 assets, and 20 datasets. Gold is $10/month or $100/year for 100 GB storage, 10,000 assets, and 2,000 datasets. Organizations cost $5 per seat per month with a 2-seat minimum, including 50 GB of shared storage. Ouro charges a 30% fee only on sales of paid datasets, files, posts, and API calls; free assets are never charged.

- **Composable research assets:** Structures, datasets, models, and write-ups are published as first-class assets that other people and agents can build on, with the full dependency chain visible.
- **Run models as APIs:** Call a published model directly from the Ouro workbench or programmatically, log what went in and what came out, and keep the run attached to your result.
- **Open team challenges:** Join or create a team around a single scientific problem, such as finding a strong magnet without rare earths, and submit scored entries.
- **Author-set monetization:** Price your own datasets, files, posts, and API routes. Buyers pay by card or in Bitcoin, and Ouro takes a 30% cut only when something sells.
- **Agent-ready infrastructure:** MCP support, documented APIs, and asset graphs designed so AI agents can publish, query, and run work alongside human researchers.

**Strengths:** Free to publish, join teams, and run free API routes, with only a 30% cut on paid sales; Every result is logged with its inputs, so reproducibility is built into the workflow rather than bolted on; Model-as-API design means published work gets reused instead of just cited; Organizations support internal, private collaboration before public release

**Limitations:** The 30% platform fee on sales is high compared with many developer marketplaces; The open-team model skews toward computational and materials-science domains; Researchers outside that space may find fewer ready-made assets and challenges to join

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

## Frequently Asked Questions

### How do Dify and Ouro compare in monthly traffic?

Dify has 819,691 monthly visits, while Ouro has 3,971. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Dify has a month-over-month growth rate of -15%; Ouro has -15.7%.

### How does pricing compare?

Dify: Freemium. Freemium model with a free cloud sandbox and free trial credits, paid Cloud plans that scale with team size, storage, logs, and usage, and a free open-source Community Edition. Dify Enterprise is quoted per deployment via sales. Ouro: Freemium. Free to join and free to publish public work, with 1 GB storage, 100 assets, and 20 datasets. Gold is $10/month or $100/year for 100 GB storage, 10,000 assets, and 2,000 datasets. Organizations cost $5 per seat per month with a 2-seat minimum, including 50 GB of shared storage. Ouro charges a 30% fee only on sales of paid datasets, files, posts, and API calls; free assets are never charged.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 2 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 3 | [Abacus.AI](https://openaitools.app/tool/abacus-ai) | 3,278,636 | -1.7% | Contact for Pricing | An all-in-one AI super assistant for professionals and enterprises | [.md](https://openaitools.app/tool/abacus-ai.md) |
| 4 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | No-code builder for custom AI agents and workflows | [.md](https://openaitools.app/tool/mindstudio.md) |
| 5 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | A self-hosted chat interface for running local LLMs, though lighter on complex visual workflow orchestration. | [.md](https://openaitools.app/tool/open-webui.md) |
| 6 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | The environments frontier AI trains on, and the agents that do the recurring work | [.md](https://openaitools.app/tool/labelbox.md) |
| 7 | [Eigent AI](https://openaitools.app/tool/eigent-ai) | 575,703 | +1.4% | Freemium, Free Trial, Contact for Pricing | Open-source desktop AI workforce that runs browser, file, and app tasks for you | [.md](https://openaitools.app/tool/eigent-ai.md) |
| 8 | [LlamaIndex](https://openaitools.app/tool/llamaindex) | 387,910 | +19.9% | Freemium, Contact for Pricing | Provides advanced programmatic frameworks for document parsing and RAG pipelines, but requires coding instead of visual canvases. | [.md](https://openaitools.app/tool/llamaindex.md) |
| 9 | [Activepieces](https://openaitools.app/tool/activepieces) | 258,553 | +1.5% | Freemium | An open-source automation platform combining visual flows with AI steps, though geared more towards general app integration. | [.md](https://openaitools.app/tool/activepieces.md) |
| 10 | [Relevance AI](https://openaitools.app/tool/relevance-ai) | 192,083 | -15.5% | Free Trial, Contact for Pricing | Built for scaling multi-agent workforces and B2B task automation, though less focused on open-source RAG pipelines. | [.md](https://openaitools.app/tool/relevance-ai.md) |

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Source: https://openaitools.app/compare/dify-ai-vs-ouro

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