The low-code AI landscape has consolidated around a few platforms that teams actually ship with. Dify, n8n, and Coze show up repeatedly in client conversations and in our own evaluation. Each targets a different primary use case: Dify for LLM-powered applications and RAG, n8n for general workflow automation, Coze for conversational AI and chatbots. The overlap is real—all three can build workflows, integrate with LLMs, and connect to external services—but the fit depends on what you're building.
Through building AI applications for clients and evaluating these platforms, we've landed on a simple heuristic: match the platform to the dominant workload. Here's what we've learned.
How we got here
We needed to recommend a platform for a client building an internal knowledge-base chatbot. The requirements were straightforward: ingest documents, answer questions with RAG, and optionally trigger actions (create tickets, send notifications). We evaluated Dify, n8n, Coze, and a few others. Dify won for that use case—native RAG, built-in evaluation, and a workflow canvas that made sense for LLM apps. But we've since used n8n for integration-heavy automations and Coze for customer-facing chatbots. The right choice depends on the job.
Dify: LLM applications and RAG
Dify is built for AI-native applications. It has first-class support for RAG: document ingestion, chunking, embedding, and retrieval are built in. You configure a knowledge base, connect an LLM, and get a working QA system without writing code. The workflow canvas supports chains, agents, and conditional logic. For internal tools—FAQ bots, document analysis, knowledge assistants—Dify is often the fastest path to production.
We've found Dify strong for knowledge-powered use cases. Internal docs, support KBs, technical documentation. The observability and evaluation tools help teams iterate: you can see which queries succeed, which fail, and how to improve retrieval. The tradeoff: Dify is less flexible for non-LLM workflows. If your primary need is connecting SaaS tools, moving data between systems, or orchestrating APIs without an LLM in the loop, Dify can do it, but it's not optimized for that. The mental model is "LLM app with optional integrations," not "workflow automation with optional LLM."
Dify is open source and can be self-hosted. The cloud offering simplifies ops. For teams that want to own their data and infrastructure, self-hosting is viable.
n8n: workflow automation first
n8n is a general-purpose workflow automation platform. Think Zapier or Make, but open source and self-hostable. It has hundreds of integration nodes—CRMs, databases, APIs, file systems. The strength is connecting things: when a form is submitted, update a spreadsheet, notify Slack, and optionally call an LLM to summarize. The LLM is one node among many; the workflow is the center.
We've used n8n for operations automation: HR onboarding flows, sales pipeline updates, finance reconciliation triggers. For these, the LLM is sometimes useful (e.g., summarizing an email before creating a ticket) but not the core. The core is "when X happens, do Y and Z." n8n excels at that. It also supports custom JavaScript, so you can extend nodes when the built-in ones fall short.
The tradeoff: n8n's AI capabilities are additive, not native. RAG, evaluation, and agent tooling are less mature than in Dify. If your primary goal is an LLM application with RAG, n8n can do it, but you'll spend more time wiring things together. If your primary goal is automation with optional LLM steps, n8n is a strong fit.
n8n is open source. Self-hosting is common; the cloud option exists for teams that prefer managed ops.
Coze: conversational AI and chatbots
Coze (formerly Bot Framework in some regions) is built for conversational AI. Chatbots, virtual assistants, and multi-turn dialogue. The focus is on building a bot, deploying it to multiple channels (web, Slack, Discord, etc.), and managing conversations. The workflow model is conversation-centric: intents, slots, and dialogue flows.
We've seen Coze used effectively for customer support bots, lead qualification, and internal Q&A assistants that need a chat interface. The no-code/low-code experience is polished. Non-technical teams can build and iterate on bots without touching code. The integration with LLMs is straightforward; you configure a model, add knowledge or tools, and deploy.
The tradeoff: Coze is less suited for heavy data workflows or complex RAG. Document ingestion and retrieval exist, but Dify's RAG tooling is more mature. For pure automation—no conversation—n8n is a better fit. Coze shines when the primary interaction is chat and the bot needs to feel natural across channels.
Coze has both a Chinese and international presence. Deployment options vary by region. Worth checking availability and compliance for your use case.
How to choose
We use a simple decision tree. If the primary workload is knowledge retrieval and LLM-powered Q&A—internal docs, support KBs, document analysis—start with Dify. If the primary workload is automation and integration—connecting SaaS tools, moving data, triggering actions—start with n8n. If the primary workload is conversational AI—customer-facing chatbots, virtual assistants, multi-channel deployment—start with Coze.
The boundaries aren't sharp. Dify can do automation; n8n can do RAG; Coze can do workflows. But each platform optimizes for a different default. Choosing the one that matches your dominant use case reduces friction.
For hybrid cases—e.g., a chatbot that also needs to run heavy integrations—you may need to combine platforms. We've seen n8n handle the integration layer and call into a Dify app for the LLM part. Or Coze for the chat interface with webhooks to n8n for backend actions. The ecosystems are interoperable.
What we recommend
Match the platform to the primary workload. Don't force Dify into a pure automation problem or n8n into a pure RAG problem. You'll fight the tool.
Consider self-hosting. All three support it. If data residency or control matters, self-hosting is viable. Factor in ops burden: Dify and n8n have active communities; Coze's self-host story varies by product line.
Start small, validate, then scale. Build a minimal version of your use case. See how the platform feels. Iterate before committing to a large migration.
Plan for the next phase. These platforms are evolving quickly. Dify is adding more agent capabilities; n8n is deepening AI nodes; Coze is expanding channels. Choose based on current fit, but keep an eye on the roadmap.
At the margins, Dify, n8n, and Coze each fill a distinct niche. Dify for LLM apps and RAG, n8n for workflow automation, Coze for conversational AI. The right choice depends on what you're building. Match the platform to the workload, and you'll move faster.