Dify vs Make: which no-code AI platform actually gets your LLM workflows cited by Perplexity's sources

Dify excels at building conversational AI agents and RAG applications with its prompt orchestration canvas, while Make specializes in connecting APIs and automating multi-step business workflows with conditional logic. Neither platform is specifically optimized for citation by Perplexity or other AI search engines, but both let you build LLM workflows that can be deployed as APIs or web apps that may eventually become citable sources if they publish valuable, structured content.

What is Dify best at?

Dify is a platform for building and deploying LLM applications with a visual prompt orchestration interface. It shines when you need to create chatbots, AI agents, or RAG (retrieval-augmented generation) systems that pull from your own knowledge base.

The platform includes a built-in vector database for document ingestion. You can upload PDFs, markdown files, or sync from Notion to create a searchable knowledge base that your LLM can query. This makes it particularly strong for customer support bots or internal documentation assistants.

Dify's canvas lets you chain multiple LLM calls together with conditional logic. You can build multi-step reasoning workflows where one model's output feeds into another. The platform supports over 20 LLM providers including OpenAI, Anthropic, and local models through Ollama.

The self-hosted version is free and open source. Cloud pricing starts at $59 per month for the team plan with higher usage limits.

What is Make best at?

Make (formerly Integromat) automates workflows by connecting different apps and services through a visual scenario builder. It excels at moving data between systems, triggering actions based on events, and orchestrating complex business processes.

The platform connects to over 1,500 apps through pre-built modules. You can watch for new Airtable records, send Slack messages, update Google Sheets, and call any REST API. Scenarios can include branching logic, error handling, and data transformation.

Make added OpenAI modules in 2023, letting you call GPT models within your automations. But it's not an AI-native platform. You're bolting LLM calls onto traditional workflow automation rather than building around the LLM as the core component.

Pricing starts with a free tier (1,000 operations per month), then $9 per month for the Core plan with 10,000 operations. Most teams doing serious LLM work end up on higher tiers at $16 to $29 per month.

Can either platform help you get cited by Perplexity?

Neither Dify nor Make directly influences whether AI search engines cite your content. Citation depends on publishing authoritative, well-structured information that becomes part of the indexed web.

Both platforms let you build applications that could eventually become citable sources. With Dify, you might create a public-facing AI assistant that answers domain-specific questions and gets indexed. With Make, you might automate publishing content to your blog or documentation site.

Getting cited requires publishing structured, factual content that search engines can crawl. The workflow tool you use is irrelevant. What matters is the quality and structure of what you publish.

If your goal is specifically citation visibility, focus on creating markdown documentation, structured data, and authoritative content pages. Both platforms can help automate that publishing process, but neither is designed for SEO or citation optimization.

How do the visual builders compare?

Dify uses a node-based canvas where each node represents a step in your LLM workflow. You connect prompt templates, knowledge base queries, code execution blocks, and model calls with visual links. The interface feels similar to LangFlow or Flowise if you've used those tools.

Make's scenario builder shows modules (action blocks) connected in sequence. Each module represents an API call or data operation. The interface is more polished than Dify's but also more complex due to the sheer number of integration options.

Dify's builder is simpler for AI-specific tasks but less flexible for general automation. You can't easily watch for Stripe payments or send SMS messages. Make's builder handles that naturally but requires more setup for LLM chaining and prompt management.

Both platforms support variables, conditional branching, and error handling. Dify includes built-in debugging for LLM outputs with token usage tracking. Make provides detailed execution logs showing exactly what data passed between each module.

When should you choose Dify?

Choose Dify if you're building an AI application as your primary deliverable. It's designed for teams creating chatbots, AI agents, content generators, or internal tools powered by LLMs.

Pick Dify when you need a built-in knowledge base. If your use case involves RAG (asking questions about your own documents), Dify handles document ingestion and vector search out of the box. You don't need to connect Pinecone or Weaviate separately.

Self-hosting is a key reason to choose Dify. The open-source version runs on your infrastructure with Docker, giving you full control over data and no per-usage fees beyond your LLM API costs.

If you're a product team building an AI feature into your app, Dify provides the fastest path from prototype to production. The platform includes API endpoints and embeddable widgets for deploying your agents.

Dify works well for solopreneurs and small teams (2-5 people) who want to launch AI products quickly without building infrastructure from scratch.

When should you choose Make?

Choose Make if AI is one component in a broader automation strategy. The platform excels when you need to connect LLMs to your existing business tools and databases.

Pick Make when your workflows span many different services. If you're building something like "when a new support ticket arrives, summarize it with GPT-4, check our knowledge base in Notion, and post to Slack if urgent", Make handles that naturally.

Operations and growth teams typically benefit more from Make than from Dify. These teams already use dozens of SaaS tools and need to automate repetitive tasks that may or may not involve AI.

If you're not building an AI product but want to add AI capabilities to your existing workflows, Make is the pragmatic choice. You can start with simple automations and add LLM steps where they provide value.

Make's extensive template library (over 10,000 pre-built scenarios) means you can often find a starting point for common workflows and customize from there.

What about other platforms like Kotopost?

Kotopost focuses specifically on content operations and publication workflows for teams managing blogs, documentation, and marketing content. It's designed for content teams who want to coordinate writing, editing, approval, and publishing in one place.

Unlike Dify or Make, Kotopost is not a general-purpose AI workflow builder. It does include AI writing assistance and can help automate content production, but it's purpose-built for the content lifecycle rather than arbitrary LLM workflows.

If your primary goal is managing a content publication pipeline, Kotopost makes sense. If you're building custom AI applications or automating diverse business processes, Dify or Make are better fits.

The platform works well for content teams of 3-