Dify vs Langflow vs FastGPT

These three low-code LLM platforms are often listed together but they are not interchangeable. Each was built around a different core assumption about what users want to do, and picking wrong means you fight the tool for a month before realising.

Dify is the most opinionated about production. It ships as a full LLM application platform — a visual workflow studio, a built-in RAG pipeline, agent tooling, prompt versioning, evaluation, and an observability layer — all wired together. You create an 'app' (chatbot, workflow, agent, or text generator), configure it in the web UI, and expose it via a generated API endpoint. The RAG engine is a standout: upload PDFs, plug in Notion or a web source, and Dify handles chunking, embedding, retrieval, and re-ranking without you touching a line of code. Dify's variable system (variables can store files, user inputs, component outputs, and flow across nodes) is powerful but takes a session to click.

Langflow takes the opposite philosophy. It is a visual canvas for chaining AI components — retrieval, prompts, tools, agents — with a live chat pane so you can test while you build. Under the hood it leans on LangChain's component library, which means almost anything in the LangChain ecosystem (new vector DB, new model provider, new parser) shows up in Langflow quickly. The license is Apache 2.0 with no usage restrictions, so you can embed Langflow-built flows into your own SaaS without compliance concerns. The tradeoff is that Langflow is deliberately a workbench, not an application — observability, auth, versioning, and deployment are your problem.

FastGPT is the most use-case-focused of the three. It is optimised for one job: building enterprise knowledge-base assistants. The ingestion pipeline is notably strong — FastGPT handles messy PDFs, Word documents, Markdown, and web sources with preprocessing that catches layout issues most tools miss. It offers a visual Flow module for complex Q&A logic (routing between documents, multi-turn disambiguation, follow-up actions) that is more mature than Dify's for pure knowledge-base work. Where FastGPT falls short is agent breadth: if you want an assistant that books meetings, queries APIs, and reasons across tools, FastGPT was not designed for that — Dify is.

Where each one wins cleanly

Production polish: Dify. The combination of evaluation, observability, prompt versioning, and team features is noticeably ahead of the other two. If you are shipping a customer-facing LLM app and care about debugging production behaviour, Dify is where to start.

Open-source freedom: Langflow. Apache 2.0 means you can build a SaaS on top and sell it. Dify's source-available license explicitly forbids multi-tenant SaaS use, which is a hard stop for many commercial scenarios. FastGPT's license is more permissive than Dify but less battle-tested in litigation.

Ingestion quality: FastGPT. The document preprocessing (table extraction, header detection, layout-aware chunking) beats Dify's default ingest on complex PDFs. For an assistant over a repository of compliance documents, FastGPT typically produces better retrieval quality without tuning.

Agent capability: Dify. The workflow editor + tool integrations (HTTP request, code execution, many third-party connectors) make Dify the best of the three for building agents that do more than chat about documents.

Visual debugging: Dify and Langflow tie. Both show execution duration per node, input/output values, and clear errors inline. FastGPT's debugging is functional but less detailed.

The licensing trap

The most common mistake teams make is choosing Dify for a project that turns into a multi-tenant SaaS. Dify's license forbids this — you cannot run a hosted Dify instance that serves isolated tenants as a commercial product. If that is the endgame, Langflow is the only one of the three where the license supports it outright. This is not theoretical — several teams have had to rewrite flows into LangChain or Langflow late in the build because they did not read Dify's license terms.

The practical shape of each

A three-person startup building an internal AI assistant on company docs: FastGPT for the ingest, or Dify if agent tools matter.

A mid-sized company shipping an external AI feature inside its product: Dify if the feature is standalone, Langflow (or raw LangChain) if it needs to be deeply embedded.

A researcher prototyping an agent architecture: Langflow, because the canvas lets you iterate on structure visually and export as LangChain code.

A team building a multi-tenant AI SaaS: Langflow, or skip the low-code layer entirely and use LangGraph directly.

Dify is free for self-hosting under its source-available license (subject to the no-SaaS clause). The cloud-hosted Dify service has a free tier (200 messages/month on the Sandbox plan), a Professional tier at $59/month (5,000 messages, 200 knowledge docs), and a Team tier at $159/month (10,000 messages, 500 docs, workspace features). LLM API costs are separate — Dify routes to your OpenAI, Anthropic, or Gemini key, and those tokens are billed by the provider.

Langflow is free under Apache 2.0 for unlimited self-hosting. The hosted DataStax Astra Langflow service is free for evaluation with paid tiers for production workloads, but most users self-host on a small VPS ($20–50/month). LLM API costs are pass-through to your model provider.

FastGPT has an open-source edition that is free for self-hosting with full features. The FastGPT Cloud service is the commercial path, with a free Experience plan, a Pro plan at roughly $20/month per user, and Enterprise custom pricing. LLM API costs sit on top, as with the others.

Hidden cost flags: Dify's knowledge-base ingestion consumes embedding tokens on every upload and re-embed — for a few thousand documents this is a one-time $5–$50 hit; for a live document pipeline it is a running cost worth modelling. Langflow's total cost is the hardest to estimate because you build and operate everything yourself — add 10–20 hours of platform work per quarter for patching, monitoring, and auth. FastGPT's preprocessing pipeline uses an internal model for layout analysis that is bundled in the OSS edition but consumes compute on ingest.