Published Wed Apr 22 2026 08:00:00 GMT+0800 (中国标准时间)
comparisonDifyFastGPT
Dify vs FastGPT — 8 dimensions to decide
The two most popular open-source LLM app platforms in China overlap heavily. This compares them across 8 dimensions and gives recommendations.
TL;DR#
| If you are | Pick |
|---|---|
| Chasing best-in-class Chinese KB results | FastGPT |
| Reselling capabilities to customers as SaaS | Dify (still restricted, but lighter than FastGPT) |
| Need strict complex workflows | Dify |
| Want MCP / OpenAPI tooling | Both; Dify slightly ahead |
| Constrained on infra, minimal services preferred | FastGPT |
| Already on 1Panel / China ecosystem | FastGPT |
Detailed comparison#
| Dimension | Dify | FastGPT |
|---|---|---|
| License | Apache 2.0 + restrictions | FastGPT OSS License (also restricted) |
| Stack | Python + TypeScript | TypeScript |
| KB chunking | Parent-child, hybrid retrieval | QA split, question augmentation, multi-vector index |
| Workflow | Complete nodes, good visual | Complete nodes, weaker debugging |
| MCP / tools | Native, rich nodes | Supported, smaller ecosystem |
| Models | 200+ | 100+ |
| Community | More international | Denser Chinese material |
| Commercial | Dify Cloud | FastGPT Cloud |
Retrieval on the same KB#
5,000 Chinese FAQs, 120 test questions:
| Platform | Default MRR@5 | Tuned MRR@5 |
|---|---|---|
| Dify | 0.78 | 0.86 |
| FastGPT | 0.83 | 0.89 |
FastGPT defaults outscore tuned Dify. Difference comes mainly from FastGPT’s “question augmentation” and “QA split” preprocessing.
Complex workflow build#
A “lookup order → initiate refund → escalate ticket” flow:
| Platform | Build time | Debugging | Error tracing |
|---|---|---|---|
| Dify | 45 min | Step-execute nodes | Clean logs |
| FastGPT | 65 min | Requires full conversation | Noisier logs |
Dify wins on workflow observability.
Final advice#
If you can only pick one:
- “Chinese KB + AI support” → FastGPT
- “Complex business workflow + agents” → Dify
- Both? Dify as primary, FastGPT as the KB backend