---
title: "Unique Viewpoint | Dify.AI’s Zhang Luyu: LLM App Success Hinges on Resources & Market Beyond Tech"
author: "Unique Research"
sourcePublication: "Unique Research (FFCAP English library)"
originalPublishedAt: "2024-03-25T16:30:00+08:00"
canonical: "https://ffcap.cn/en/research/20240325a06d1o00"
source: "https://ffcap.cn/en/research/20240325a06d1o00"
language: "en"
cnSourceUrl: "https://view.inews.qq.com/a/20240325A06D1O00"
---

# Unique Viewpoint | Dify.AI’s Zhang Luyu: LLM App Success Hinges on Resources & Market Beyond Tech

_Unique Research · Unique Viewpoint · March 25, 2024_

## Background

On **January 24, 2024**, Unique Capital and partners held the **2024 AIGC Application Development Summit** in Shanghai (~**1,000** AI practitioners). Edited from the guest keynote dialogue.

## Guests

-   **Zhang Luyu** — Founder & CEO, Dify.AI
-   **Yin Zhi** — Chief Advisory Consultant, Shanghai AI Technology Association

## Bottlenecks for LLM app landing (IT stack view)

**Zhang:** Dify is a service-oriented open-source middleware to help enterprises and developers ship LLM apps — covering data and ops pain. Across many builders, the first question he asks is: _Who are you?_ Identity shapes scenarios and failure modes. Models are only part of an app — often <**50%**; the rest is product DNA, original customers, and accumulated resources. Common pattern: process apps = LLM + orchestration + legacy logic. Others are interaction- or data-centric. SaaS firms with clear user needs have a simpler path; uncertain experiments without understanding model properties tend to fail.

## What frameworks will the industry need in 2024?

**Zhang:** Early half-year was fragmented POC with OpenAI APIs/SDKs or LangChain glue. Paradigms are clarifying around two parts: (1) **RAG** pipelines — indexing, cleaning, query (kin to prior data mid-platforms); (2) **app orchestration** — logic + prompts + multi-size commercial/open models into multimodal apps (kin to RPA).

## Value of AI dev platforms (incl. Dify) for enterprises

**Zhang:** Exploration predated OpenAI’s GPT Store / Assistants-style surfaces by ~half a year. Value stack:

-   **Near-term:** fast, low-cost near-POC prototypes
-   **Learning:** prompt-first skills for traditional software teams
-   **Mid-term:** engineering convenience for complex pipelines, multi-source data, multi-model assembly and performance
-   **Long-term:** post-launch data collection, labeling, feedback to improve prompts and models — no AI app stays static

**Yin** notes prior “AI Four Dragons”-era full stacks already faced heavy engineering (data import, automation) before RAG/agents — new tools simplify some steps but add new complexity.

## Will Dify offer MaaS?

**Zhang:** No — MaaS (cloud train/infer) fits cloud vendors’ compute/scheduling strengths. Dify focuses on problems _after_ you have a model. Yin expects model choice to converge toward a few OS-like options (iOS/Android analogy).

## Ideal one-stop framework — who can build apps?

**Zhang:** Vision: make AI definable and usable by everyone — developer vs non-developer less binary. GPTs-class tools lower the floor for small experiments; professional/monetized products still need integration, UX, and domain skill. Analogy: small apps as easy as Excel formulas; real products still demand craft.

## Platform trends: ToC vs ToB

**Zhang:** ToC platforms may sit with mega traffic entrances or top model vendors (OpenAI-like standards). Enterprise platforms emphasize private data/security — e.g., agent platforms over internal data + tool APIs. Differentiation also by model size: small models → mobile-like frameworks; large models → internal AI mid-platforms.

## Big tech vs startups

**Zhang:** Startups win on speed and developer focus — Dify’s product (Jun 2023 launch) already >**16,000** GitHub stars; BAT/model vendors began copying similar surfaces. “Seat problem”: model vendors optimize for their models; clouds optimize for selling compute. Startups optimize for getting developers’ apps live.

**Yin:** Large firms have infrastructure flexibility — algorithms must eventually run on domestic hardware (e.g., Huawei stack).

## 2024: ToC fire or ToB scale?

**Zhang:** ToC is emergent — unpredictable creativity, hard to SOP; builder population may not massively expand. ToB: same app pattern can replicate across thousands of enterprises — many deployments, fewer categories. Different dynamics, both real.

_4 CN images; attach on ingest._

— END —

---

Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20240325a06d1o00
On-site reading page: https://ffcap.cn/en/research/20240325a06d1o00