Layered Architecture for Enterprise AI Applications: Responsibilities of the Infrastructure Layer / Platform Layer / Application Layer / User Layer
Enterprise AI should not be understood as “one LLM + one chat box.” A more robust approach is to design by layers.
This content can be retained, but it should be made clear that it belongs to a “methodology article supportable by public sources” rather than an official Dify architecture white paper. While public sources do not provide a standard Dify four-layer architecture diagram, they provide sufficient basis for layering: on one hand, Enterprise documentation clearly covers infrastructure elements such as databases, Redis, object storage, vector databases, Ingress, licensing, and resource specifications; on the other hand, Dify’s public features cover model integration, knowledge bases, Workflow, Agent, API, and application publishing. Therefore, dividing enterprise AI into infrastructure layer / platform layer / application layer / user layer is a reasonable abstraction supported by public sources.
1. Layering Basis Confirmed by Public Sources
1. Infrastructure and Platform Capabilities Are Already Separate in Public Documentation
Enterprise deployment documentation extensively discusses infrastructure: Kubernetes, storage, databases, Redis, vector databases, Ingress, and resource specifications. Meanwhile, Dify product documentation discusses models, knowledge bases, Workflow, Agent, and applications. This naturally forms the distinction between the “infrastructure layer” and the “platform layer.”
2. Dify Is Better Positioned as the Platform Layer, Not the Final Business Layer
From the public product structure, Dify itself is not a single fixed business application but a construction platform capable of supporting multiple business applications. Therefore, placing it in the platform layer of an enterprise AI layered architecture is a conclusion supported by public sources.
3. API and Publishing Capabilities Indicate an Independent User Access Layer Exists
Dify publicly supports web usage, API calls, application publishing, and external integrations. This shows that the end-user entry point is not the same as the platform itself, making it reasonable to have a separate user layer.
2. Infrastructure Layer
Responsible for compute, storage, networking, security, databases, object storage, and vector databases.
3. Platform Layer
Responsible for model integration, knowledge bases, Workflow, Agent, permissions, logging, and governance. Dify most closely corresponds to this layer.
4. Application Layer
Responsible for specific business applications, such as contract assistants, FAQ systems, approval draft generators, and document processing pipelines.
5. User Layer
Responsible for actual usage entry points, including web, internal systems, API access, and mobile.
6. Conclusion
The value of layering is that infrastructure teams, platform teams, business teams, and end users do not have to compete within the same abstraction layer.
Public Source References
note.com
- No particularly strong direct hits on note.com at this time. Current evidence is better drawn from the structural differences between Dify’s product layer and Enterprise deployment layer public documentation.
zenn.dev / Official Documentation / Other Public Sources
- Overview - Dify Docs | https://docs.dify.ai/en/use-dify/workspace/readme
- Dify Helm Chart - Dify Enterprise Docs | https://enterprise-docs.dify.ai/en-us/deployment/advanced-configuration/dify-helm-chart
- Kubernetes - Dify Enterprise Docs | https://enterprise-docs.dify.ai/en-us/deployment/prerequisites/kubernetes
- API 基づく開発 - Dify Docs | https://docs.dify.ai/versions/legacy/ja/user-guide/application-publishing/developing-with-apis
Verified Information from Public Sources for This Article
- Public documentation naturally distinguishes infrastructure concerns from platform functionality concerns
- Dify more closely corresponds to the platform layer in enterprise AI layering, rather than a single business application
- This article can be retained as a public source-based methodology piece