Pattern: LLM in backend

The classic way to build LLM-powered apps is to keep all the AI logic, prompts, and orchestration in the backend. This is the default for most teams, and for good reason: it's simple, secure, and puts all the sensitive stuff where you can control it.

See also: Pattern: LLM in frontend.

When to use it

Use the LLM-in-backend pattern when:

• Security, compliance, or data privacy are your top priorities and you need to keep all AI logic, prompts, and orchestration server-side.
• You want maximum control over API keys, rate limits, logging, and access control.
• Your business logic or AI flows are too sensitive or complex to expose to the client.
• Your frontend team isn't ready to own prompt engineering or AI orchestration.

Avoid this pattern if:

• You need to iterate rapidly on prompts, AI flows, or user experience, and want to empower frontend teams to experiment.
• Your product requires frequent experimentation or close collaboration between frontend and product teams.

LLM-in-backend is best for teams and products that value safety, centralized control, and strict management of sensitive operations over speed and flexibility.

How it works

The frontend is just a UI layer. It collects user input and sends it to the backend. The backend owns everything AI-related: prompt templates, orchestration, OpenAI SDK calls, and any business logic that needs to interact with the model. The backend talks to OpenAI directly, so API keys and secrets never leave your server.

Frontend                 Backend
+----------------+       +---------------------+
| UI components  | ----> | Prompts             |
+----------------+       | OpenAI SDK          | ----> OpenAI
                         +---------------------+

Advantages

• Security: All secrets, API keys, and business logic stay server-side. Nothing sensitive ever touches the client.
• Control: You can enforce rate limits, logging, and access control in one place.
• Consistency: All prompt engineering and AI flows are centralized, so you don't have to worry about version drift across clients.

Disadvantages

• Slower iteration: Every prompt tweak or AI logic change requires a backend deploy. This slows down experimentation and keeps frontend teams waiting.
• Bottlenecked frontend: Product teams can't quickly try new ideas or tune prompts; they're stuck behind backend release cycles.
• Heavy backend: The backend ends up doing a lot: business logic, AI orchestration, and all the glue code between your app and OpenAI.

Expanded architecture

Here's what a typical LLM-in-backend architecture looks like in practice:

Frontend                 Backend
+----------------+       +----------------------+
| UI components  | ----> | API server           |
+----------------+       |   Prompts & AI logic | --+
                         |   Business APIs      |   |
                         +----------------------+   |
                           OpenAI SDK <-------------+
                               |
                               v
                            OpenAI API

Summary

LLM-in-backend is ideal for teams that value safety, centralized control, and strict management of sensitive operations over speed and flexibility. If you need to keep everything confidential and tightly managed, this is the pattern to use.