𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗽𝗽𝘀 𝗪𝗶𝘁𝗵 𝗔 𝗠𝗼𝗱𝗲𝗹 𝗔𝗰𝗰𝗲𝘀𝘀 𝗟𝗮𝘆𝗲𝗿

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗽𝗽𝘀 𝗪𝗶𝘁𝗵 𝗔 𝗠𝗼𝗱𝗲𝗹 𝗔𝗰𝗰𝗲𝘀𝘀 𝗟𝗮𝘆𝗲𝗿

AI apps usually start with one model. You use one SDK. You use one model name. Your first version works.

Your product grows. Needs change. Some tasks need speed. Others need logic. Chatbots need low latency. Agents need tool use. Hardcoding these choices slows you down.

Your code often ends up with:

• Model names in many files.
• Provider formats in your logic.
• Hardcoded URLs.
• Duplicated retry logic.
• No way to compare models.

Use a model access layer. It is a boundary between your logic and your models. Your app sends requests to this layer. The layer handles the details.

The layer manages:

• Model selection.
• Request formats.
• Model switching.
• Usage tracking.
• Latency monitoring.
• Fallback behavior.

Different tasks need different models:

• Chatbots need speed.
• RAG apps need reasoning.
• Agents need tool reliability.
• Workflows need structured output.

Keep your product logic separate from your model access. This gives you room to grow.

Source: https://dev.to/ye_allen_/building-ai-apps-with-a-model-access-layer-mip