You will understand what a fallback model is, why retrying the same model is the wrong instinct, and how to wire a working fallback chain in Spring AI so your app keeps running when your primary AI model goes down.
Picture this: a student is using EngineerPrep's AI tutor at 11 PM the night before their Google interview. They ask a question about system design. The spinner keeps spinning. Nothing comes back. On the server side, every call to Claude on Amazon Bedrock is returning a ThrottlingException . Bedrock has a per-account request quota, and EngineerPrep just hit it. Your first instinct — retry the call — makes things worse. You're already over quota. Retrying the same provider burns more of the same quota and delays the student even longer. What you actually want is simple: try a different model . If Claude is busy, ask GPT-4. If that's slow, fall back to a smaller, faster model. Keep the student unblocked. That idea — "if the first choice fails, try the next one" — is a fallback model. Let's understand it properly before we write any code.
Think about how you book a restaurant. You have a favourite place. But on a busy Friday night it might be full. So you have a backup in mind. And a backup to that backup. You don't give up eating dinner just because your first choice is unavailable. A fallback model works exactly the same way. It's a list of AI models ordered by preference. Your app tries the first one. If it fails — because it's throttled, too slow, or just down — it automatically moves to the second. Then the third. The student gets an answer regardless. The word fallback just means "the thing you fall back on when your first choice doesn't work." In Spring AI, the library EngineerPrep uses to talk to AI providers, you can wire this list by composing multiple ChatClient beans. Each bean is a configured connection to one AI model — Claude on Bedrock, GPT-4 on OpenAI, or a smaller open-source model.…
Imagine three boxes in a row, left to right. Label them Claude (Bedrock) , GPT-4 (OpenAI) , and Mistral (small/fast) . An arrow points from your app into the first box. Step 1 — The request arrives. A student asks the AI tutor: "Explain consistent hashing." Your app sends that question to Claude on Bedrock. Draw a small envelope moving from your app into the Claude box. Step 2 — Claude says no. Bedrock responds with a 429 ThrottlingException. The Claude box turns red. The envelope bounces back. At this point, a naive app would show the student an error. But a fallback chain does something different. Step 3 — The service catches the failure. The fallback logic intercepts the error before it reaches the student. It crosses off Claude and looks at the next box in the chain. Step 4 — The envelope moves to GPT-4. The service resends the exact same question to GPT-4 on OpenAI.…