𝗪𝗵𝘆 𝗜 𝗦𝘁𝗼𝗽𝗽𝗲𝗱 𝗥𝗲𝗹𝘆𝗶𝗻𝗴 𝗼𝗻 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗔𝗜 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿
I built a real-time chatbot for a community forum. I used only the OpenAI API. It seemed simple.
Three weeks later, I hit a 5xx error during peak hours. My chatbot went dark. Users were angry. I realized I cannot trust one provider for production apps.
I faced several issues with a single provider:
- Rate limits
- Timeouts
- Complete outages
I tried other providers, but they all had different formats and authentication methods. My code became a mess of switch-case statements.
I needed a system to:
- Standardize different providers
- Retry automatically when one fails
- Cache responses
- Avoid vendor lock-in
I avoided third-party libraries because they were too rigid. Instead, I built a custom fallback system using a simple design.
First, I created a common interface for all providers. This allows any AI model to work with the same code.
Next, I built a router class. This class tries providers in order. It uses exponential backoff and simple caching to manage failures.
Here is the logic:
- Define an abstract base class for AI providers.
- Implement specific classes for OpenAI and other providers.
- Use a router to loop through your list of providers.
- If a provider fails, the router waits and tries the next one.
This system saved my project during three recent outages. It stays transparent and simple.
If you build with AI, remember these points:
- Use Redis for caching in production instead of a local dictionary.
- Add cost tracking to monitor your spending.
- Implement asynchronous support for faster responses.
- Parse "Retry-After" headers to handle rate limits better.
Do not over-engineer if your project is small. But if your service depends on uptime, build a fallback.
How do you handle provider reliability in your projects? Do you use a fallback layer or rely on one vendor?