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I run a small project that sends a daily news digest. Every morning, a script fetches articles, summarizes them with AI, and emails me a summary.
It worked for three months. Then, at 3:14 AM, my phone buzzed. My pipeline was failing.
The cause was simple. I used a free API tier. The provider hit a rate limit and blocked my requests.
I learned a hard lesson. Relying on one AI provider is risky. If that one service goes down, your whole project dies.
My first attempt to fix it was a simple retry logic. This helped with small network hiccups. It did not help when the API was down for an hour.
My second attempt was a messy chain of if-else statements. I tried OpenAI, then Hugging Face, then Cohere. This became a nightmare to manage. Every provider had different code and different rules.
I solved this by building an abstraction layer.
I created a single template for all AI providers. Each provider now follows the same rules. This makes the code clean.
Here is how I structured it:
โข I built a base class for all summarizers. โข I created specific versions for OpenAI and Hugging Face. โข I built an orchestrator to manage them.
Now, the system works like this:
The script tries the first provider. If it fails, it immediately tries the second one. If the cloud APIs fail, it uses a local model on my own machine.
The local model is slower, but it always works.
This setup has run for six months without a single error.
The trade-offs:
- More code to maintain.
- Local models require more computer power.
- Testing requires more effort.
But reliability is worth the extra work.
Do you rely on a single API for your projects? How do you handle failures?
Optional learning community: https://t.me/GyaanSetuAi