How to design a reliable fallback system for LLM apps using an AI gateway
Blog post from Portkey
In production environments, Language Model Models (LLMs) face numerous reliability challenges, including rate limits, timeouts, quota issues, and returning inaccurate outputs, necessitating robust fallback mechanisms to maintain application functionality. Designing systems that anticipate and handle these failures is crucial, as LLM outputs can be unpredictable, with issues ranging from API call timeouts to hallucinations, where models provide incorrect answers with unwarranted confidence. To mitigate these risks, AI gateways can facilitate fallback strategies by managing multiple providers, implementing routing logic, handling retries, and enforcing policies without complicating application logic. This approach ensures applications remain resilient and user experiences are unaffected, even during outages or degraded model performance. An AI gateway offers centralized control over LLM traffic, allowing for seamless integration of new models, dynamic routing adjustments, and enhanced observability, ultimately transforming a fragile system into a scalable reliability layer.
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