Large language models (LLMs) form the backbone of today’s AI applications, powering everything from chatbots and co-pilots to industry-specific automation. Yet not all models are created equal. Some are optimized for complex reasoning, others for speed, efficiency, or domain-specific accuracy. This diversity means organizations often face trade-offs when choosing a single provider. In many cases, applications are built tightly around a specific model, creating dependencies at the code, prompt, and response handling levels. Additionally, recent outages across OpenAI products, including ChatGPT, Sora, and its API, highlight another challenge: vendor dependency. Depending on a single model or provider can undermine the reliability that businesses require from their systems. For applications where downtime can translate directly into lost trust or revenue, the risk is magnified. Switching providers later often incurs technical debt, as application logic must be untangled from model-specific behavior or assumptions. …