AI model aggregators have emerged as a practical shortcut to multi-model access. They bundle multiple AI models into a single, cost-effective subscription, allowing for simplicity, flexibility, and faster experimentation. This is more attractive than juggling multiple subscriptions from different AI service providers. Unfortunately, these aggregators face the challenge of keeping operating costs under control, the same challenge as other businesses in the AI and technology space. Rising operating costs are a consequence of rising RAM, GPU, and storage prices. This can lead some aggregators to be dishonest and substitute advertised models with cheaper models to save on costs. It is not a simple task to validate the integrity of these models, given how well smaller models can perform. CIOs and IT leaders must recognize this risk when using aggregators and implement verification and monitoring to safeguard performance, security, and …