1. Model Training: Google AI Platform allows users to train machine learning models using their own data and scalable computing resources, while NanoNets offers pre-trained models that can be fine-tuned with user-specific data.
2. Integration: Google AI Platform integrates seamlessly with other Google Cloud services and offers a wide range of tools for data preparation, experimentation, and deployment, whereas NanoNets focuses more on providing an easy-to-use platform specifically for image and video recognition tasks.
3. Scalability: Google AI Platform provides scalable infrastructure for training and deploying models, allowing users to handle large datasets efficiently, while NanoNets is designed for smaller scale projects with simpler requirements.
4. Customization: Google AI Platform offers more flexibility in terms of customizing machine learning pipelines and workflows, allowing users to incorporate their own algorithms and libraries, whereas NanoNets is more focused on providing out-of-the-box solutions tailored for specific use cases.
5. Cost: Google AI Platform is a paid service with various pricing plans based on usage and resources, while NanoNets offers a free tier with limited features and additional paid plans for more advanced functionalities.
6. Support and Community: Google AI Platform benefits from Google's extensive documentation, support services, and large user community, providing users with resources and assistance, whereas NanoNets has a smaller user base and limited support options.

In Summary, Google AI Platform offers a more customizable, scalable, and integrated solution for machine learning projects, while NanoNets focuses on providing pre-trained models and simplicity for specific use cases.