Amazon Elastic Inference vs Google AI Platform: What are the differences?

## Key Differences Between Amazon Elastic Inference and Google AI Platform

Amazon Elastic Inference is a service that allows you to attach low-cost GPU-powered inference acceleration to Amazon EC2 and SageMaker instances, enabling you to reduce the cost of running deep learning inference by up to 75% compared to a dedicated GPU instance. On the other hand, Google AI Platform is a managed service that enables you to build and deploy machine learning models using popular frameworks like TensorFlow and scikit-learn on Google Cloud. 

## Scalability:
Amazon Elastic Inference allows you to scale the amount of inference acceleration needed independent of the compute capacity of the instance, providing flexibility in managing costs and performance. Google AI Platform offers auto-scaling capabilities that adjust resources based on demand, making it easy to handle varying workloads efficiently.

## Cost Structure:
Amazon Elastic Inference charges are based on the type and number of Elastic Inference accelerators attached to instances, providing a transparent pricing model based on usage. In contrast, Google AI Platform follows a pay-per-use pricing model, where you only pay for the resources you consume, making it simple to manage costs without over-provisioning.

## Training vs. Inference:
Amazon Elastic Inference focuses specifically on inference acceleration, allowing you to optimize the execution of trained machine learning models without the need for dedicated GPU instances during inference. Google AI Platform covers both model training and deployment, offering a seamless end-to-end solution for the development and deployment of machine learning models.

## Model Serving and Monitoring:
Amazon Elastic Inference provides integration with AWS SageMaker for model serving and monitoring, enabling you to easily deploy and manage machine learning models in production. In comparison, Google AI Platform offers built-in tools for model versioning, monitoring, and continuous evaluation, simplifying the process of tracking model performance over time.

## Customization and Extensibility:
Amazon Elastic Inference allows you to customize the type and size of inference accelerators based on your specific application requirements, providing flexibility in optimizing performance for different use cases. Google AI Platform offers pre-built templates and tools for common machine learning tasks, as well as the ability to integrate with custom models and pipelines, allowing for greater customization and extensibility.

In Summary, Amazon Elastic Inference specializes in providing cost-effective GPU-powered inference acceleration, while Google AI Platform offers a comprehensive solution for building, training, and deploying machine learning models with features like auto-scaling, transparent pricing, and integrated monitoring tools.