GCP vs AWS : Resource management interfaces

AWS and GCP each provide a command-line interface (CLI) for interacting with the services and resources. AWS provides the Amazon CLI, and GCP provides the Cloud SDK. Each is a unified CLI for all services, and each is cross-platform, with binaries available for Windows, Linux, and macOS. In addition, in GCP, you can use the Cloud SDK in your web browser by using Google Cloud Shell.

AWS and GCP also provide web-based consoles. Each console allows users to create, manage, and monitor their resources. The console for GCP is located at https://console.cloud.google.com/.
Service types

At a high level, cloud platforms begin by providing a set of baseline services: compute, storage, networking, and database services. AWS's baseline services include:

    Compute: Amazon Elastic Compute Cloud (EC2)
    Storage: Amazon Simple Storage Service (S3) and Amazon Elastic Block Store (EBS)
    Networking: Amazon Virtual Private Cloud (VPC)
    Databases: Amazon Relational Database Service (RDS) and Amazon DynamoDB

GCP's baseline services include:

    Compute: Google Compute Engine and Google App Engine
    Storage: Google Cloud Storage
    Networking: Google Virtual Private Cloud
    Databases: Google Cloud SQL, Google Cloud Datastore, and Google Cloud Bigtable

Each platform then builds other higher-level services on top of these services. Typically, these higher-level services can be categorized as one of four types:

    Application services: Services designed to help optimize applications in the cloud. Examples include Amazon SNS and Google Cloud Pub/Sub.
    Big data and analytics services: Services designed to help process large amounts of data, such as Amazon Kinesis and Google Cloud Dataflow.
    Management services: Services designed to help you track the performance of an application. Examples include Amazon CloudWatch and Google Stackdriver Monitoring.
    Machine learning services: Services designed to help you incorporate perceptual AI such as image or speech recognition, or to train and deploy your own machine learning models. Examples include Amazon SageMaker and Google Cloud Machine Learning Engine.

references:
https://cloud.google.com/docs/compare/aws/