Moving to Google Cloud is not an all-or-nothing option. Certain workloads can continue to remain on-premise while the predominant chunk moves to Google Cloud

Per Google docs, specifically for GCP Cloud Storage there exists four types of storage with one of them, standard storage, being described as "storage for data that is frequently accessed ("hot" data) and/or stored for only brief periods of time." https://cloud.google.com/storage

Cloud Datastore is the best for semi-structured application data that is used in app engines' applications.
Bigtable is best for analytical data with heavy read/write events like AdTech, Financial or IoT data. Cloud Storage is best for structured and unstructured, binary or object data like images, large media files and backups. SQL is best for web frameworks and in existing applications like storing user credentials and customer orders. Cloud Spanner is best for large scale database applications that are larger than two terabytes; for example, for financial trading and e-commerce use cases. As I mentioned at the beginning of the module, depending on your application, you might use one or several of these services to get the job done.

Many organizations have separate development and production environments so they can build and test new features without disturbing production traffic. In Optimizely, you can create separate projects for each environment to help with governance.
With separate development and production projects, your organization can safely build and QA experiments and Personalization campaigns in a development environment before deploying to production. This approach allows multiple stakeholders in your organization to act as gatekeepers for running new experiments in production.

Reference link- https://support.optimizely.com/hc/en-us/articles/4410284353805-Set-up-projects-for-development-and-production-environments