multi-cloud-architecture

To avoid vendor lock-in in multi-cloud architecture, use a decision framework that prioritizes cloud-native alternatives and abstraction layers over proprietary services. This approach ensures portability while allowing you to leverage best-of-breed services across AWS, Azure, and GCP.

The best way to select cloud services across AWS, Azure, and GCP is to apply a structured decision framework that compares compute, storage, and database offerings. This framework evaluates specialized capabilities, such as GCP for AI/ML and Azure for enterprise integration, to optimize cost and performance.

You need a multi-cloud architecture when a single provider cannot meet your requirements, such as needing specialized AI/ML capabilities from GCP alongside strong enterprise integration from Azure. It is also used for disaster recovery strategies and leveraging best-of-breed services.

Yes, you can use GCP and Azure together in a best-of-breed cloud architecture. This setup involves using GCP for specialized AI and ML capabilities while leveraging Azure for robust enterprise services, connected through architectural patterns that ensure resilience and cost optimization.

The limitation of using abstraction layers in multi-cloud systems is the potential for increased complexity and restricted access to provider-specific features. While they prevent vendor lock-in, abstraction layers can limit your ability to fully leverage native optimizations across AWS, Azure, and GCP.