How to Choose Between AWS, Azure, and Google Cloud

No. Base compute pricing across the three is close, and the cheapest option depends entirely on your specific workload, usage patterns, and existing agreements. An organization with Microsoft enterprise licensing may find Azure cheaper in practice, while a data-heavy team may save more on Google Cloud. Model your real usage rather than relying on headline rates.

Usually not at the start. Multi-cloud increases operational overhead, splits your team's expertise, and complicates security and cost management. Begin with a single primary provider and only add a second when a specific, concrete requirement justifies the added complexity.

All three offer strong AI and machine learning services, so the best choice depends on where your data and team already live. Google Cloud is often favored for data pipelines and custom ML, Azure has deep integration with OpenAI models and Microsoft tooling, and AWS offers a broad set of services through SageMaker and Bedrock. Choose based on your data platform and existing skills rather than marketing claims.

For most mid-market environments, expect a serious migration to run several months, depending on the number of workloads, data volume, and how many proprietary services are involved. Containerized, well-documented applications move faster; deeply integrated proprietary systems take longer. Always budget for a period of running both environments in parallel.

Only to the degree you let it. Compute and containers stay portable, but proprietary managed services create real switching costs. You can control lock-in by containerizing workloads, using infrastructure as code, and isolating provider-specific services behind your own interfaces.