Choosing the Right Cloud Provider

As CIO at Microsoft, at The Walt Disney Company, as well as CIO for the U.S. Federal Government, I've learned that public cloud selection is much more than just a pricing exercise. Business requirements, architectural considerations, and team skill sets and capabilities are all important additional considerations in selecting the right cloud platform. AWS and Google Cloud are often the choice for those seeking the ultimate in options for custom building applications and capabilities. Microsoft Azure offers more pre-integrated solutions for those organizations that are already heavily invested in Microsoft-based infrastructure and technologies. All of the “big three” platforms are innovating at a rapid pace, including AI options, advanced management and security tooling, and the ability to take advantage of the latest in compute, storage, and networking technologies. Cost considerations aren't just about compute and storage. Network bandwidth between cloud and on-premise systems often blindside teams. In some cases, these connectivity costs can match or exceed the cost for cloud compute and storage. The most successful cloud choices happen when teams do four things: 1. Test workloads and architecture before committing 2. Map all integration points and data flows 3. Account for ongoing optimization and growth needs 4. Select the appropriate level of cybersecurity protection for the business needs of the organization What matters isn't picking the "best" cloud. Pick the one that aligns best with your team's capabilities, operational model, and business requirements.

Your CTO wants Databricks. Your CFO wants Snowflake. Your Microsoft rep is pushing Fabric. All three are right- and all three are wrong. I've watched this exact conversation derail platform decisions for months. Everyone's got a compelling pitch. Nobody's asking the question that actually matters: Which platform is best for our team, budget, and use case—not which one has the best marketing? Here's the decision matrix your vendor reps won't share: Choose Microsoft Fabric when: ✅ You're already Microsoft-native (Azure, Power BI, Office 365). ✅ Your analytics team is business analysts, not data engineers- they want drag-and-drop, not code. ✅ Predictable monthly costs matter more than pay-per-query flexibility. ✅ Your CEO wants consolidation- one vendor for data, BI, and AI. ✅ Governance isn't optional- you need Purview built in. 🚩 Red flag: If your data science team lives in Python notebooks and avoids Power BI, Fabric will frustrate them. Choose Databricks when: ✅ You're building custom ML models, not just dashboards. ✅ Your team thinks in Python, Spark, and notebooks- SQL is a fallback. ✅ You need best-in-class ML infrastructure (MLflow, feature stores, model serving). ✅ You're processing massive unstructured data at petabyte scale. ✅ Cloud-agnostic flexibility is non-negotiable. 🚩 Red flag: If your CFO demands predictable pricing and your team builds dashboards, Databricks will burn budget. Choose Snowflake when: ✅ Structured data warehousing is 90% of your workload. ✅ Zero infrastructure management is required. ✅ Pay-per-query flexibility beats fixed costs. ✅ You're tool-agnostic for BI (Tableau, Looker, Power BI, Sigma). ✅ Cross-cloud data sharing matters. 🚩 Red flag: If you need production ML pipelines, Snowpark is still catching up. 𝙏𝙝𝙚 𝙃𝙮𝙗𝙧𝙞𝙙 𝙍𝙚𝙖𝙡𝙞𝙩𝙮-- Most $50M–$100M companies don't need to pick one. The winning 2026 architecture: 🔹 Fabric for BI/governance (Power BI + Purview + self-service) 🔹 Databricks for AI/ML (data science + feature engineering) 🔹 Snowflake for enterprise DW (if already invested) Use OneLake shortcuts and Delta sharing to connect platforms without duplicating data. Leaders using this framework make platform decisions in weeks, not months-and avoid $500K migrations because "the vendor said so. Which platform are you betting on for 2026? What's the one capability you wish it had from the others? #MicrosoftFabric #Databricks #Snowflake #DataStrategy

If you look closely at this stack across providers, you’ll notice that AI is just part of the puzzle. I’m not exaggerating when I say, when launching production-grade systems, 80% of the AI challenges continue to be engineering challenges. Selecting which model to work with isn’t even close to being the whole story. To successfully deploy and scale intelligent systems, one needs to understand how to make tradeoffs while evaluating hundreds of services offered by cloud providers like AWS, Google Cloud, and Microsoft Azure Each cloud has its edge; AWS leads in scalability, Google in data innovation, and Microsoft in enterprise integration. Let’s see how they compare across every key layer of the stack : 1.🔸Security & Governance - AWS ensures secure access and monitoring with IAM and GuardDuty. - Google focuses on unified security through Command Center and KMS. - Microsoft leads enterprise defense with Azure Defender and Sentinel. 2.🔸Integration & Automation - AWS automates workflows with Step Functions and Glue. - Google connects systems using Dataflow and Workflows. - Microsoft streamlines operations through Logic Apps and Data Factory. 3.🔸Compute & Infrastructure - AWS delivers scalable compute with EC2, Lambda, and Inferentia chips. - Google uses TPUs and GKE for AI scalability. - Microsoft powers hybrid workloads with Azure VMs and Functions. 4.🔸Data & Analytics - AWS supports data analysis through Redshift and Athena. - Google dominates big data with BigQuery and Looker. - Microsoft combines analytics and visualization via Synapse and Power BI. 5.🔸Edge & Hybrid - AWS offers low-latency AI with Outposts and Wavelength. - Google secures edge processing with GDC and Confidential Computing. - Microsoft extends cloud capabilities using Azure Arc and Stack Edge. 6.🔸Cloud AI Services - AWS offers SageMaker, Comprehend, and Rekognition APIs. - Google provides Vertex AI and Gemini for advanced AI solutions. - Microsoft integrates OpenAI, Cognitive Services, and ML Studio. 7.🔸Agent & Developer Tools - AWS includes Bedrock Agents and CodeWhisperer. - Google enables Gemini and LangChain integrations. - Microsoft supports Copilot Studio and Semantic Kernel. 8.🔸Prototyping & Design Tools - AWS empowers testing with SageMaker Studio Lab. - Google simplifies development using AI Studio and Opal. - Microsoft focuses on no-code creation via Designer and Recognizer Studio. 9.🔸Core Models - AWS relies on Titan and Bedrock models. - Google leads with Gemini. - Microsoft uses Phi, Orca, and Azure OpenAI. Understand how to set up your architecture for scalability, performance, cost, and reliability is a huge advantage, whether via single-cloud, multi-cloud, hybrid, or on-prem. Curious to know how you evaluate tradeoffs from services across these providers to set up your AI systems.