This framework guides startups in choosing to build, buy, or assemble AI/data platforms on AWS by weighing costs, time to value, team skills, scalability, security, and risks for a clear 12-18 month roadmap.

Strong data foundations on AWS—quality, accessible, governed data—are crucial for AI success. Use AWS tools like S3, Lake Formation, SageMaker, and Glue to build scalable, compliant, cost-optimized AI that drives clear commercial outcomes.

This playbook guides AWS-native startups to prioritize revenue-driven AI use cases, balance build vs. buy decisions, implement data governance, manage ML costs with FinOps, and ensure risk compliance for impactful AI strategies.

This guide clarifies AWS build vs buy trade-offs for startups, focusing on speed, cost, skills, compliance, and vendor lock-in to help align data platform choices with business goals and resources.

This guide outlines a lean, scalable AWS data strategy for startups, emphasizing simplicity, cost control, compliance, real-time processing, and readiness for AI to support growth without over-engineering.