This article highlights the pitfalls of adopting technologies without understanding business needs, illustrated through examples like cloud migrations and Kubernetes usage. It emphasizes the importance of aligning technology choices with specific requirements and offers practical recommendations for better architectural decisions.

The article discusses the rising adoption of GPUs for AI workloads and how organizations are increasingly using serverless compute services like AWS Lambda and Google Cloud Run. It highlights the inefficiencies in resource utilization across various platforms and the growing use of Kubernetes features like Horizontal Pod Autoscaler to optimize resource management.

Elastic's transformation to a serverless architecture for Elastic Cloud Serverless involved shifting from a stateful system to a stateless design, leveraging cloud-native object storage and Kubernetes for orchestration. The changes aimed to meet evolving customer needs for simplified infrastructure management and scalability while optimizing performance and reducing operational complexity. Key strategies included using a push model for control and data communication, automated upgrades, and flexible usage-based pricing.

Dacadoo successfully transformed its API service from a virtual machine to a Kubernetes-based architecture and finally to a fully serverless solution on AWS, achieving a remarkable 78% reduction in cloud costs and significantly lowering operational maintenance efforts. The transition enhanced scalability, availability, and automation, while complying with regulatory requirements for sensitive health data. This journey highlights the benefits of adopting managed services and modern cloud technologies.