FSICOST12: Are you saving costs by adopting a set of modern microservice architectures?

Financial institutions are moving from monolithic legacy systems such as mainframes into modern microservices architectures, giving them the flexibility of provisioning multiple environments to develop features rapidly, instead of waiting for the single monolith environment to be available, giving them greater agility and faster time-to-market.

FSICOST12-BP01 Migrate your mainframe and on-premises infrastructure to adopt a cloud-based microservices approach

Financial institutions are moving from monolithic legacy systems such as mainframes into modern microservices architectures, giving them the flexibility of provisioning multiple environments to develop features rapidly, instead of waiting for the single monolith environment to be available, giving them greater agility and faster time-to-market. Quantifying this gain is important for stakeholder buy-in.

Apply microservice design to generative AI architectures by decomposing large AI pipelines into modular micro-flows such as retrieve, reason, and act. This allows each step to scale and cost-optimize independently — for example, using smaller, lower-cost models for retrieval or classification, while reserving larger, high-quality models for reasoning or complex generation tasks. Deploy each flow as a separate containerized or serverless component (for example, using AWS Lambda, Amazon ECS, or Step Functions) to improve cost control, maintainability, and fault isolation.

This modular approach aligns generative AI workloads with modern software delivery practices and enables continuous cost visibility and performance tuning across the AI lifecycle.