Accelerating generative AI applications with a platform engineering approach

This article explains how platform engineering principles accelerate generative AI application development and deployment, addressing the challenge that only 6% of organizations successfully deploy generative AI in production despite 71% experimenting with it.

• Platform engineering enables faster time-to-value, cost control, and scalable innovation for generative AI
• Generative AI applications require frontend, data, controls, observability, orchestration, and LLM layers
• Frontend components include session management, authentication, authorization, and API connectors
• Data infrastructure requires vector databases for unstructured data and specialized APIs for structured data
• Unified output controls enforce safety and quality policies across all generative AI applications
• Observability through monitoring, logging, and evaluation ensures application health and performance
• Orchestration using Step Functions and DynamoDB manages complex multi-step workflows and agentic systems
• LLM deployment options include pretrained, fine-tuned, and custom models for different use cases

Platform engineering for generative AI enables organizations to rapidly adopt new models, maintain consistency, control costs, and future-proof their AI initiatives while scaling responsibly.