Securing and Scaling AI-Powered APIs (capestart.com)

A recent engineering effort described an end-to-end architecture for securing and scaling AI-powered APIs—designed to support Life Sciences workflows such as literature review, NER, document summarization, semantic search and other real-time NLP/CV tasks. The solution combines microservices, an API Gateway with token/IAM-based authorization, and a Web Application Firewall to enforce authentication, rate limiting and common-threat protection. It emphasizes low latency and predictable performance through a Redis caching layer (session, response and data caching), distributed search/indexing, read replicas on a managed relational DB, and request queuing to prevent overload. Key technical choices target scalability and cost-efficiency: Kubernetes horizontal autoscaling (pods scaled on CPU/memory), AWS Lambda for event-driven tasks like image processing, load balancers and service discovery (NLB/Kubernetes), and asynchronous messaging via SQS. Operational best practices include centralized secrets (AWS Secrets Manager), observability with CloudWatch, and domain-driven microservice design. The architecture roadmap adds AI-driven predictive scaling and more serverless functions. For the AI/ML community, this blueprint shows pragmatic patterns—combining caching, autoscaling, security layers and hybrid serverless/container strategies—to reliably serve production-grade, latency-sensitive ML services while maintaining compliance and cost control.