What is AI infrastructure? Key components & how to build your stack

AI infrastructure encompasses a comprehensive stack of components necessary for developing, training, and deploying AI models, including compute, storage, networking, orchestration, and developer tools. Beyond just GPUs, which are crucial for training and inference tasks, AI infrastructure requires secure runtimes, vector databases, microservices, CI/CD, cost tracking, and observability tools to build a robust product around an AI model. Many platforms today focus on specific aspects like model serving or GPU access, but AI companies need a holistic approach that includes storage, databases, APIs, scheduling, and secure environments for reliable deployment. Northflank exemplifies a full-stack AI infrastructure platform by supporting the entire lifecycle of AI workloads, from training to deployment, while enabling integration with non-AI services like databases and microservices, ensuring robust security and scalability with features like multi-tenant support and hybrid GPU deployments.