Webinar Replay: The 4 Biggest Challenges of Scaling Cloud-Native AI Workloads

A CNCF webinar examines the challenges of deploying and operating AI models in cloud-native production environments, emphasizing that conventional provisioning, testing, and observability methods may not adequately address LLM API behavior. It highlights data quality and prompt design, Retrieval-Augmented Generation as a lower-cost alternative to training proprietary models, model serving infrastructure, and AI-specific monitoring metrics such as output accuracy and token consumption alongside latency, throughput, saturation, and errors. The presenter demonstrates an open-source Kubernetes proof of concept using Hugging Face Text Generation Inference, a React interface, a Node.js API, and GPU-enabled infrastructure to run an open-source model, while showing how token limits can affect response time, completeness, and error conditions. The session also recommends API-level observability and service mocking, which records realistic model responses and failures so developers can test locally without repeatedly deploying expensive GPU-backed models.

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