Serverless AI on Kubernetes: Benefits and Challenges of Using Knative for ML Workloads

Abstract

Traditional Kubernetes deployments for artificial intelligence workloads often result in resource underutilization and continuous infrastructure provisioning, leading to significant cost inefficiencies. Serverless computing paradigms address these challenges by enabling dynamic resource allocation and automatic scaling based on demand. Knative emerges as a prominent Kubernetes-native serverless platform that transforms how machine learning models are deployed and executed in containerized environments. The platform provides two core components: Knative Serving for automated deployment and traffic management, and Knative Eventing for creating complex event-driven workflows that enable asynchronous AI workload orchestration. Key advantages include scale-to-zero capabilities that eliminate resource waste during idle periods, seamless integration with existing Kubernetes ecosystems, and support for microservices-based AI applications. However, implementation presents notable challenges including cold start latency that affects real-time inference performance, dependency on specialized GPU optimization plugins, and constraints imposed by stateless architecture requiring external state management solutions. The complexity of debugging multi-component eventing workflows further complicates operational management. These trade-offs between resource efficiency and performance characteristics determine the suitability of Knative for specific machine learning deployment scenarios, particularly influencing decisions around latency-sensitive applications versus cost-optimized batch processing workloads.