The exponential increase in AI inference workloads has necessitated integration with cloud-native architectures to deliver scalable, low-latency, and cost-effective production environments. The paper discusses design trends for deploying advanced AI models, such as LLMs, computer vision systems, and agentic workflows, across microservices, Kubernetes clusters, and serverless platforms. Among the most important are KServe, a declarative serving system with InferenceGraph; vLLM, a paged, attention-based, high-throughput inference system; Triton Inference Server, a multi-framework GPU optimization; and Ray Serve, a dynamic allocation system. These enable elastic scaling with GPU-affinity Horizontal Pod Autoscalers and model versioning in multi-tenant environments. Cold-starts lasting more than 60 seconds in 95 percent of serverless applications, GPU fragmentation with scheduling conflicts, lack of observability in opaque inference pipelines, security vulnerabilities in shared clusters, and increased energy consumption from sustained inference are all quantitatively analyzed. Future directions all point to AI-native cloud platforms that self-heal autonomously, the edge-cloud continuum of privacy-preserving real-time applications, and sustainable AI through quantization and carbon-conscious scheduling. The definitions of the symbols are as follows: L is the end-to-end inference latency (in milliseconds), T is the throughput (in requests per second), and alpha is the replica scaling factor. The research offers practical lessons for bringing experimental AI to robust enterprise implementations.