From Flask to vLLM: How Model Inference has evolved (2017-2025)

AI model inference has evolved from early Flask-based microservices to specialized serving frameworks, while production systems increasingly combine multiple models, agents, and modalities rather than relying solely on large language models. The discussion contrasts TensorFlow Serving and TorchServe for framework-specific production serving, FastAPI for flexible prototypes, LitServe for general-purpose AI systems with built-in batching, streaming, and multi-model support, and vLLM or SGLang for optimized LLM-only deployments requiring capabilities such as KV caching. It argues that performance improvements usually depend on careful profiling and efficient model operations rather than indiscriminate use of compilers or custom CUDA kernels. Hosting options range from local cloud environments and VM snapshots to Docker containers, Kubernetes, and managed deployment services, with trade-offs involving startup speed, scalability, observability, and operational complexity. The author’s central recommendation is to begin with a simple deployment approach, avoid unnecessarily complex infrastructure stacks, and select serving tools according to model type, performance requirements, and system complexity.

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