Java Spring Boot vs Python FastAPI for Serving ML/LLM Inference APIs: A Comparative Study of Performance, Concurrency, Ecosystem Fit and Deployment Cost

Machine learning models are of little use until they are wrapped in a service that other applications can call. In practice, this serving layer is built with one of two very different families of tools. Python frameworks such as FastAPI sit right next to the libraries in which most models are trained, while Java frameworks such as Spring Boot dominate enterprise back ends where teams already have mature security, monitoring and deployment practices. The question a team usually faces is whether to keep the model API in Python or to move it to the JVM stack the rest of the organisation already runs. This paper compares the two options for serving both classical ML models and large language model (LLM) workloads. The comparison covers the programming and concurrency model of each framework, how each one loads and runs models, support for token streaming, request validation, observability, container footprint, start-up behaviour and developer productivity. A reproducible benchmark design is proposed in which the same ONNX model and the same request payloads are served by a FastAPI service and a Spring Boot service, and both are measured under identical load for latency percentiles, throughput, CPU use, memory use and start-up time. For LLM workloads, the paper looks at the case where the web framework acts as a gateway in front of a dedicated inference engine and streams generated tokens back to the client. The study is intended to give practitioners a clear, evidence-based way of choosing between the two frameworks rather than defaulting to habit.