Benchmarking Note: Comparing FastAPI and Triton Inference Server for ML Model Deployment

Efficient and scalable deployment of machine learning models is essential for production environments where latency, throughput, and reliability are critical. This benchmarking note provides a concise comparison between two common deployment methods: FastAPI and Triton Inference Server. Using a lightweight sentiment analysis model, we measured median (p50) and tail (p95) latency, as well as throughput, under a controlled experimental setup. Results show that Triton achieves superior scalability and throughput with batch processing, while FastAPI provides simplicity and lower overhead for smaller workloads. This note aims to highlight the architectural components and innovations, [SHG+15] benchmark its alignment with industry best practices, and [RDK19] provide a critical outlook on future extensions and research implications [MRA+25]. By citing the DOI and registering this note as a separate scholarly artifact, we enable proper attribution, reuse, and citation tracking within the research community. This note cites and builds upon Gopalan’s (2025) reference architecture for healthcare AI inference [Gop25], and is published on Zenodo with its own DOI for citation tracking.