FastAPI vs Triton: Which AI Inference Server Should You Use for Healthcare?
- FastAPI Single Request Latency: 22ms — Suitable for simple services
- Triton Throughput: 780 RPS per GPU — Overwhelming for large batch processing
- Conclusion: A hybrid approach using both is the answer
Comparison at a Glance
| Item | FastAPI | Triton Inference Server |
|---|---|---|
| Latency (p50) | 22ms | 0.44ms |
| Throughput | Limited (single process) | 780 RPS/GPU |
| Learning Curve | Low | High |
| Batch Processing | Manual implementation required | Built-in dynamic batching |
| HIPAA Compliance | Used as a gateway | Dedicated to backend inference |
Features of FastAPI
It’s a Python web framework. Simply put, it’s a tool that wraps a model into a REST API. Installation to deployment can be completed in a few hours.[arXiv]
Advantages
- Low barrier to entry — You can start right away if you know Python
- Flexible — Customizable as desired
- Low latency of around 22ms in a single request
Disadvantages
- Limited scalability — Large-scale processing is not possible with a single process[Medium]
- Synchronous inference blocks the event loop — Even with an async handler, other requests cannot be processed during inference
Features of Triton Inference Server
It is an inference-specific server created by NVIDIA. TensorRT, PyTorch, and ONNX models can be uploaded as is. Optimized for high-volume traffic.[NVIDIA Docs]
Advantages
- Dynamic batching — Collects requests and processes them at once, improving throughput by 2x[arXiv]
- Multi-GPU support — Easy horizontal scaling
- Recorded 15x faster performance compared to FastAPI in the Vestiaire case[Vestiaire]
Disadvantages
- Steep learning curve — Requires understanding of configuration files and backend concepts
- Infrastructure overhead — Excessive for small services
When to Use Which?
When to choose FastAPI: Prototype stage, CPU-only inference, internal tools with low request volume
When to choose Triton: Production deployment, GPU utilization required, processing hundreds or more requests per second
Personally, I think a hybrid approach is more realistic than choosing just one. The conclusion of the paper is also the same.
Hybrid Architecture in Medical AI
The method proposed by the research team is as follows. FastAPI handles PHI (Protected Health Information) de-identification at the front end, and Triton at the back end is responsible for the actual inference.[arXiv]
The reason why it is important is that HIPAA compliance has become stricter in 2026. HHS has significantly revised the security rules for the first time in 20 years.[Foley] The moment AI touches PHI, encryption, access control, and audit logs become essential.
The hybrid structure captures both security and performance. Sensitive information is filtered out in the FastAPI layer, and Triton processes only clean data. The paper calls this “best practice for enterprise clinical AI.”
Frequently Asked Questions (FAQ)
Q: Can I use FastAPI and Triton together?
A: Yes, it is possible. In fact, that’s the method the paper recommends. FastAPI acts as a gateway, handling authentication, logging, and preprocessing, while Triton handles GPU inference. Using the PyTriton library makes integration easier because you can control Triton with a Python-friendly interface.
Q: What do you recommend for beginners?
A: It’s best to start with FastAPI. After learning the basic concepts of model serving, you can switch to Triton when traffic increases. If you use Triton from the beginning, you will be struggling with the settings and not focusing on improving the model. However, if high-volume traffic is expected from the beginning, going directly to Triton will reduce rework later.
Q: What are the precautions when deploying Kubernetes?
A: This paper is the result of benchmarking in a Kubernetes environment. In the case of Triton, GPU node scheduling and resource limit settings are key. Installing the NVIDIA device plugin is essential, and HPA (Horizontal Pod Autoscaling) must be set based on GPU metrics to operate properly. FastAPI is not much different from general Pod deployment.
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References
- Scalable and Secure AI Inference in Healthcare – arXiv (2026-01-19)
- Triton Inference Server Documentation – NVIDIA (2026-02-03)
- HIPAA Compliance for AI in Digital Health – Foley & Lardner (2025-05-01)