FastAPI / Flask vs NVIDIA Triton Inference Server

FastAPI / Flask

In the olden days, most people rolled their own servers to expose their ML models to client applications. In fact, Roboflow Inference's HTTP interface and REST API are built on FastAPI.

In this day and age, it's certainly still possible to start from scratch, but you'll be reinventing the wheel and will run into a lot of footguns others have already solved along the way. It's usually better and faster to use one of the existing ML-focused servers.

Choose FastAPI or Flask if: your main goal is learning the intricacies ofmaking an inference server.

NVIDIA Triton Inference Server

Triton is a powerhouse tool for machine learning experts to deploy ML models at scale. Its primary focus is on extremely optimized pipelines that run efficiently on NVIDIA hardware. It can be tough to use, tradingoff simplicity and a quick development cycle for raw speed and isgeared towards expert users. It can chain models together, but doingso is a rigid and manual process.

Make any camera an AI camera with Inference

Inference turns any computer or edge device into a command center for your computer vision projects.

  • 🛠️ Self-host your own fine-tuned models
  • 🧠 Access the latest and greatest foundation models (like Florence-2, CLIP, and SAM2)
  • 🤝 Use Workflows to track, count, time, measure, and visualize
  • 👁️ Combine ML with traditional CV methods (like OCR, Barcode Reading, QR, and template matching)
  • 📈 Monitor, record, and analyze predictions
  • 🎥 Manage cameras and video streams
  • 📬 Send notifications when events happen
  • 🛜 Connect with external systems and APIs
  • 🔗 Extend with your own code and models
  • 🚀 Deploy production systems at scale

Get started today.

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