Can you recommend a GPU inference service for deploying custom vision models in production?

Yes — for custom vision model inference in production, I’d usually recommend one of these based on your priorities:

Best all-around for production control

AWS SageMaker Real-Time Endpoints

• Good if you want managed deployments, autoscaling, and strong AWS integration
• Supports custom containers, so vision models like YOLO, Detectron, segmentation, etc. are straightforward
• Better for teams that already use AWS and want enterprise-grade ops

Best if you want flexibility + easier deployment

Modal

• Very easy to deploy Python model code with GPU support
• Great for custom vision inference APIs, batch jobs, and rapid iteration
• Less infrastructure work than building on raw cloud VMs

Best for high-performance low-level control

Runpod

• Good GPU pricing and flexibility
• Useful if you want to run your own inference server stack like Triton, vLLM (for multimodal), TorchServe, or FastAPI + TensorRT
• More DIY than SageMaker, but often cheaper and more customizable

Best for edge-to-cloud style managed serving

NVIDIA Triton Inference Server on a managed GPU platform

• Excellent for optimized vision inference
• Supports batching, multiple model backends, and high throughput
• Often paired with Runpod, AWS, GCP, or Azure rather than used as the platform itself

Best enterprise managed option outside AWS

Google Vertex AI or Azure Machine Learning

• Good if you’re already in those ecosystems
• Strong MLOps features, model registry, monitoring, and autoscaling

My practical recommendation

• If you want fastest path to production with minimal ops: Modal
• If you want enterprise production deployment and are already on AWS: SageMaker
• If you want cheaper GPU infra and full control: Runpod + Triton

For custom vision models specifically, look for:

• GPU types available: T4, L4, A10, A100, H100
• Support for custom Docker containers
• Autoscaling and cold-start behavior
• REST/gRPC serving
• Request batching
• Monitoring/logging
• Cost per inference at your expected traffic
• Support for model formats: PyTorch, ONNX, TensorRT

If you tell me:

1. your framework (PyTorch / ONNX / TensorRT),
2. expected traffic, and
3. your cloud preference,

I can recommend the best specific service and deployment setup.