The 47ms Difference That Made Me Reconsider TorchServe
First inference on a ResNet-50 model: 62ms with ONNX Runtime, 109ms with TorchServe. That's almost 2x slower for TorchServe — but here's the thing, those numbers flip completely once you understand what each tool is actually optimizing for.
I ran these tests on an AWS t3.medium (2 vCPUs, 4GB RAM) because that's what most people actually have access to when prototyping. The gap narrows dramatically on GPU instances, but CPU-only deployments are still the reality for many teams shipping their first model.
Why Setup Complexity Matters More Than You Think
TorchServe requires Java. That's the first surprise for Python developers expecting a pip-install-and-go experience. The model archiver, the configuration files, the handler classes — there's real infrastructure thinking baked in. ONNX Runtime? It's pip install onnxruntime and you're running inference in three lines.
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