I/O Bottleneck Cost Me $800/Month: Triton vs TorchServe

#triton #torchserve #modelserving #mlops

The $800 Wake-Up Call

My GPU utilization was sitting at 23%. The T4 instances were running around the clock, inference requests were piling up, and AWS was billing me $800/month for what turned out to be mostly idle hardware waiting on disk I/O.

The model wasn't the problem. A ResNet-50 variant for industrial defect detection, nothing exotic. The issue was TorchServe's default configuration treating every request like a separate snowflake—loading preprocessing configs from disk, writing temporary files, logging everything to S3.

I'd assumed model serving frameworks were optimized out of the box. That assumption cost me three months of unnecessary cloud bills before I finally benchmarked Triton Inference Server as an alternative.

Long exposure of Triton Fountain in Valletta, Malta, illuminated against the night sky. — Photo by Angelos Lamprakopoulos on Pexels

What Actually Happens During Inference

Most tutorials skip the unsexy parts. They show you the model forward pass and call it done. But in production, that forward pass is maybe 30% of your request latency.

Here's what a real inference pipeline does:

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