Roboflow Serverless vs. Self-Hosted Inference: What a 90-Second Traffic Spike Does to One GPU Server

A benchmark of a computationally intensive vehicle-counting vision workflow found that a single self-hosted NVIDIA L40S GPU and Roboflow Serverless performed similarly under a steady load of five requests per second, but diverged sharply during a 90-second burst of 20 requests per second. Each request used SAHI to split a high-resolution parking-lot image into 16 tiles, creating 16 model inferences plus post-processing, which made the workload substantially heavier than a simple single-image inference. The self-hosted server could complete roughly 13 to 15 requests per second during the burst, causing a backlog, rising latency, timeouts, and continued failures after traffic returned to normal; it completed 1,095 requests with about half failing. Serverless temporarily increased latency but continued handling requests with near-zero failures, completing 2,273 requests at a 1.4-second median latency. The comparison argues that self-hosted GPU deployments require either costly peak-capacity provisioning or autoscaling that may be too slow for short bursts because GPU instances and inference servers can take minutes to start, while the serverless platform uses shared queued GPU workers, warm spare capacity, and fleet-level scaling to absorb spikes.

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