Eliminating Cold Starts with Pre-Warmed Inference Pools
How Upbox keeps inference pools warm and ready, delivering sub-10ms response times even for bursty traffic patterns.
Why Cold Starts Kill User Experience
A cold start happens when your model needs to spin up from scratch - loading weights, initializing the runtime, and warming up GPU kernels. For large models, this can take 30 seconds or more. Users don't wait 30 seconds. They leave. Cold starts are the silent killer of AI applications.
The Pre-Warmed Pool Architecture
Upbox maintains pools of pre-warmed inference workers for every deployed model. These workers have already loaded your model, allocated GPU memory, and run warm-up inference. When a request arrives, it's routed to an already-warm worker instantly. No initialization, no waiting, just inference.
Predictive Pool Sizing
We don't just keep a fixed number of warm workers. Our system analyzes your traffic patterns and predicts when you'll need more capacity. If you get a traffic spike every day at 9 AM, we start warming additional workers at 8:55 AM. You never pay for idle capacity, but you always have warm workers ready.
The Always-On Mode
For latency-critical applications, we offer always-on mode. This guarantees at least one worker is always warm and dedicated to your model, ensuring sub-10ms response times for the first request and every request after. It costs more than scale-to-zero, but for some applications, guaranteed latency is worth it.