Originally published on AI Tech Connect.
What you need to know Serverless GPU platforms occupy the tier between the two options every AI team already understands. Below them sit managed model APIs — you buy tokens from a model somebody else runs and you cannot change. Above them sits self-hosted infrastructure — your own GPU nodes, your own serving stack, your own autoscaler, and a bill that arrives whether anyone used the product or not. The serverless tier splits the difference: you bring your own model and container, the platform brings the GPUs, spins your workload up when a request arrives, bills by the second or minute, and scales it back to zero when traffic goes quiet. That middle tier wins in three situations. First, spiky or unpredictable traffic — a product that does 40 requests a minute at Indian lunchtime and UK…
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