Data as of Oct 13, 2026A topic in MLOps and Inference Serving Platforms.
Reviewed by Dimitry Apollonsky
Mentioned in · Jul 8 – Oct 13, 2026
Google AI ModeOct 13, 2026
Several robust serverless GPU providers scale down to zero when inactive , ensuring you are not charged for idle time and only pay per second (or fraction of a second) when your code or model is actively processing requests.
Top options that fit this description include:
If you want to narrow this down, let me know:
I can help recommend which provider's infrastructure and cold-start profile match your setup best.
ChatGPT SearchOct 13, 2026
If you want to host an AI model on a GPU without paying for an idle machine, you're looking for serverless GPU inference with scale-to-zero. The GPU starts when requests arrive and shuts down when there's no work, so you pay for actual usage rather than 24/7 uptime.
I'd start by comparing these four providers, depending on whether you want to deploy your own model or use a model that's already hosted.
(Best all-around starting point)
Run your own model in a container and expose it as an API. Serverless workers can scale to zero, and billing is per second while workers run. Watch the idle timeout: a worker may continue to incur charges briefly after its last request.
Best for: custom models, Docker containers, and flexible GPU choices.
I need a platform for deploying models on serverless GPUs where I only pay for active compute time.
Can you recommend a GPU inference platform that operates on a serverless basis and doesn't bill me when the model is idle?
A strong choice for deploying your own Python-based inference function. It scales compute with demand and bills by the second. GPU, CPU, and memory charges can all contribute to your total, so don't compare GPU rates alone.
Best for: Python developers, custom inference pipelines, and bursty workloads.
Offers serverless GPU execution as well as hosted model APIs. Its published billing details distinguish runner execution from queue waiting and image pulling, which aren't billed as GPU execution time.
Best for: image, video, audio, and other generative AI workloads.
Particularly convenient if you want to call a pre-hosted model through an API rather than manage the deployment yourself. Public models generally bill for request execution, but private deployments can have different idle-billing behavior.
Best for: the simplest route to running an existing model.
If you're happy to use a model from an existing catalog, OpenRelay offers an OpenAI-compatible API billed by tokens, with no hourly charge while idle. The trade-off is that you can't simply upload arbitrary model weights and have your own model served through its shared catalog.
The most important distinction is whether you need to host your own weights.
1. What do you want to run?
My own model or custom weights
An existing hosted model via API
2. What kind of model?
LLM / chatbot
Image generation
Video generation
Other inference workload
3. How often will it receive requests?
Occasional requests, long idle periods
Bursts of traffic throughout the day
Continuous, fairly steady traffic
4. What's your biggest priority?
Lowest possible cost
Fast responses, minimal cold starts
Easy deployment with minimal maintenance
[Find my best option]
My initial recommendation: RunPod Serverless if you need to bring your own model, or OpenRelay if a catalog of existing language models will do. Both avoid the need to pay for a permanently running GPU, though their billing models differ.
