This paper proposes a way to predict the cheapest safe AWS spot fleet before launching it.

AWS spot machines can be much cheaper, but users usually cannot see the final fleet price across regions before starting, so this paper turns that blind choice into a comparison that can save up to 64%.

Spot instances are cheap because they are conditional: the cloud provider can take them back, prices move, and capacity shifts by region.

The quiet problem is that AWS helps users launch spot fleets, but not fully see the fleet’s price or best region before launch.

The authors build a service that watches how AWS creates these fleets, learns those patterns with time-aware AI models, and then estimates the fleet mix and cost across 9 regions.

A user gives the service a target amount of computing power and a placement strategy, and the service returns region-ranked options before anything is launched.

They tested it on AWS with fleets up to 1500 virtual CPUs, using 720 test launches after a 90-day monitoring period.

The predicted fleet matched AWS exactly in 92.78% of cases, reached 99.79% overall accuracy against AWS behavior, and AWS accepted every recommended fleet.

Result is that choosing the best region mattered far more than changing the strategy inside 1 region, with possible savings up to 64%.

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Paper Link – arxiv. org/abs/2605.22778

Paper Title: "AI-Driven Multi-Region Provisioning for Cloud Services Using Spot Fleets"