The problem In the age of generative AI and large language models (LLMs), access to GPU compute is the new oil. GPU pricing is volatile, opaque, and scattered across a fragmented market of cloud providers. For teams deploying inference pipelines at scale, these cost fluctuations aren’t just a nuisance—they're a financial and operational risk. For example, let's take a look at spot pricing for p5.48xlarge ( 8 x Nvidia H100s) on AWS over the past 6 months: p5.48xlarge Stockholm (eu-north-1) currently runs H100s at about $1.1 per gpu per hour whereas London (eu-west-2) pays almost $3.7 per gpu per hour. If Brexit hasn't hit the UK hard enough, AWS is charging almost 3x more for running H100s in the UK! The higher cost in London is also an indication that gpu resources are tighter in London than Stockholm and thus provisioning 8 H100s will be more challenging for both spot and "On-Demand" instances. Stockholm (eu-north-1) $1.1 per gpu per hour London (eu-west-2) $3.7 per gpu per hour This isn't isolated to H100s, but also affects cheaper gpu hardware like the g4dn.xlarge instance, a single Nvidia T4 gpu: g4dn.xlarge which costs about $0.07 per gpu per hour in me-south-1 (Bahrain) whereas Singapore (ap-southeast-1) are paying $0.28 per gpu per hour for the same service. At GordianLabs, we often see 2-4x cost difference across datacentres from a single provider. $0.07 per gpu per hour me-south-1 (Bahrain) Singapore (ap-southeast-1) $0.28 per gpu per hour Cloud GPU pricing is a moving target. Depending on the provider, region, and instance type, spot instance prices can swing 2x–5x within days. On-demand and reserved prices vary too, with limited transparency into supply, demand, or underlying trends. 2x–5x within days This leads to: Cost overruns on training runsDelayed deployments due to spot instance interruptionsMissed savings from poor regional selection Cost overruns on training runs Cost overruns Delayed deployments due to spot instance interruptions Delayed deployments Missed savings from poor regional selection Missed savings Most teams react to GPU costs after the fact—when the bill arrives. GordianLabs.ai flips this model by predicting costs before you spin up a single instance. before The Solution GordianLabs uses world leading AI experts and more than 55M data points of cloud pricing data to predict future GPU pricing from 1 day to 3 months in advance. world leading AI experts 55M data points We serve these predictions in a simple API, which can be wired into your existing infrastructure. Drop us an email - hello@gordianlabs.ai if you'd like save more than 50% on your gpu budget.
