The whitepaper examines the rising costs and challenges of using accelerator-backed instances like GPUs and ASICs in cloud computing for HPC and AI applications.

It highlights the importance for IT decision-makers in balancing cost and performance in cloud platforms such as AWS, GCP, and Azure. The focus is on achieving optimal price-to-performance ratios without compromising quality and reliability. It suggests utilizing AI, FinOps, and expert services to optimize cloud costs and performance effectively.

Cloud computing has enabled businesses to innovate and scale faster than ever. However, as the demand for high-performance computing (HPC) and artificial intelligence (AI) applications grows, so does the cost of cloud resources. Accelerator-backed instances, such as GPUs and ASICs, offer significant speed and efficiency advantages over traditional CPUs, but they also come with a hefty price tag.

As organizations increasingly migrate to the cloud, the quest for optimal price-to-performance ratios has become a critical consideration. Here, we discuss the escalating costs associated with accelerator-backed instances for GPUs and ASICs, delving into the challenges across major cloud providers like AWS, GCP, and Azure while focusing on the pivotal role played by IT decision-makers such as CTOs, CIOs, and IT managers responsible for making decisions about cloud services, infrastructure, and expenditures.

So, how can you find the best price for performance on the cloud, without compromising on quality, performance, and reliability? In this article, we will explore the challenges and opportunities of using accelerator backed instances on the cloud, and how IT DecisionMakers within the customer’s organization can use AI, FinOps, and Eviden’s expertise to optimize your cloud cost and performance.