Cloud computing has opened the door to staggering innovation in the past few years, but such resource consumption also comes with a price tag. Sticker shock is ongoing and made even more dramatic with some major providers increasing prices due to inflation. This even spawned a new term: “cloud-flation.”
Pay-as-you-go cloud solutions allow you to use what you need right now and scale on demand, but without the proper guardrails in place, cloud bills can rise quickly, spinning out of control. A 2023 study by Wakefield Research showed that 98% of DevOps leaders surveyed had seen unexpected spikes in costs several times during the year. More than half said they saw unexpected overages monthly.
Solution architects are driving toward a fully managed, cloud-based, end-to-end solution for high-performance computing (HPC) and AI that makes it easier, faster, and more cost-efficient for end users, developers, and data scientists to deploy HPC, AI, and converged HPC/AI workloads on high-performance clusters.
A Cloud-First Approach to Running HPC and AI Workloads
Experienced HPC users with legacy data center infrastructure may choose to run most workloads on-prem and burst to the cloud when they need excess capacity in a hybrid cloud compute environment. However, newer HPC and AI users tend to deploy workloads in a cloud-only environment. A cloud-only environment reduces the hefty upfront costs for infrastructure, but can generate significant—and sometimes unanticipated—compute bills.
Regardless of how you operate, you need a way to operationalize your cloud resources efficiently, especially when it comes to CPU and GPU horsepower, so that your team has the compute power they need when they need it—without blowing the budget.
Cloud deployments, however, generally lack the day-to-day usage oversight needed to manage costs, and corporate IT administrators are typically already stretched thin responding to requests for a broad array of services. Cloud-flation can happen quickly, especially with users charged with running compute-intensive workloads running on cloud-based clusters of high-powered instances.
Data science teams, for example, are charged with producing specific—and highly valued—results. In an effort to deliver timely results, they may configure cloud-based compute clusters without full awareness of the hourly cost of usage—or, of their spending profile relative to their team’s budget.
There are other challenges as well. Even if users have access to dashboards showing the costs of cloud resources, they have limited visibility into the whole picture. Organizations need tools that:
- Manage the use of on-premises, dedicated cloud, and public cloud resources in a single solution
- Enable IT teams to establish spending guardrails and implement usage policies
- Provide finance teams with cost and usage reporting to support better decision-making
A Hybrid Cloud Control Plane
By taking this holistic view of all available compute resources—whether in your data center or residing in the cloud—Penguin provides an end-to-end control plane for HPC, AI, and converged HPC/AI workloads on high-performance clusters balancing the increasing demand for compute resources and budgetary constraints.
Allowing users to execute workflows across thousands of cores from a centralized, intuitive interface, you can control resource settings and configure new compute resources as needed, selecting from a range of instance types and spinning up or shutting down pools as needed.
By optimizing cloud and on-prem environments, you can control costs without sacrificing capacity, enabling high availability, bursting, and scaling up to thousands of nodes. This enables you to manage the cloud without tying up support staff.
Controlling Compute Costs
Besides optimizing the compute environment, you get robust tools to manage spending. Even with diligent monitoring of cloud costs, many cloud providers only provide spending data on a 24-hour delay. When you’re spinning up hundreds of nodes, you can run up a hefty bill and not know until the next day.
Automatically pull and analyze cloud billing and usage data within minutes, so you can better forecast and manage your spend. You can also enable rules to prevent overspending and provide notifications to project groups when they hit their spending thresholds.
Optimizing Cloud Compute Resources and Costs
Manage all aspects of your HPC and AI workloads from a single interface that works with all major cloud service providers with built-in cost controls.
Benefits include:
- An intuitive interface with insights into computer spending
- Tools to create reusable and shareable workflows
- Resource allocation for cloud and on-prem clusters
- Workload processing and results assessments for running simulations
- Pre-built workflows for dozens of HPC and AI applications
Streamline Your HPC and AI Workloads While Controlling Your Spend
Purpose-built for HPC and AI and fully validated on Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure, and Penguin On-Demand (POD), end-users can access the compute resources they need without having to worry about infrastructure limitations, while working within cost controls and budget constraints. Organizations can optimize their infrastructure and avoid sticker shock in monthly bills.
Get the most out of your HPC and AI workloads with Penguin Solutions. For additional information, contact Penguin Solutions today.
