Pepperdata transforms GPU resource management from reactive and ad hoc to proactive and data driven. Match GPU supply with demand. Maximize GPU utilization at scale. Dramatically reduce GPU costs.
PEPPERDATA RESOURCE OPTIMIZATION maximizes utilization and significantly reduces GPU cost by leveraging NVIDIA's Multi-Instance GPU (MIG) feature. Pepperdata partitions single GPUs into secure, independent GPU slices, creating three GPU slice pools in your environment for workload placement. Based on real-time data about GPU usage and demand, Pepperdata dynamically adjusts the capacity of each pool, scaling up or down as needed to prevent underutilization and bottlenecks. Pepperdata then intelligently assigns workloads to the most appropriate GPU partitions, learning from historical usage patterns to refine these assignments over time. The result is a fractional GPU slice management solution that rightsizes workload placement to dramatically minimize resource waste for GPU-intensive workloads.
PEPPERDATA DEMAND OPTIMIZATION provides you with a holistic understanding of GPU supply and demand in your environment. Pepperdata empowers you to make data-driven decisions about shifting GPU workload demand based on schedule or GPU type. Instead of reacting to requests from across the company, you can now proactively address imbalances between GPU demand and availability.
BENEFITS OF PEPPERDATA FOR GPUs
MORE EFFECTIVE GPU CAPACITY: Pepperdata maximizes GPU compute and memory utilization in the cloud or on prem.
IMPROVED GPU RESOURCE ALLOCATION: Pepperdata equips platform owners with the information they need to ensure that GPUs are consistently operating at optimal capacity.
AUTOMATICALLY INCREASED THROUGHPUT: More available GPUs and increased utilization mean more workloads can run to completion with the same resources. Furthermore, smaller, less demanding workloads can complete quickly, without having to wait for a full GPU to become available.
REDUCED OVERPROVISIONING: With accurate insights into true demand from Pepperdata, platform owners can avoid unnecessary GPU scaleups in the cloud or additional capital expenditures on prem.
SIGNIFICANT COST SAVINGS: Pepperdata cuts costs by running more workloads on fewer GPUs.
SUPPORTED TECHNOLOGIES
GPUs
Resource Optimization: NVIDIA A100 and newer GPUs,
Demand Optimization: All NVIDIA GPUs
PEPPERDATA: YOUR TRUSTED PARTNER
Pepperdata delivers dynamic resource optimization for Kubernetes workloads and AI infrastructure. Since 2012 Pepperdata has helped companies ranging from startups and mid-sized ISVs to top enterprises such as Citibank, Autodesk, Magnite, Royal Bank of Canada, and members of the Fortune Five save over $250 million. Learn more at pepperdata.ai.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay based on usage, billed by a per-hour vCPU fee. This single dimension charges you for each virtual CPU (vCPU) the software optimizes, measured hourly. As your clusters run more vCPU capacity, your cost scales up. When usage drops, your cost falls. There are no tiers or separate add-ons to choose. The tool runs in the background and continuously optimizes GPU, CPU, and memory across your Kubernetes and related workloads. Because billing follows actual vCPU-hours, your charges track how much compute the product manages over time.
Top-of-mind questions for buyers
What counts as one vCPU for the per-hour vCPU fee?
A vCPU is one virtual CPU on the nodes the software optimizes. The fee meters each vCPU the tool manages, counted per hour of running time. As nodes run more vCPU capacity across your clusters, more vCPU-hours are counted toward your bill.
Am I charged when my clusters or nodes are powered off or idle?
The fee meters running vCPU-hours only. Nodes that are fully powered off do not add vCPU-hours to your bill. For idle workloads, the tool signals the scheduler that unused resources are free, so other workloads can use them and existing nodes stay packed.
Does the fee apply on top of my existing autoscaler and cloud costs?
Yes. The per-hour vCPU fee is separate from your underlying AWS compute and storage charges. The tool works alongside your existing autoscaler and cost tools rather than replacing them. It adds resource optimization on top, so both the vCPU fee and your cloud provider charges apply.
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Pepperdata Professional Services hours to assist with installing, configuring, and tuning the Pepperdata Capacity Optimizer solution to optimize Kubernetes resources.
Quickly and efficiently realize up to 75% cost savings for your Amazon EKS or Amazon EMR clusters. No manual tuning. No recommendations. No application code changes.
Pepperdata Capacity Optimizer is enterprise software that automatically increases utilization by up to 80%, improving performance and delivering 30% average cost savings on Amazon EMR. Pepperdata Capacity Optimizer eliminates the need for manual tuning by optimizing compute and memory in real time with no application code changes. Pepperdata Capacity Optimizer pays for itself, immediately decreasing instance hours/waste, and freeing developers from manual tuning to focus on revenue-generating innovation.
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