List of Pepperdata Capacity Optimizer Customers
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United States
Since 2010, our global team of researchers has been studying Pepperdata Capacity Optimizer customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Pepperdata Capacity Optimizer for SaaS Spend Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Pepperdata Capacity Optimizer for SaaS Spend Management include: Citigroup, a United States based Banking and Financial Services organisation with 230000 employees and revenues of $81.09 billion, Autodesk, a United States based Professional Services organisation with 15300 employees and revenues of $6.13 billion, Extole, Inc., a United States based Professional Services organisation with 100 employees and revenues of $15.0 million and many others.
Contact us if you need a completed and verified list of companies using Pepperdata Capacity Optimizer, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Autodesk | Professional Services | 15300 | $6.1B | United States | Pepperdata | Pepperdata Capacity Optimizer | SaaS Spend Management | 2020 | n/a |
In 2020, Autodesk deployed Pepperdata Capacity Optimizer to optimize Apache Spark workloads running on Amazon EMR across its United States cloud environment, aligning the project with SaaS Spend Management objectives. The deployment targeted cloud cost controls and performance efficiency with a focus on EC2 instance hour reduction and Amazon EMR cost containment.
Pepperdata Capacity Optimizer was configured to perform workload-driven sizing, automated tuning, and continuous utilization monitoring for Spark jobs on EMR. The implementation used capacity optimization and adaptive resource allocation capabilities to reduce manual intervention by the platform data team while maintaining or improving job throughput.
Integrations are explicitly with Amazon EMR and underlying EC2 compute, reflecting an architecture that operates in the AWS data processing stack. Operational coverage was centered in the United States, and the initiative engaged both cloud operations and finance stakeholders for a cost-optimization use case, freeing the platform data team from manual tuning activities.
Governance and operational workflow adjustments focused on shifting tuning responsibility from engineers to the optimizer, enabling continuous policy-driven resource control and greater visibility for finance and IT. Outcomes reported include reduced EC2 instance hours, cutting Amazon EMR costs by over 50 percent, increased utilization, and improved throughput, demonstrating Pepperdata Capacity Optimizer delivering measurable SaaS Spend Management benefits for Autodesk.
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Citigroup | Banking and Financial Services | 230000 | $81.1B | United States | Pepperdata | Pepperdata Capacity Optimizer | SaaS Spend Management | 2021 | n/a |
In 2021, Citigroup implemented Pepperdata Capacity Optimizer to improve cloud cost controls and capacity planning. The deployment was positioned as a SaaS Spend Management solution supporting finance and IT infrastructure functions in the United States.
The implementation centered on the Pepperdata Capacity Optimizer module, leveraging capacity profiling and workload optimization capabilities typical of capacity optimization solutions. Configuration work focused on workload-level visibility, automated right-sizing recommendations, and policy-driven capacity controls to align compute consumption with finance workflows.
Integrations were oriented to cloud compute fleets, with explicit emphasis on cloud EMR and EKS environments and cloud billing feeds to surface consumption for chargeback and budgeting. Operational coverage targeted infrastructure operations and finance teams in the United States, enabling shared telemetry between platform engineering and cost management processes.
Governance activities included embedding Capacity Optimizer outputs into capacity planning cycles and cloud cost governance, and adapting tagging and allocation processes to consume optimizer insights. Vendor materials cited by Pepperdata report typical infrastructure savings around 30 percent and multi-million-dollar hardware and cloud cost reductions, these outcomes are presented as vendor-reported results for financial-services deployments using Pepperdata Capacity Optimizer.
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Extole, Inc. | Professional Services | 100 | $15M | United States | Pepperdata | Pepperdata Capacity Optimizer | SaaS Spend Management | 2025 | Rackspace |
In 2025 Extole, Inc. implemented Pepperdata Capacity Optimizer on Amazon EMR in the United States to optimize reporting and analytics workloads. The deployment positioned Pepperdata Capacity Optimizer as part of Extole's SaaS Spend Management approach, framed specifically as an operational and finance optimization for reporting and analytics workloads.
Configuration focused on capacity optimization, dynamic resource allocation, and real-time workload visibility to align Amazon EC2 consumption with job demand. The implementation used the optimizer's tuning and scheduling controls to reduce idle capacity and prioritize throughput for analytics jobs, leveraging EMR-native metrics and Pepperdata monitoring to drive automated adjustments.
Rackspace introduced Pepperdata and supported initial provisioning on Amazon EMR, enabling integration between the optimizer, EMR job orchestration, and EC2 compute resources. Operational coverage targeted data engineering and finance reporting workflows in the United States, with teams instrumenting cost and performance dashboards to track optimization activities.
The rollout was executed as a focused operational and finance program and delivered explicit outcomes, achieving a 30% reduction in Amazon EC2 compute costs within five days while improving job throughput. Governance emphasized ongoing cost and performance reporting owned by data operations and finance to sustain the optimization.
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