List of Apolo AI Platform Customers
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Since 2010, our global team of researchers has been studying Apolo AI Platform 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 Apolo AI Platform for ML and Data Science Platforms 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 Apolo AI Platform for ML and Data Science Platforms include: Synthesis AI, a Japan based Manufacturing organisation with 355 employees and revenues of $85.0 million, Cato Digital, a United States based Banking and Financial Services organisation with 20 employees and revenues of $10.0 million, PowerSetter, a United States based Utilities organisation with 50 employees and revenues of $8.0 million and many others.
Contact us if you need a completed and verified list of companies using Apolo AI Platform, 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.
The Apolo AI Platform customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Cato Digital | Banking and Financial Services | 20 | $10M | United States | Apolo | Apolo AI Platform | ML and Data Science Platforms | 2024 | n/a | In 2024 Cato Digital deployed the Apolo AI Platform, Category: , natively on its low-carbon bare-metal platform to deliver turnkey AI and ML services to customers. The implementation concentrated on data center GPU orchestration and an MLOps stack that enabled a rapid launch of sustainable AI services running on 100% green infrastructure. The Apolo AI Platform deployment implemented orchestration and MLOps functional modules for GPU workload scheduling, model lifecycle management, continuous training and deployment pipelines, and integrated monitoring and security for AI workloads. The stack was provisioned directly on bare-metal servers to optimize GPU utilization and operational efficiency for production AI workloads. Operational scope was within the United States with Milpitas and New York partnerships noted in the case study, and the project targeted customer-facing AI/ML service offerings alongside platform operations. Governance centered on integrated monitoring and security controls for AI workloads and on operational procedures for managing GPU orchestration and model lifecycle processes, supporting a repeatable rollout of sustainable AI services. | |
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PowerSetter | Utilities | 50 | $8M | United States | Apolo | Apolo AI Platform | ML and Data Science Platforms | 2024 | n/a | In 2024, PowerSetter implemented Apolo AI Platform to provision a GPU Hub and an AI-centric ecosystem for its energy comparison service in the United States, using the AI/ML + GPU infrastructure model to accelerate data processing and personalized recommendations. The deployment targeted real-time personalization workflows and model-driven customer recommendations for the company’s US-facing platform, aligning the Apolo AI Platform with PowerSetter’s operational goals. The implementation combined Apolo’s GPU Hub for pooled GPU compute with AI model training and inference orchestration, configuring training pipelines, model versioning, and inference endpoints to support continuous personalization. Configuration work emphasized GPU resource scheduling and automated model deployment pipelines consistent with AI/ML operational patterns, enabling both batch and lower-latency inference for recommendation generation. Operational integration tied the Apolo AI Platform directly to PowerSetter’s energy comparison application in the US, feeding processed data into the recommendation engine and closing the loop on personalized user outputs. The implementation scope focused on the customer-facing recommendation function and backend data processing layers, without broader platform replacement language. Outcomes reported in the case study include reduced HPC costs and faster, more real-time personalization, with the project reporting roughly $6,300 per month savings on HPC spend. The narrative reflects a concentrated AI/ML plus GPU infrastructure deployment using Apolo AI Platform to centralize high-performance compute for model training and inference across PowerSetter’s energy comparison workflows. | |
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Synthesis AI | Manufacturing | 355 | $85M | Japan | Apolo | Apolo AI Platform | ML and Data Science Platforms | 2024 | n/a | In 2024 Synthesis AI implemented the Apolo AI Platform in their AWS environment to scale synthetic data model training and MLOps. The deployment is categorized as MLOps/platform migration and moved core ML workflow orchestration from Kubeflow into an Apolo cluster, with the project documented in a vendor case study tied to a San Francisco US region deployment. The Apolo AI Platform implementation focused on cluster provisioning in AWS and orchestration of training pipelines and experiment workflows, aligning with standard MLOps capabilities such as experiment tracking, training orchestration, compute provisioning, and synthetic data pipeline execution. Configuration work included cluster sizing and pipeline templating to support higher experiment concurrency and to standardize pipeline definitions across data science teams. Integrations during the migration explicitly included the transition from Kubeflow and co location in Synthesis AIs AWS account, enabling the Apolo cluster to drive synthetic data model training and end to end MLOps operations. Operational scope covered ML engineering and data science functions, with the Apolo AI Platform serving as the centralized platform for model training and experiment lifecycle management. Governance and rollout followed a platform migration pattern that centralized experiment orchestration and standardized pipeline templates to reduce ad hoc notebook driven runs and to accelerate repeatable training workflows. Per the vendor case study the migration tripled ML experiment throughput in month one and produced six figure compute cost savings, outcomes reported by the vendor for the San Francisco US engagement. |
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