List of Google Compute Engine A3 Customers
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Since 2010, our global team of researchers has been studying Google Compute Engine A3 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 Google Compute Engine A3 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 Google Compute Engine A3 for ML and Data Science Platforms include: Character AI, a United States based Professional Services organisation with 90 employees and revenues of $35.0 million, Tabnine, a Israel based Professional Services organisation with 100 employees and revenues of $10.0 million, Contextual AI, a United States based Professional Services organisation with 50 employees and revenues of $5.0 million and many others.
Contact us if you need a completed and verified list of companies using Google Compute Engine A3, 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 | Insight Source |
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Character AI | Professional Services | 90 | $35M | United States | Google Compute Engine A3 | ML and Data Science Platforms | 2023 | n/a | In 2023, Character AI deployed Google Compute Engine A3 as part of a Google Cloud partnership to scale its conversational AI platform within its ML and Data Science Platforms footprint. The implementation uses Google Compute Engine A3 VMs with NVIDIA H100 accelerators alongside Google Cloud TPUs to support model development and production serving for large language models, explicitly targeting ML/model training and inference workloads. The architecture combines Google Compute Engine A3 instances and TPU capacity into distributed training and inference clusters, with standard ML components such as training pipelines, model parallelism and sharded checkpointing, and inference serving layers. Google Compute Engine A3 is used for GPU-accelerated training and inference, while TPUs contribute to high throughput training runs, creating a hybrid accelerator fabric to balance development iteration and production latency needs. The deployment is implemented on Google Cloud infrastructure in the United States and is integrated into Character AI s conversational platform to enable rapid scale to millions of users. Operational scope focuses on ML/model training and inference workloads across the platform rather than specific departmental modules, with cloud-native orchestration and autoscaling patterns implied by the use of A3 VMs and TPUs. Governance centered on production serving clusters and controlled model rollouts to support new feature launches, aligning release workflows with accelerator provisioning and capacity planning. The partner announcement states the configuration improved training and inference efficiency and expanded capacity for new features, enabling faster development cycles and larger scale serving without specifying quantitative metrics. | ||
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Contextual AI | Professional Services | 50 | $5M | United States | Google Compute Engine A3 | ML and Data Science Platforms | 2023 | n/a | In 2023, Contextual AI began using Google Compute Engine A3 as part of its ML and Data Science Platforms strategy with Google Cloud named as the preferred cloud provider. The announcement specifies use of GPU VMs including Google Compute Engine A3 (H100) and A2 (A100) for building and training enterprise large language models, with a focus on retrieval augmented generation enabled enterprise AI use cases in the United States and targeting ML model training and inference for enterprise applications. Contextual AI configures Google Compute Engine A3 for scalable model development workflows and production inference serving, implementing standard ML platform capabilities such as distributed GPU training clusters, data preprocessing and feature pipelines, fine tuning of LLMs, and retrieval augmented generation pipelines. The deployment emphasizes autoscaling compute to support iterative model development and deployment, and uses orchestration of training jobs to manage GPU resource utilization across development and production workloads. The implementation is centered on Google Cloud infrastructure and aligns inference endpoints with enterprise data retrieval layers to support RAG workflows without naming external vendors. Operational scope is the United States and the initiative is positioned to support enterprise application use cases by centralizing model training and inference on Google Compute Engine A3 while instituting controlled rollout and orchestration practices to govern model promotion into production. | ||
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Tabnine | Professional Services | 100 | $10M | Israel | Google Compute Engine A3 | ML and Data Science Platforms | 2025 | n/a | In 2025, Tabnine deployed Google Compute Engine A3 to run real time inference for its AI assisted code completion product. The deployment uses Google Compute Engine A3 A3 High machine types with NVIDIA H100 GPUs to support production inference workloads that serve developer workflows in Israel and globally. Tabnine configured Google Kubernetes Engine clusters that include smaller A3 High machine types, containerized model servers, and GPU provisioning to host low latency model endpoints. The implementation leverages node pools sized for H100 GPU capacity and standard inference serving patterns common to ML and Data Science Platforms, including automated scaling of replica counts to respond to developer request traffic. Integrations center on Google Kubernetes Engine and Google Cloud GPUs as the primary compute and orchestration stack, with model serving and container orchestration aligned to cloud native operational practices. Operational coverage spans engineering and product teams responsible for runtime model management and developer experience globally and within Israel. Governance focused on configuration controls for node pool sizing and model placement to manage latency tradeoffs, and operational runbooks for GPU instance lifecycle and scaling. Tabnine reports that moving inference to the smaller A3 H100 configurations delivered an approximate 36% reduction in real time code assist model latency, an explicit ML performance outcome tied to the Google Compute Engine A3 deployment in its ML and Data Science Platforms architecture. |
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