List of Google Kubernetes Engine Customers
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Since 2010, our global team of researchers has been studying Google Kubernetes Engine 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 Kubernetes Engine for Container Service 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 Kubernetes Engine for Container Service include: ANZ Bank, a Australia based Banking and Financial Services organisation with 43094 employees and revenues of $13.40 billion, Resemble AI, a United States based Communications organisation with 2400 employees and revenues of $600.0 million, Major League Baseball (MLB), a United States based Leisure and Hospitality organisation with 862 employees and revenues of $300.0 million, Lightricks, a Israel based Professional Services organisation with 650 employees and revenues of $150.0 million, Jenzabar, a United States based Professional Services organisation with 520 employees and revenues of $102.0 million and many others.
Contact us if you need a completed and verified list of companies using Google Kubernetes Engine, 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 Google Kubernetes Engine 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 |
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ANZ Bank | Banking and Financial Services | 43094 | $13.4B | Australia | Google Kubernetes Engine | Container Service | 2019 | n/a |
In 2019 ANZ Bank implemented Google Kubernetes Engine to provision a containerized platform as part of its Google Cloud smart analytics program, using the Container Service to host customized data services and customer-facing visualization experiences. The initiative supported ANZ’s Institutional Banking division, which operates across 34 markets, and targeted analytics workflows that surface liquidity, risk and cash management insights for institutional customers.
Google Kubernetes Engine was configured to run containerized data services and visualization microservices, providing orchestration, scaling and platform isolation consistent with a Container Service deployment. The implementation enabled rapid deployment of data science models and visualization tooling alongside Google BigQuery for analytics and Google Cloud Composer for data movement orchestration, positioning Google Kubernetes Engine as the execution layer for customer-facing services and internal analytical workloads.
Integrations were explicit and central to the architecture, ANZ used Google BigQuery to perform heavy computational queries on aggregated, de-identified data sets, and Google Cloud Composer to manage pipelines, dependencies and transformations. Google Kubernetes Engine hosted the application components that delivered the visualizations and customized data services consumed by bankers and institutional customers, extending across analytics and front-office functions within the bank.
Governance emphasized secure, compliant handling of regulated financial data while enabling faster insight delivery, reflecting ANZ’s focus on risk-aware cloud adoption. Outcomes reported by ANZ include reducing analysis time for a single table from five days to 20 seconds and delivering banker-facing insights approximately 250 times faster, improvements enabled by the combined use of Google Kubernetes Engine, BigQuery and Cloud Composer.
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Descript | Professional Services | 150 | $50M | United States | Google Kubernetes Engine | Container Service | 2020 | n/a |
In 2020, Descript deployed Google Kubernetes Engine as its Container Service to host containerized components of its public website. The implementation centers on running web workloads in Kubernetes clusters managed on Google Kubernetes Engine, providing a platform for container orchestration and service exposure for site traffic.
Google Kubernetes Engine is used to provide core Container Service capabilities including cluster provisioning, node pool management, workload scheduling, automated scaling, rolling updates, and service discovery. Configuration is centered on Kubernetes manifests and container images with declarative service definitions that enable controlled deployments and standard operational patterns for web services.
Operational ownership sits with engineering and platform teams responsible for cluster configuration, deployment pipelines, and runtime operations for website workloads. Governance emphasizes manifest driven deployments, versioned container images, and staged rollouts to manage change while operating Google Kubernetes Engine for Descript's website.
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Glean | Professional Services | 200 | $32M | United States | Google Kubernetes Engine | Container Service | 2020 | n/a |
In 2020, Glean implemented Google Kubernetes Engine to host its search index and containerized application workloads, using Google Kubernetes Engine as their Container Service for operationalizing search infrastructure. The deployment centers the search index inside the project Google Kubernetes Engine cluster and positions container orchestration as the primary runtime for search and serving components.
Glean's data processing layer uses Google Cloud Dataflow to extract relevant content from multiple workplace knowledge sources, augment records with relevance signals, and push enriched documents into the search index hosted on Google Kubernetes Engine. Google Cloud Dataflow is also used to generate training data at scale for models trained on Google Cloud, creating a pipeline that connects ingestion, enrichment, and model training data generation.
The implementation ties Google Cloud Dataflow pipelines to the Google Kubernetes Engine hosted index, enabling autoscaling of processing and serving workloads as corpus size fluctuates. Operational coverage targets enterprise workplace search and knowledge retrieval workflows across the product, with engineering and data science functions consuming the index and generated training datasets.
Governance is expressed through project-scoped hosting of the search index on Google Kubernetes Engine and pipeline orchestration in Dataflow, which together centralize lifecycle control for indexed content and training data production. Google Kubernetes Engine serves as the Container Service backbone for deployment, scaling, and runtime management of the search stack while Dataflow handles extract, transform, and training data generation responsibilities.
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Professional Services | 520 | $102M | United States | Google Kubernetes Engine | Container Service | 2024 | n/a |
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Professional Services | 650 | $150M | Israel | Google Kubernetes Engine | Container Service | 2022 | DoiT International |
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Leisure and Hospitality | 862 | $300M | United States | Google Kubernetes Engine | Container Service | 2024 | n/a |
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Professional Services | 80 | $8M | Belgium | Google Kubernetes Engine | Container Service | 2020 | n/a |
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Communications | 2400 | $600M | United States | Google Kubernetes Engine | Container Service | 2024 | n/a |
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Buyer Intent: Companies Evaluating Google Kubernetes Engine
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