List of Google TensorFlow Customers
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Since 2010, our global team of researchers has been studying Google TensorFlow 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 TensorFlow 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 TensorFlow for ML and Data Science Platforms include: Elevance Health, formerly Anthem, Inc, a United States based Insurance organisation with 104900 employees and revenues of $171.34 billion, Intel, a United States based Manufacturing organisation with 88400 employees and revenues of $53.10 billion, The Coca-Cola Company, a United States based Consumer Packaged Goods organisation with 69700 employees and revenues of $47.06 billion, OpenAI, a United States based Professional Services organisation with 4000 employees and revenues of $13.10 billion, Airbnb, a United States based Professional Services organisation with 7300 employees and revenues of $11.10 billion and many others.
Contact us if you need a completed and verified list of companies using Google TensorFlow, 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 TensorFlow 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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Airbnb | Professional Services | 7300 | $11.1B | United States | Google TensorFlow | ML and Data Science Platforms | 2018 | n/a |
In 2018 Airbnb began using Google TensorFlow within its ML and Data Science Platforms work to improve photo intelligence for listings. The implementation focused on image classification and object detection workflows applied to listing photos to support listing quality verification and amenity discovery.
For image classification Airbnb retrained a deep neural network, ResNet50, on a dataset of a few million images using an AWS P2.8xlarge instance with Nvidia 8 core K80 GPUs, sending a batch of 128 images to eight GPUs per training step. Training used Google TensorFlow as the backend in a parallel training topology, and the model was compiled after parallelizing to enable successful execution. To accelerate convergence the team initialized model weights with pre trained ImageNet weights loaded via keras.applications.resnet50.ResNet50.
The best ResNet50 model was obtained after three epochs of training which ran for about six hours, after which the model began to overfit and validation performance stopped improving. These training observations guided stop criteria and weight initialization choices for subsequent experiments.
Airbnb also evaluated object detection by running a pre trained Faster R-CNN model trained on the Open Images Dataset and using the TensorFlow Object Detection API for quick evaluations. The detector produced bounding boxes for items such as Window, Door and Dining Table in listing photos, showing the capability to localize amenity objects.
Operational scope covered listing photo pipelines to enable automated verification of host listings and to make it easier for guests to find homes with specific amenity needs. Governance and rollout plans include creating Airbnb specific amenity labels and retraining the Faster R-CNN model on those labels, integrating algorithm detected amenities into listing quality assessment and search workflows.
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Altair Engineering | Professional Services | 3300 | $666M | United States | Google TensorFlow | ML and Data Science Platforms | 2023 | n/a |
In 2023, Altair Engineering implemented Google TensorFlow within its ML and Data Science Platforms tooling to build a user-assisting feature for the HyperWorks simulation environment. The work was performed by the HyperWorks core development team in Troy, MI, and focused on applying sequence modeling to in-application user behavior data to support engineering workflows.
The implementation used deep learning sequence modeling techniques, specifically recurrent neural networks, to model user command history data and predict the next command in a sequence. Development artifacts and runtime components were built using Python and Pandas with model training and inference executed in Google TensorFlow, while the final inference output was surfaced through a GUI component inside the simulation client.
Integration concentrated on coupling TensorFlow model inputs to HyperWorks command history telemetry and embedding the inference endpoint into the simulation GUI so that predictions could be called inline during user sessions. The effort impacted software engineering and product development functions within the HyperWorks team, aligning model training, validation, and deployment workflows with application telemetry and UI event streams.
Governance and delivery followed an iterative prototyping approach led by the HyperWorks core development team during the internship period, with test data derived from historical command sequences and model validation performed against held out sequences. The implementation positioned Google TensorFlow as the ML runtime in Altair Engineering ML and Data Science Platforms for next-command prediction use cases in simulation software.
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Anaplan | Professional Services | 2200 | $1.0B | United States | Google TensorFlow | ML and Data Science Platforms | 2018 | n/a |
In 2018, Anaplan implemented Google TensorFlow to embed machine learning capabilities into its Connected Planning platform as part of a strategic move into ML and predictive planning. The deployment used TensorFlow models running on Google Cloud Machine Learning Engine to add predictive forecasting and optimization capabilities to Anaplan’s planning workflows.
Anaplan engaged Google Cloud Professional Services to identify required datasets and accelerate model development, resulting in two customer prototype builds that used custom TensorFlow models. The implementation focused on predictive planning and inventory optimization workflows, aligning TensorFlow model training and inference with Anaplan’s Connected Planning use cases and complementing existing mathematical optimization capabilities such as Optimizer.
Integrations implemented during the proofs of concept included ingestion of historical sales, inventory, and promotions data, point of sale feeds, external signals such as customer demographics and weather from BigQuery Public Datasets, and cross‑market sales and health data. The first POC ran on 10 percent of a large beverage customer’s U.S. market over two months, and a second POC for a CPG customer evaluated short term forecasting across three brands over six weeks, demonstrating the operational scope was customer program level and focused on planning and supply chain decisioning.
Governance and rollout emphasized rapid proofs of concept to discover high value use cases, a collaborative delivery model with Google Cloud Professional Services to avoid false starts, and an ongoing partnership model to provide continued ML expertise and platform updates. The approach prioritized fast time to market for machine learning solutions and selective operationalization of models that proved relevant to enterprise customers.
Outcomes reported by Anaplan and Google include identification of almost $2 million in achievable savings for a large beverage customer through local retail inventory optimization, described as a 15 percent uplift versus other methods, and more than $4 million in potential improvements for a CPG customer from improved forecasting and inventory decisions. Anaplan stated that running Google TensorFlow models on Google Cloud Machine Learning Engine made the company more competitive in an AI driven market and reduced the risk and cost of tactical decision making for its customers.
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Anthropic | Professional Services | 2500 | $10.0B | United States | Google TensorFlow | ML and Data Science Platforms | 2022 | n/a |
In 2022 Anthropic implemented Google TensorFlow as a core part of its ML and Data Science Platforms architecture to support trust and safety engineering for model oversight. Anthropic Google TensorFlow ML and Data Science Platforms trust and safety engineering work centered on building production-grade model monitoring and abuse detection systems aligned with the company mission to create reliable, interpretable, and steerable AI systems.
The implementation focused on model training and inference pipelines using Google TensorFlow, with dedicated modules for abuse detection model development, real-time monitoring, and automated enforcement orchestration. Development work included supervised detection models, feature extraction and data mining workflows coded in Python and SQL friendly toolchains, and internal dashboards that surface model behaviors to analysts and reviewers. Google TensorFlow was used to iterate training-stage hardening workflows that feed signals back into research teams for model remediation.
Operational integrations were centered on API partner telemetry and internal analyst tooling, with monitoring systems ingesting API usage signals to detect unwanted behaviors, and surfacing flagged instances to analysts for manual review. The scope of operational coverage included trust and safety teams, research groups, and operations teams responsible for abuse response, with internal full stack tooling to analyze user reports and automate pattern detection. Data pipelines and model outputs were structured to support both real-time defenses and batch model retraining cycles.
Governance and process changes emphasized multi-layered review and escalation workflows, automated enforcement actions balanced with analyst-led manual review, and alignment of detection rules with terms of service and acceptable use policies. Rollout prioritized instrumentation for monitoring, integration points to surface abuse patterns to research, and mechanisms to feed findings into training-stage model hardening. The stated objective of these implementations was to detect unwanted model behaviors, prevent disallowed use, and provide transparent oversight pathways without asserting specific outcome metrics.
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Borealis AI | Professional Services | 150 | $18M | Canada | Google TensorFlow | ML and Data Science Platforms | 2020 | n/a |
In 2020 Borealis AI implemented Google TensorFlow as its core ML and Data Science Platforms capability to support a research-to-production machine learning pipeline. The deployment targeted the company research lab and affiliated research staff in Toronto, Waterloo, Vancouver, and Montreal, providing a common deep learning stack to enable theoretical and applied machine learning projects aimed at advancing banking-related algorithmic solutions.
Google TensorFlow was applied to standard platform functions common to ML and Data Science Platforms, including large scale model development, iterative experimentation, model training on massive structured and unstructured datasets, and workflows for deep learning research. Configurations emphasized Python integration and reproducible experiment workflows to support publishable research outputs and prototype handoffs, with explicit focus on time series and event forecasting scenarios referenced in hiring and research priorities.
Operational coverage extended across research scientists, ML engineering, and development teams, with the development organization responsible for transferring research models into production software. The implementation supported collaboration between research and product engineering to interpret organizational needs and to design algorithmic solutions that can drive next generation banking experiences, while keeping research dissemination and conference participation as part of the operational remit.
Governance practices described around the deployment centered on industry best practices for software engineering, reproducibility and research review, and a process to identify and disseminate relevant new AI technologies into the broader technology capability set. The platform usage was tied to a publication and peer review pipeline that enabled researchers to produce academic work and participate in conferences such as NeurIPS, ICLR, ICML, and CVPR.
Explicit benefits stated in the implementation context included access to massive datasets and the tools necessary to build advanced statistical models, opportunities for publication in peer reviewed journals, and support for researchers to influence product capabilities at local and global scale. Google TensorFlow is referenced by Borealis AI as a technical enabler within their ML and Data Science Platforms environment to operationalize research while preserving academic rigor and production readiness.
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Professional Services | 90 | $35M | United States | Google TensorFlow | ML and Data Science Platforms | 2022 | n/a |
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Professional Services | 150 | $50M | United States | Google TensorFlow | ML and Data Science Platforms | 2022 | n/a |
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Insurance | 104900 | $171.3B | United States | Google TensorFlow | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 50 | $5M | United States | Google TensorFlow | ML and Data Science Platforms | 2019 | n/a |
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Manufacturing | 88400 | $53.1B | United States | Google TensorFlow | ML and Data Science Platforms | 2016 | n/a |
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Buyer Intent: Companies Evaluating Google TensorFlow
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Google TensorFlow. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Google TensorFlow for ML and Data Science Platforms include:
- Visa, a United States based Banking and Financial Services organization with 28800 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD Google TensorFlow Coverage
Google TensorFlow is a ML and Data Science Platforms solution from Google.
Companies worldwide use Google TensorFlow, from small firms to large enterprises across 21+ industries.
Organizations such as Elevance Health, formerly Anthem, Inc, Intel, The Coca-Cola Company, OpenAI and Airbnb are recorded users of
Google TensorFlow for ML and Data Science Platforms.
Companies using Google TensorFlow are most concentrated in Insurance, Manufacturing and Consumer Packaged Goods, with adoption spanning over 21 industries.
Companies using Google TensorFlow are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Google TensorFlow across Americas, EMEA, and APAC.
Companies using Google TensorFlow range from small businesses with 0-100 employees - 18.18%, to mid-sized firms with 101-1,000 employees - 13.64%, large organizations with 1,001-10,000 employees - 45.45%, and global enterprises with 10,000+ employees - 22.73%.
Customers of Google TensorFlow include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Google TensorFlow customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.