List of PyTorch Customers
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United States
Since 2010, our global team of researchers has been studying PyTorch 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 PyTorch for Apps Development 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 PyTorch for Apps Development include: Anthropic, a United States based Professional Services organisation with 1000 employees and revenues of $990.0 million, Lightricks, a Israel based Professional Services organisation with 650 employees and revenues of $150.0 million, Hugging Face, a United States based Professional Services organisation with 500 employees and revenues of $50.0 million, Jasper, a United States based Professional Services organisation with 220 employees and revenues of $45.0 million, Layer 6, a Canada based Professional Services organisation with 100 employees and revenues of $30.0 million and many others.
Contact us if you need a completed and verified list of companies using PyTorch, 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 PyTorch 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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Anthropic | Professional Services | 1000 | $990M | United States | PyTorch | PyTorch | Apps Development | 2022 | n/a |
In 2022, Anthropic deployed PyTorch to support its trust and safety engineering and oversight workflows, explicitly using PyTorch as part of its Apps Development tooling to build detection and enforcement models. The deployment focused on embedding machine learning into operational monitoring, enabling model behavior detection and automated enforcement actions while surfacing signals to human reviewers.
The implementation used PyTorch to develop abuse detection models, real-time inference components, and model hardening prototypes aligned with safety requirements. Functional capabilities implemented included monitoring systems for API partner behavior, automated enforcement triggers, internal analyst dashboards for manual review, and machine learning pipelines to surface abuse patterns for research teams, leveraging Python, SQL, Scikit-Learn, TensorFlow, and PyTorch as engineering staples.
Operational coverage spanned trust and safety engineering, research, analyst operations, and platform engineering, with systems designed to ingest user reports and telemetry to produce actionable alerts for analysts and researchers. Integrations centered on API partner monitoring and internal tooling for surfacing abuse patterns to research and operations teams, supporting a feedback loop from incident detection to model retraining and hardening.
Governance and workflow changes included instrumenting enforcement workflows to enforce terms of service and acceptable use policies, creating multi-step review paths that combined automated enforcement with manual analyst oversight. The technical approach emphasized lifecycle instrumentation, model retraining inputs from surfaced abuse patterns, and close collaboration between engineering and operational teams to iterate on safety controls.
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Borealis AI | Professional Services | 150 | $18M | Canada | PyTorch | PyTorch | Apps Development | 2020 | n/a |
In 2020, Borealis AI implemented PyTorch to standardize deep learning research and model development workflows. PyTorch is deployed as the primary framework supporting Borealis AI's Apps Development activities across research and engineering teams.
The deployment emphasizes experimentation and training capabilities, including large scale handling of structured and unstructured datasets, model prototyping, and reproducible training pipelines. Researchers at Borealis AI use PyTorch to develop novel AI solutions, conduct original publishable research, and prepare models for handoff to development teams, with domain-aligned work that includes time series and asynchronous event forecasting when applicable.
Operational coverage includes staff researchers based in Toronto, Waterloo, Vancouver, and Montreal, and the implementation is explicitly aligned with transferring research outputs into the bank's technology capabilities. Governance and workflow changes center on formalizing research to production handoff, aligning machine learning development practices with product and banking experience requirements, and supporting academic publication and conference participation such as NeurIPS, ICLR, ICML, and CVPR.
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Falkonry | Professional Services | 70 | $11M | United States | PyTorch | PyTorch | Apps Development | 2021 | n/a |
In 2021 Falkonry implemented PyTorch as a core element of its Apps Development toolchain to support time series machine learning and production pipeline orchestration. The engagement concentrated on embedding PyTorch-based model development into a Kubernetes-native pipeline environment at the Sunnyvale based professional services firm, supporting ML engineering and data science workflows.
Falkonry deployed Kubeflow Pipelines and MLRun on Kubernetes to automate and scale pipeline orchestration, with PyTorch used for model training and inference steps. The implementation included a custom featurizer for time series data built with the Scattering Transform, implemented in PyTorch using Kymatio and Python, producing translation invariant representations for classification tasks.
Kubeflow Pipelines and MLRun were instrumented on the Kubernetes platform to orchestrate end to end workflows from feature extraction through model training and packaging, integrating PyTorch model artifacts into automated CI like experiment reproduction and deployment pathways. Operational ownership was centered on the companys ML engineering and data science teams, leveraging containerized workloads and pipeline metadata tracking for reproducibility.
Rollout followed pipeline automation into production orchestration, and the project explicitly reported savings of over 200 work hours monthly through the automated pipeline processes. By embedding PyTorch within this Apps Development architecture, Falkonry standardized time series feature engineering and model lifecycle workflows using the stated technologies.
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Professional Services | 500 | $50M | United States | PyTorch | PyTorch | Apps Development | 2022 | n/a |
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Professional Services | 220 | $45M | United States | PyTorch | PyTorch | Apps Development | 2022 | n/a |
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Professional Services | 100 | $30M | Canada | PyTorch | PyTorch | Apps Development | 2019 | n/a |
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Professional Services | 650 | $150M | Israel | PyTorch | PyTorch | Apps Development | 2022 | n/a |
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Professional Services | 100 | $20M | United States | PyTorch | PyTorch | Apps Development | 2023 | n/a |
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Professional Services | 50 | $9M | Ukraine | PyTorch | PyTorch | Apps Development | 2019 | n/a |
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Professional Services | 10 | $1M | United States | PyTorch | PyTorch | Apps Development | 2021 | n/a |
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Buyer Intent: Companies Evaluating PyTorch
- Bds Services Private, a India based Professional Services organization with 600 Employees
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