List of Pachyderm Customers
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Since 2010, our global team of researchers has been studying Pachyderm 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 Pachyderm 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 Pachyderm for ML and Data Science Platforms include: Lockheed Martin, a United States based Aerospace and Defense organisation with 121000 employees and revenues of $71.04 billion, LogMeIn Hungary, a Hungary based Professional Services organisation with 800 employees and revenues of $95.0 million, Agbiome, a United States based Professional Services organisation with 82 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Pachyderm, 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 Pachyderm 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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Agbiome | Professional Services | 82 | $10M | United States | Hewlett Packard Enterprise | Pachyderm | ML and Data Science Platforms | 2020 | n/a |
In 2020, AgBiome implemented Pachyderm to convert genomics and microbial analysis workflows into scalable, repeatable end to end pipelines so scientists could focus on research rather than infrastructure. The deployment positioned Pachyderm within AgBiome's ML and Data Science Platforms estate to operationalize bioinformatics and genomics workflows for research use cases.
Pachyderm centralized pipeline definitions and enabled data centric ML and bioinformatics pipelines with integrated data versioning and built in reproducibility across large genome datasets. Functional capabilities implemented included pipeline orchestration, automated workflow execution, provenance tracking for inputs and outputs, and standardized workflow artifacts to support re runs and validation. The architecture emphasized reproducible data repositories and pipeline metadata to maintain lineage for complex analyses.
Operational coverage targeted research and development scientists running microbial analysis and genomics experiments, consolidating disparate analysis steps into Pachyderm pipelines and versioned data stores. Governance was adjusted toward pipeline standardization and reproducible data practices, with teams enforcing data versioning and consistent pipeline execution as part of routine research operations. The implementation delivered reproducibility across large genome datasets and accelerated analysis while improving operational efficiency.
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Lockheed Martin | Aerospace and Defense | 121000 | $71.0B | United States | Hewlett Packard Enterprise | Pachyderm | ML and Data Science Platforms | 2023 | n/a |
In 2023, Lockheed Martin integrated Pachyderm into its AI Factory to enable reproducible, production-scale ML pipelines for defense and aerospace applications in the United States. Pachyderm, now part of Hewlett Packard Enterprise, was delivered as part of HPE's AI at scale offering and acts as a controlled execution and data versioning layer for model development and deployment.
The implementation emphasized pipeline orchestration, data lineage, versioned data repositories, and model traceability, capabilities aligned with ML and Data Science Platforms. Pachyderm provided automated pipeline execution and end to end provenance tracking for data scientists and MLOps engineers, which helped standardize AI tooling across contested environments.
Operational coverage centered on AI Factory workflows supporting engineering and data science functions across Lockheed Martin defense programs in the United States. Governance changes introduced consistent model provenance and reproducibility practices to improve trust and traceability of models, and the rollout was delivered through HPE's bundled AI at scale approach.
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LogMeIn Hungary | Professional Services | 800 | $95M | Hungary | Hewlett Packard Enterprise | Pachyderm | ML and Data Science Platforms | 2019 | n/a |
In 2019, LogMeIn Hungary implemented Pachyderm to parallelize and automate data preparation for speech recognition and natural language processing, with the project categorized under ML and Data Science Platforms. The deployment targeted data engineering and MLOps workflows, using Pachyderm to accelerate model training pipelines and reduce manual preprocessing bottlenecks.
The implementation focused on pipeline automation and reproducible data engineering, leveraging Pachyderm to orchestrate containerized data pipelines, capture data lineage, and enable repeatable dataset versioning for voice and NLP workloads. Functional capabilities implemented included automated data ingestion and transformation, parallelized preprocessing across datasets, and pipeline orchestration to support iterative model development.
Work was executed by LogMeIn's AI Center of Excellence in Israel and centered on operationalizing voice and NLP model training. The scope emphasized ML pipeline automation to improve time-to-production for speech recognition and NLP models, and the technical stack was deployed in conjunction with Hewlett Packard Enterprise infrastructure to support scale and compute requirements.
Governance changes included standardizing pipeline definitions and enforcing reproducible dataset versioning, enabling engineering teams to run consistent training pipelines. Outcomes reported from the implementation include a reduction in a key data-processing step from seven weeks to roughly seven hours, improving the cadence of model iterations for voice and natural language processing projects.
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