List of Anaconda Enterprise Platform Customers
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Since 2010, our global team of researchers has been studying Anaconda Enterprise Platform 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 Anaconda Enterprise Platform 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 Anaconda Enterprise Platform for ML and Data Science Platforms include: Citigroup, a United States based Banking and Financial Services organisation with 230000 employees and revenues of $81.09 billion, PNC Bank, a United States based Banking and Financial Services organisation with 53859 employees and revenues of $20.81 billion, National Grid USA, a United States based Utilities organisation with 18177 employees and revenues of $15.20 billion and many others.
Contact us if you need a completed and verified list of companies using Anaconda Enterprise Platform, 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 Machine Learning software purchases.
The Anaconda Enterprise Platform 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 Machine Learning 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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Citigroup | Banking and Financial Services | 230000 | $81.1B | United States | Anaconda | Anaconda Enterprise Platform | ML and Data Science Platforms | 2018 | n/a |
In 2018, Citigroup implemented Anaconda Enterprise Platform as an ML and Data Science Platform. The Anaconda Enterprise Platform was provisioned to standardize Python and R analytics workflows and to provide a centralized runtime for reproducible model development across enterprise data science teams.
The deployment emphasized core Anaconda Enterprise Platform capabilities including environment and package management, hosted interactive notebooks, model packaging and artifact repositories, job scheduling, and model serving endpoints. Workloads were configured to support PySpark driven distributed processing and to enable scripted R workflows alongside Python, using conda-based environment isolation and dependency management to enforce reproducibility.
Integrations tied the Anaconda Enterprise Platform into Citigroup's data stack, including Dataiku and Arcadia for data preparation and visualization, and Hive and HDFS for persistent data access. The platform was used as the execution and packaging layer for models developed in Python and R, and it interoperated with PySpark for scalable compute across large datasets.
Governance focused on standardized environment templates, model versioning and access controls within the Anaconda Enterprise Platform, and centralized artifact governance to support model lifecycle processes. Operationally, the implementation oriented analytics teams toward shared workflows and repeatable deployments, aligning data science development, packaging, and operationalization under a single ML and Data Science Platform.
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National Grid USA | Utilities | 18177 | $15.2B | United States | Anaconda | Anaconda Enterprise Platform | ML and Data Science Platforms | 2017 | n/a |
In 2017, National Grid USA implemented Anaconda Enterprise Platform to provide an enterprise-ready environment for the ETO analytics team, aiming to support a more sophisticated, risk-based analytical methodology. The deployment focused on an ML and Data Science Platform approach that preserved the team’s preference for open source Python and the PyData ecosystem while meeting strict internal IT governance requirements.
Anaconda Enterprise Platform was configured to centralize Python environment and package management, enabling IT to verify and govern third-party packages used by analysts. The implementation emphasized reproducible project workflows and controlled runtime environments, with role-based access and environment isolation to align developer productivity and operational compliance for data science workloads.
Operational coverage was concentrated within National Grid USA’s ETO analytics function, with close coordination between the analytics team and IT governance to enforce package approval and license controls. Paid Anaconda subscriptions were procured to provide 24/7 support, a requirement the team deemed essential for sustained production analytics operations.
Governance and process adjustments included formalized package verification workflows and approval gates to satisfy internal IT policies, and centralized management of Python libraries to reduce unvetted dependencies. Anaconda Enterprise Platform met National Grid USA’s Python governance needs and supported the ETO analytics team’s use of open source data science tooling without compromising IT control.
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PNC Bank | Banking and Financial Services | 53859 | $20.8B | United States | Anaconda | Anaconda Enterprise Platform | ML and Data Science Platforms | 2017 | n/a |
In 2017, PNC Bank implemented Anaconda Enterprise Platform as the core tooling to establish a centralized data science competency center, supporting its operations across 19 states. The deployment positioned Anaconda Enterprise Platform within PNC Bank as an ML and Data Science Platform to enable standardized data science and machine learning capabilities across the institution, a story presented by Data Manager Ann Manchella and Data Scientist Jim Ogle at AnacondaCON.
The implementation focused on consolidating interactive development and reproducible environments, including notebook-based workflows, package and environment management, and model packaging for operational reuse. Anaconda Enterprise Platform was configured to provide role-based access controls, environment versioning, and shared repositories for libraries and artifacts, aligning with standard ML and Data Science Platform capabilities for collaboration and reproducibility.
Operational scope targeted enterprise-wide adoption to embed data science workflows into business functions across the bank, with the competency center serving as the governance and enablement layer. Governance workstreams emphasized standardized development-to-deployment workflows, access governance, and competency-building processes to onboard data scientists and scale model delivery while maintaining consistent environment configurations and reproducibility.
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Buyer Intent: Companies Evaluating Anaconda Enterprise Platform
- Amity University, a India based Education organization with 3000 Employees
- Unimrkt Research, a India based Professional Services company with 500 Employees
- Landis+Gyr, a Switzerland based Manufacturing organization with 6347 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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