List of Snowflake ML Customers
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Since 2010, our global team of researchers has been studying Snowflake ML 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 Snowflake ML 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 Snowflake ML for ML and Data Science Platforms include: Storio group, a Netherlands based Retail organisation with 1109 employees and revenues of $401.0 million, Jahez International Company for Information Systems Technology, a Saudi Arabia based Retail organisation with 500 employees and revenues of $325.0 million, Scene+, a Canada based Professional Services organisation with 60 employees and revenues of $8.0 million and many others.
Contact us if you need a completed and verified list of companies using Snowflake ML, 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 Snowflake ML 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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Jahez International Company for Information Systems Technology | Retail | 500 | $325M | Saudi Arabia | Snowflake | Snowflake ML | ML and Data Science Platforms | 2024 | n/a |
In 2024, Jahez International Company for Information Systems Technology implemented Snowflake ML within its ML and Data Science Platforms footprint to productionize real time Estimated Time of Arrival ETA and logistics models for delivery optimization in Saudi Arabia. The deployment used Snowflake ML Model Serving running in Snowpark Container Services to host containerized inference workloads.
The implementation centralized model serving and inference, configuring real time ETA prediction pipelines and logistics optimization models that support courier order assignment. Snowflake ML enabled model packaging and containerized deployment with automated scaling to reduce deployment delays and maintain continuous model serving.
Operational coverage targeted delivery operations across Jahez Group's Saudi Arabia logistics network, impacting courier dispatch and order assignment workflows and integrating with existing delivery orchestration processes. The Snowpark Container Services architecture delivered sub one second online inference, supporting real time decisioning at the point of dispatch.
Governance and rollout emphasized productionization best practices within the ML and Data Science Platforms environment, including controlled model serving, automated scaling, and rollback capable deployments to manage operational risk. The Snowflake ML implementation delivered improved courier order assignment, reduced deployment delays through auto scaling, and yielded reported cost and efficiency improvements.
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Scene+ | Professional Services | 60 | $8M | Canada | Snowflake | Snowflake ML | ML and Data Science Platforms | 2024 | n/a |
In 2024, Scene+ implemented Snowflake ML to productionize CRM and loyalty models for its Canadian loyalty program. The deployment used Snowflake ML within the ML and Data Science Platforms category to centralize model development, versioning and production scoring for more than 30 models.
Development and operational tooling centered on Snowflake ML components, including notebooks for exploratory and reproducible development, a feature store to enforce consistent feature engineering, a model registry for version control and deployment metadata, and ML observability for monitoring model behavior in production. This configuration removed the need for cross-platform data movement by keeping training, feature materialization and scoring inside the Snowflake environment, and pipelines were configured to support repeatable training and promotion workflows.
Integration scope focused on CRM and loyalty data pipelines, with production scoring surfaced to loyalty operations and analytics teams supporting campaign and member lifecycle use cases across Canada. Operational coverage encompassed model development, validation and production hosting for loyalty use cases rather than exporting data and models to external ML tooling.
Governance leveraged the model registry and ML observability to enforce version control, deployment approvals and monitoring workflows during staged rollouts to production. The deployment reduced time-to-production by over 60% and lowered costs by more than 35% across 30+ models, outcomes reported by the implementation team.
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Storio group | Retail | 1109 | $401M | Netherlands | Snowflake | Snowflake ML | ML and Data Science Platforms | 2024 | n/a |
In 2024, Storio group implemented Snowflake ML to build a scalable MLOps platform supporting personalized product recommendations, aligning the company with the ML and Data Science Platforms category for production-grade model development and monitoring. The deployment of Snowflake ML centralized model training, evaluation, and lineage capture within Snowflake's execution environment to reduce handoffs between feature engineering and production scoring.
The implementation leveraged Snowflake ML capabilities including ML Observability, Dynamic Tables and ML Lineage to operationalize model lifecycle workflows. Dynamic Tables were used to automate feature materialization and refresh for inference, ML Lineage provided traceability for model artifacts and feature provenance, and ML Observability powered automated dashboards for live model evaluation and feature-drift monitoring.
Operational coverage extended across Storio group's European operations and focused on personalization workflows for photo-product recommendations, with the data science function and product personalization pipelines as primary consumers. Model governance and traceability were reinforced through lineage and observability instrumentation, enabling controlled production rollouts and continuous monitoring of model behavior.
The initiative yielded faster production cycles and freed data scientists to concentrate on value-driving work by automating dashboards for live evaluation and drift detection, while Snowflake ML served as the centralized ML and Data Science Platforms environment for ongoing model development and observability.
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