Shakudo Partners with Geyser Data to Deliver Cost-Effective, Scalable Cold Data Archiving for AI

TUSTIN, Calif. - Rezul -- Shakudo, the pioneering AI and data infrastructure company, today announced a strategic partnership with Geyser Data to offer seamless cold data cloud archiving as part of its AI Operating System. The integration will enable Shakudo's growing customer base to efficiently archive, store, and retrieve massive datasets—at a fraction of the cost and with zero egress fees—using Geyser's Cold Data Cloud Archive.

As AI development accelerates, organizations are grappling with an explosion of data generated during training, testing, and deployment. The need to cost-effectively store and reuse this data without the penalty of high retrieval or egress costs has become mission-critical. With this partnership, Shakudo customers can now offload aging or infrequently accessed data into Geyser's Amazon S3-compatible archive, gaining a scalable, secure, and environmentally conscious solution tailored for modern AI workflows.

"Our mission is to make it easy for AI teams to build and scale their entire stack without the usual infrastructure friction," said Yevgeniy Vahlis, Co-Founder and CEO of Shakudo. "Partnering with Geyser strengthens this commitment by giving our customers a smart, integrated way to manage cold data, without sacrificing agility or facing unpredictable storage bills."

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Geyser's Cold Data Cloud Archive runs on an enterprise-grade tape library-based backend architecture designed for long-term durability, low power consumption, and cost efficiency. By eliminating egress and restore fees, Geyser enables AI companies to retrieve archived data freely whenever it's needed again, whether for retraining, audits, or reproducibility, without rethinking their budgets.

"AI teams don't just need storage, they need infrastructure that respects the full data lifecycle," said Nelson Nahum, CEO of Geyser Data. "We're excited to work with Shakudo to bring our Cold Data Cloud Archive to more innovators who want to move fast, stay compliant, and reduce costs without compromises."

Shakudo's all-in-one AI Operating System enables data science and engineering teams to transition from notebook to production with zero DevOps overhead, integrating 200+ best-in-class tools across the data and AI stack. The platform supports a wide array of technologies, including Apache Airflow, dbt, and Dagster for orchestration; Spark, Dask, and Ray for distributed computing; and frameworks like TensorFlow, PyTorch, and NVIDIA Triton for machine learning and model deployment. With seamless connections to cloud storage services such as Amazon S3, Snowflake, and BigQuery, Shakudo streamlines everything from ingestion to inference.

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By partnering with Geyser as its cold archive partner, Shakudo extends this flexibility beyond active compute to deep storage—providing a true end-to-end solution for AI-driven businesses that need scalable, affordable, and easily retrievable data infrastructure.

To learn more, visit http://www.shakudo.io and http://www.geyserdata.com

About Shakudo
‍Shakudo's operating system for data and AI provides enterprises with production-ready AI and data infrastructure that combines the flexibility of custom-built stacks with the reliability of managed platforms. The platform enables organizations to leverage best-in-class tools while maintaining full control over their data and compute resources.

About Geyser Data
Geyser Data is a leading provider of data archiving and protection solutions, offering secure, scalable, and cost-effective Cold Data Cloud Archive for businesses worldwide. Designed for organizations that need to archive large volumes of cold data without the expense and complexity of managing hardware or relying on traditional cloud providers, Geyser Data delivers a flexible, cloud-connected solution with no retrieval fees or hidden costs. Through strategic partnerships and an unwavering commitment to innovation, Geyser Data empowers businesses to protect their data, lower storage costs, and simplify long-term archiving.