UltiHash

UltiHash: The Object Storage for AI + Analytics

UltiHash is an object storage platform purpose-built to supercharge AI and analytics workloads. It combines a modern data lakehouse-friendly foundation with Kubernetes-native deployment, S3-compatible APIs, and advanced data-management features to deliver high throughput, cost-efficient storage for AI data and large-scale analytics.

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How UltiHash Helps

• Reduces storage costs by enabling byte-level deduplication and efficient data management across AI and analytics datasets.
• Speeds up data access with high-throughput architecture suitable for AI training, inference, and real-time analytics.
• Provides flexible deployment across cloud, on-premises, or hybrid environments via Kubernetes.
• Integrates with common data processing and analytics tools through an S3-compatible API, plus open table formats (Iceberg, Delta Lake, Hudi).
• Supports policy-based access management and data resiliency features to meet governance and reliability needs.

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Key Use Cases

• Generative AI data pipelines and large language model (LLM) workflows
• Retrieval-Augmented Generation (RAG) data stores for AI apps
• Computer vision, self-driving vehicle data, and sensor data for AI/ML workloads
• Global speech-to-text and other AI-powered data processing pipelines
• High-throughput analytics and data lakehouse architectures

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How It Works

• Object storage with a Kubernetes-native architecture that can run cloud, on-premises, or hybrid.
• S3-compatible API for easy integration with processing engines (Python, Airflow, Spark, Flink, Kafka, Trino, Presto).
• Metadata-aware storage layer that supports open table formats (Iceberg, Delta Lake, Hudi) for lakehouse-style querying.
• Byte-level deduplication to minimize redundant data and reduce overall storage footprint (up to 60% reduction noted in materials).
• Efficient, scalable deletion with reference accounting to reclaim space immediately when fragments are no longer used.
• Erasure coding (Reed-Solomon) for data resiliency (coming soon) to guard against data loss.
• Policy-based access management to enforce granular data access controls.

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Architecture Highlights

• S3-compatible API for broad compatibility and easy migration.
• Kubernetes-native deployment for cloud, on-prem, and hybrid setups.
• High-throughput design optimized for AI/ML and analytics workloads without adding compute overhead from deduplication.
• On-demand, scalable storage that supports petabytes and beyond with flexible storage classes.

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Security & Compliance

• Built-in access management with granular policies to control who can access which datasets.
• Data sovereignty and governance features aligned with enterprise requirements.
• High-throughput, efficient operations to minimize exposure windows during data management tasks.

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Industry Use Cases

• Generative AI + LLM data storage and processing
• RAG-based AI applications requiring fast, scalable data retrieval
• AI-ready data lakehouse environments with open table format support
• Self-driving vehicle data and large-scale computer vision datasets
• Global speech-to-text and other AI-enabled content processing workflows

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Tech Stack & Capabilities

• Object storage with Kubernetes-native deployment
• S3-compatible API for compatibility with tools like Python, Airflow, Spark, Flink, Kafka, Trino, Presto
• Metadata layer supporting Iceberg, Delta Lake, and Hudi
• Byte-level deduplication to reduce storage needs
• Efficient delete operations with immediate space reclamation
• Erasure coding for data resiliency (Coming Soon)
• Policy-based access management for granular security
• High-throughput architecture optimized for AI + analytics workloads

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Start Today

• Cloud/on-prem/hybrid deployment options with Kubernetes
• No-signup required for evaluation in some configurations (varies by deployment)
• Learn more about storage savings, integration, and security from UltiHash whitepapers and docs

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Safety and Legal Considerations

• UltiHash emphasizes data security, privacy, and governance; ensure appropriate access controls and compliance with internal policies and external regulations when storing sensitive data.

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Core Features

• Kubernetes-native deployment across cloud, on-prem, and hybrid environments
• S3-compatible API for easy integration with processing and analytics tools
• High-throughput object storage optimized for AI + analytics workloads
• Byte-level deduplication to reduce total storage footprint (up to ~60% reduction)
• Open table format support (Iceberg, Delta Lake, Hudi) for lakehouse analytics
• Efficient, immediate space reclamation on data deletion
• Policy-based access management for granular data security
• Scalable, resilient storage designed for AI data pipelines