Best Object Storage Solutions for Amazon SageMaker Unified Studio
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Compare the Top Object Storage Solutions that integrate with Amazon SageMaker Unified Studio as of December 2025
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This a list of Object Storage solutions that integrate with Amazon SageMaker Unified Studio. Use the filters on the left to add additional filters for products that have integrations with Amazon SageMaker Unified Studio. View the products that work with Amazon SageMaker Unified Studio in the table below.
What are Object Storage Solutions for Amazon SageMaker Unified Studio?
Object storage solutions are systems designed to store large amounts of unstructured data, such as multimedia files, backups, logs, and archives, in a highly scalable and accessible manner. These platforms break data into individual objects, each containing the data itself, metadata, and a unique identifier, which makes retrieval and management more efficient. Object storage is typically used for cloud storage environments, where flexibility, scalability, and redundancy are key. It allows organizations to store vast amounts of data with high durability, often offering features like automated data tiering, access controls, and encryption. Object storage solutions are ideal for businesses that need cost-effective, scalable, and secure storage for large datasets or growing volumes of unstructured data. Compare and read user reviews of the best Object Storage solutions for Amazon SageMaker Unified Studio currently available using the table below. This list is updated regularly.
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Amazon S3 Vectors
Amazon
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vector embeddings at scale, delivering purpose-built, cost-optimized vector storage for semantic search, AI agents, retrieval-augmented generation, and similarity-search applications. It introduces a new “vector bucket” type in S3, where users can organize vectors into “vector indexes,” store high-dimensional embeddings (representing text, images, audio, or other unstructured data), and run similarity queries via dedicated APIs, all without provisioning infrastructure. Each vector may carry metadata (e.g., tags, timestamps, categories), enabling filtered queries by attributes. S3 Vectors offers massive scale; now generally available, it supports up to 2 billion vectors per index and up to 10,000 vector indexes per bucket, with elastic, durable storage and server-side encryption (SSE-S3 or optionally KMS).
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