"Amazon Web Services (AWS) has announced significant performance improvements for its Amazon S3 Vectors service in a bid to compensate for surging enterprise AI workload demands. New updates for the cloud object storage service, first unveiled in July this year, mean S3 Vectors now scale up to two billion vectors per index, which the hyperscaler noted is a 40-times increase compared to preview capacity. The service also supports up to 20 trillion vectors per bucket, AWS revealed, and will offer users 2-3x faster query performance."
"The hyperscaler claims the service can reduce the cost of uploading, storing, and querying vectors by around 90%. These cost savings are crucial given the intense enterprise focus on generative AI over the last three years. Vector search is a technique used in generative AI applications to identify similarities between specific data points. According to AWS, vectors are a "numerical representation of unstructured data created from embedding models"."
S3 Vectors now scales up to two billion vectors per index, a 40-times increase over preview capacity, and supports up to 20 trillion vectors per bucket. Query performance improves 2-3x to handle surging enterprise AI workloads and growing data volumes. S3 stores more than 500 trillion objects and hundreds of exabytes of data. The maximum S3 object size will increase to 50TB, enabling storage of high-resolution videos, seismic data, and AI training datasets as single objects. S3 Vectors is positioned as a purpose-built durable vector storage solution that can reduce vector upload, storage, and query costs by around 90%. Vector search uses numerical embeddings produced by embedding models to perform semantic searches and identify similarities between unstructured data points.
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