Ceph is synonymous with block, file, and object storage at petabyte to exabyte scale, powering high performance computing, AI/ML workloads, and enterprise IT environments. As generative AI workloads accelerate, these users increasingly need more than traditional storage—they need native support for embedding data and nearest neighbor search to enable retrieval augmented generation at scale.
Today, this capability is often delivered through standalone vector databases, introducing new infrastructure, unfamiliar interfaces, and significant operational overhead. Operations teams and developers alike face the cost and complexity of deploying, securing, and maintaining yet another system alongside their existing storage platforms.
Ceph’s journey toward nearest neighbor search aims to change that. Ongoing research into vector storage formats, libraries, and databases is bringing semantic search closer to where enterprise data already lives. By exposing vector search capabilities through S3 Vectors, Ceph enables AI practitioners to use familiar cloud native tools, SDKs, and workflows, without introducing a separate vector database.
In this webinar, we will explore how LanceDB libraries power a vector search implementation in Ceph, delivered through S3 Vectors API actions. Attendees will see how Ceph can provide out of the box, billion scale, multi tenant nearest neighbor search that meets the needs of most RAG workloads while preserving the unified, software defined storage model enterprises already trust.
AI systems are only as powerful as the data foundations that support them. As applications increasingly rely on vectors, tables, graphs, and long-lived datasets, object storage must evolve to do more than simply hold data—it must actively support how AI applications coherently access, move, and reuse it over time.
In this webinar we will examine the architectural patterns shaping modern AI-ready object storage. We will explore how object storage is designed to support emerging AI data types alongside traditional unstructured data, while remaining scalable, durable, and cost efficient at cloud scale. Central to this evolution is tighter integration between data lifecycle management and application workflows, enabling data to flow seamlessly from ingestion and training to inference, governance, and long-term retention.
Attendees will gain practical approaches to building AI-ready storage systems that deliver scale, efficient access, and long-term data reuse.
