Uploaded July 2026 | Updated September 2026, 2 weeks ago
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.
Presented by Kyle Bader, IBM; Prashanth Rao, LanceDB; Christine Bassani, Seagate
Read Q&A Blog: snia.org/blog/2026/ceph-object-storage-meets-vector-search-qa
Learn More:
• SNIA Website: snia.org
• SNIA Educational Library: snia.org/library
• X: twitter.com/SNIA
• LinkedIn: linkedin.com/company/snia
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.
Presented by Kyle Bader, IBM; Prashanth Rao, LanceDB; Christine Bassani, Seagate
Read Q&A Blog: snia.org/blog/2026/ceph-object-storage-meets-vector-search-qa
Learn More:
• SNIA Website: snia.org
• SNIA Educational Library: snia.org/library
• X: twitter.com/SNIA
• LinkedIn: linkedin.com/company/snia
