Qdrant, the leading provider of high-performance, composable vector search, announced three enterprise capabilities for Qdrant Cloud: GPU-accelerated indexing, Multi-AZ clusters, and audit logging. Together, these address the performance, availability, and compliance requirements that enterprise teams need for production vector search — especially as AI workloads write continuously, require always-on retrieval, and demand accountability for every decision made on retrieved context.

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“Pair (GPU-accelerated HNSW construction) with multi-AZ replication and audit logging, and enterprise teams have everything they need to run Qdrant in production for their most critical workloads.” – Andre Zayarni, CEO and Co-Founder

As AI systems continue to become more mission critical, vector search faces new demands: indexing that keeps pace with large write loads, maintaining high availability, and enabling audit trails for autonomous systems making decisions on retrieved context. Qdrant Cloud now addresses these challenges.

“GPUs aren’t just for model inference. They’re for indexing too. We’ve supported GPU-accelerated HNSW construction in open source since v1.13, and now it’s available in Qdrant Cloud,” said Andre Zayarni, CEO and Co-Founder of Qdrant. “Pair that with multi-AZ replication and audit logging, and enterprise teams have everything they need to run Qdrant in production for their most critical workloads.”

GPU-accelerated indexing delivers up to 4x faster HNSW index builds on dedicated GPUs in Qdrant Cloud, based on Qdrant benchmarks. Customers can add GPUs to existing clusters for high-volume indexing bursts. Available today on AWS, with additional cloud providers and regions on the roadmap.

Multi-AZ clusters replicate data across three availability zones within a region through cross-AZ replication — not failover. If an availability zone goes down, reads and writes continue from the surviving zones with no failover delay and no customer action required. Available on the Premium Multi-AZ tier, offering up to 99.95% uptime SLAs.

Audit logging captures all operations performed through the Qdrant API: queries, upserts, deletes, collection management, and snapshot operations. Each entry is structured JSON with user and API key attribution, timestamp, target collection, and result of the action (allowed or denied). When an autonomous system acts on retrieved context, audit logging provides the trail showing which service queried which collection, when, and whether the request was authorized. Retention is configurable; for long-term needs, logs can be downloaded via the API and stored externally. Audit logging is available on all paid Qdrant Cloud clusters.

Enterprise teams evaluating vector search typically ask three questions:

• Can it keep up? High-write workloads (dynamic catalogs, agentic memory, real-time recommendations) need indexing that keeps pace.
• Can it stay up? SRE teams and procurement require multi-AZ availability before signing off on mission-critical infrastructure.
• Can we audit it? Compliance and security teams need a trail showing who accessed what and when, especially as AI agents make more autonomous decisions.