Milvus vector database with HPE Alletra Storage MP X10000

[PiotrDrag]

In the era of AI and data-driven innovation, finding relevant information quickly is a game changer. Traditional databases often struggle with the complexities of semantic search, real-time processing, and high-dimensional data retrieval. This is where the Milvus Vector Database, integrated with HPE Alletra Storage MP X10000, transforms the game. Designed for extreme scalability, lightning-fast performance, and advanced similarity search, this solution empowers applications from recommendation systems to multimodal search.

What is Milvus vector database

AI adoption is exploding, and so is the volume of vectors—high-dimensional numerical representations of images, text, audio, and more. These vectors must be indexed and queried in real time to power recommendation engines, RAG pipelines, and anomaly detection services. Traditional databases and storage systems struggle with the high-throughput, low-latency demands of this workload, creating bottlenecks that slow innovation and inflate costs

Milvus is a popular open-source vector database designed specifically for managing, searching, and analyzing large-scale embedding vectors generated by AI and machine learning models. It excels at handling high-dimensional data such as images, audio, video, and natural language text by enabling fast and accurate similarity search and retrieval. Milvus supports massive datasets and provides efficient indexing and querying capabilities, making it a popular choice for applications in recommendation systems, computer vision, natural language processing, and other AI-driven fields.

When deployed on Kubernetes and backed by HPE Alletra Storage MP X10000, it gains an ultrafast, log-structured key–value engine that delivers massive IOPS for metadata and high throughput for vector segments. The disaggregated compute-and-storage model lets AI teams scale independently, while GPU Direct RDMA support keeps query latencies sub-second even at billion-vector scale

Simplified diagram of Milvus deployment with HPE Alletra MP X10000 object storage

Solution architecture

Milvus with HPE Alletra Storage MP X10000 provides a fast, scalable, and secure solution — so data scientists can focus on building smarter applications instead of wrestling with infrastructure.

Main elements of the solution architecture:

• Milvus supplies distributed indexing, hybrid search, multitenancy, replication, and optional GPU acceleration
• Kubernetes orchestrates the environment, enabling elastic scaling and simplified operations across on-prem, hybrid, or multicloud deployments
• HPE Alletra Storage MP X10000 provides cloud-native, ultra-fast object storage with a log-structured key-value engine optimized for both small metadata I/O and large sequential vector segment reads.
• Compute and storage are disaggregated: vector data is offloaded to HPR Alletra MP object storage, reducing reliance on costly local disks and allowing each layer to scale independently.
• Features such as GPU Direct RDMA, dynamic data placement, and disklet-based architecture keep query latency sub-second even at massive scale.

 

 Simplified solution diagram of Milvus with HPE Alletra MP X10000 object storage

The combined technology stack integrates seamlessly with HPE GreenLake and HPE Private Cloud AI, enabling unified management, monitoring, and consumption-based pricing for enterprise workloads. It delivers enterprise-grade security and compliance through robust data protection, encryption, and lifecycle management, complemented by Kubernetes RBAC and Milvus multi-tenancy capabilities. This ensures organizations can securely manage and scale their AI and analytics initiatives while maintaining operational efficiency and regulatory adherence.

More details can be found in this solution brief.

Benefits of joint solution

This joint solution empowers organizations with low-latency, high-throughput access for real-time inference, ensuring rapid and efficient processing of AI workloads. It offers elastic, cost-efficient scaling by offloading cold vectors to object storage, optimizing performance and reducing expenses. Enterprise-grade durability, robust encryption, and comprehensive lifecycle management deliver strong compliance and data protection. Additionally, the seamless integration with HPE GreenLake and HPE Private Cloud AI simplifies operations through a consumption-based model, enabling a streamlined and future-proof AI infrastructure.

Using HPE Alletra Storage MP X10000 with Milvus Vector Database provides several key benefits for AI and similarity search workloads:

1. High Performance and Faster Time to Insight – The all-flash NVMe SSD-based S3 object storage in HPE Alletra MP X10000 accelerates Milvus’s distributed query processing, enabling faster read speeds and improving overall query performance for vector similarity searches.
2. Extreme Scalability and Right Provisioning – The disaggregated, scale-out architecture of HPE Alletra MP X10000 allows independent scaling of compute and storage. This complements Milvus’s horizontally scalable architecture, ensuring organizations can match capacity and performance to dynamic AI workloads efficiently.
3. Enhanced Security Features – HPE Alletra integrates advanced enterprise-grade security (TLS encryption, object lock immutability, erasure coding, triple parity RAID) that pairs with Milvus’s RBAC, audit logging, data masking, and tokenization for robust data governance.
4. TCO Optimization and Data Control – With cloud-like consumption models and pay-as-you-go pricing, HPE Alletra MP X10000 reduces total cost of ownership while giving full visibility and control over where data resides.
5. Simplified Management – Unified storage management, non-disruptive upgrades, and rich data services simplify operation of large-scale Milvus environments, reducing operational complexity.
6. Future-Proof Data Strategy – HPE Alletra’s object storage intelligence enhances Milvus’s similarity search capabilities, ensuring readiness for future AI and analytics requirements without major infrastructure changes.

Use cases

The following use cases are powered by Milvus's high-performance, distributed vector database capabilities and complemented by HPE Alletra Storage MP X10000's highly scalable, all-flash NVMe-based object storage—resulting in high-speed query processing, extreme scalability, advanced indexing methods, and enterprise-grade security.

1. Retrieval-Augmented Generation (RAG) – enhancing large language models (LLMs) and AI applications by incorporating external data sources for more accurate and contextually relevant results.
2. Image Similarity Search – identifying visually similar images or objects within large image repositories.
3. Recommendation Systems – matching user behaviors and content features with similar profiles or items to provide personalized recommendations.
4. Multimodal Similarity Search – running searches across diverse data types simultaneously, such as text, image, and video.
5. Text/Semantic Search – searching for semantically similar text across massive document collections, going beyond simple keyword matches.
6. Anomaly Detection – identifying data points, events, or observations that deviate significantly from expected patterns.
7. Video Similarity Search – finding similar videos, scenes, or objects across large-scale video libraries.
8. Molecular Similarity Search – comparing molecular structures to identify similar substructures for research and development in scientific and pharmaceutical contexts.

Summary

Milvus coupled with HPE Alletra Storage MP X10000’s delivers a future-ready, high-performance, and cost-effective foundation for enterprises looking to scale AI applications that rely on large-scale vector search. By using containerized, disaggregated, and high-performance S3 object storage, the solution delivers faster queries, scalable infrastructure, and robust security features. Benefits include enhanced accuracy, reduced TCO via pay-as-you-go provisioning, streamlined data management, and future-proof capabilities for AI-driven workloads such as recommendation systems, anomaly detection, multimodal search, and Retrieval Augmented Generation (RAG). This combination empowers enterprises to run large-scale, high-dimensional similarity searches efficiently while maintaining flexibility, performance, and security across hybrid cloud environments.