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New for Zilliz Cloud: 10X Performance Boost and Enhanced Enterprise Features

New for Zilliz Cloud: 10X Performance Boost and Enhanced Enterprise Features

Nov 19, 20243 min read

Vector databases are hitting their stride as more companies move AI applications into production, but this shift brings new challenges around performance, scale, and management. Today's release of Zilliz Cloud tackles these challenges head-on with our new Cardinal vector search engine and a suite of enterprise-ready features.

Breaking the Performance Barrier with Cardinal

Cardinal, our new vector search engine that delivers a 10X performance boost in production environments, is at the heart of this release. This isn't just about raw speed - we've reimagined how vector search should work at scale.

Cardinal introduces AutoIndex, which automatically tunes your index configuration based on data characteristics and hardware setup—no more manual tweaking or performance tradeoffs. The system continuously adapts to deliver optimal search performance, letting development teams focus on building features instead of managing infrastructure.

Under the hood, Cardinal employs a three-layer optimization approach. The algorithm layer combines advanced IVF-based and graph-based approaches, maintaining high accuracy even with filtered results. The engineering layer implements specialized memory allocation and multi-threading for real-world performance gains. At the lowest level, kernel optimizations for x86 and ARM platforms achieve up to 3X speedup over standard implementations.

Production-Ready Features for Enterprise Scale

As vector databases become critical infrastructure for AI applications, we've enhanced Zilliz Cloud's enterprise capabilities across the board.

Multi-replica Goes GA

Our multi-replica feature is now generally available, offering linear QPS improvements as you add replicas. For high-traffic applications, queries are automatically distributed across replicas, reducing latency and improving efficiency. The system also distributes replicas across availability zones, ensuring continuous operation even during zone outages.

Infrastructure Improvements

We've increased the capacity of our performance-optimized compute units by 50%, now supporting up to 1.5 million vectors per CU (at 768 dimensions). For large-scale deployments, this translates to approximately 30% cost reduction.

Enhanced Observability

Integration with Prometheus enables real-time monitoring of system metrics. We've implemented 41 alerts across 26 metrics covering everything from infrastructure health to data operations. This comprehensive monitoring ensures teams can proactively manage their vector search infrastructure.

Simplified Data Migration

We've added migration pathways from Qdrant and Pinecone Serverless to support the growing ecosystem. The migration process preserves your data integrity while allowing you to use Zilliz Cloud's enhanced capabilities.

Authentication and Global Presence

Security meets convenience with our new Auth0-based authentication system, supporting email, GitHub/Google authentication, and enterprise SSO through Okta. We've also expanded our global footprint with a new AWS Tokyo region, bringing our total to 12 regions worldwide.

Developer Experience Improvements

Beyond the technical improvements, we've overhauled the developer experience. The new web console provides enhanced visualization tools and streamlined workflows. We've also revamped our payment system with extended billing cycles for enterprise workflows, automated notifications, and detailed cost visibility through itemized invoices.

Getting Started

These features are available now across all Zilliz Cloud deployments. Explore with our free tier (no credit card required), or try our 30-day enterprise trial with up to $200 in credits. Subscribe through any cloud marketplace and receive an additional $100 credit.

For detailed documentation and migration guides, visit our documentation.

Steffi Li
Steffi is the Director of Product Marketing at Zilliz, with prior experience leading the GTM strategies for open-source data technologies such as Apache Kafka and Apache Airflow. Passionate about travel, yoga, and non-fiction reading, she brings a balanced and enriched worldview to her professional pursuits.