GPU-Accelerated Search for Sub-Millisecond Latency

Zilliz leverages GPU-accelerated indexing to deliver dramatically faster search performance compared to CPU-only vector databases. This is critical for applications where query speed directly impacts user experience, such as real-time recommendation engines.

Business outcome: Enables real-time AI features that feel instantaneous to end-users, improving engagement and satisfaction.

DiskANN Indexing for Cost-Effective Scale

The platform supports DiskANN, an indexing algorithm that allows for efficient vector search directly from disk. This reduces memory requirements, enabling businesses to handle billions of vectors on more cost-effective hardware.

Business outcome: Dramatically reduces the infrastructure cost associated with scaling to very large datasets.

Streaming Inserts for Real-Time Data Pipelines

Zilliz is built to handle streaming inserts, meaning new data is available for search almost immediately. This is essential for applications that rely on up-to-the-minute information, such as news aggregation or live inventory search.

Business outcome: Ensures AI applications are always working with the freshest data, improving the accuracy of results and recommendations.

Apache 2.0 Licensed Milvus Core

The underlying Milvus project is fully open-source under the Apache 2.0 license. This provides businesses with the freedom to self-host, audit the codebase, and avoid vendor lock-in, a major consideration for many enterprises.

Business outcome: Provides strategic flexibility and reduces long-term risk associated with proprietary infrastructure.

Managed Cloud Service for Zero-Ops

Zilliz Cloud abstracts away the complexity of deploying, scaling, and maintaining a Milvus cluster. Teams can spin up a production-ready database in minutes and rely on Zilliz for updates, backups, and high availability.

Business outcome: Frees up engineering resources to focus on core product development rather than infrastructure management.

Seamless Integration with AI Frameworks

Zilliz integrates with popular AI and data science frameworks like LangChain and LlamaIndex, making it straightforward to embed into existing RAG pipelines. This reduces development friction and accelerates the path from prototype to production.

Business outcome: Speeds up the development cycle for AI features, allowing teams to launch new capabilities faster.

• Massive Scale PerformanceEngineered to handle billions of vectors with high throughput and low latency, making it a top-tier choice for enterprise-scale AI.
• GPU-Accelerated SearchOffers a significant performance advantage for latency-sensitive applications by leveraging GPU power for vector indexing and search.
• Open-Source FoundationThe Apache 2.0 licensed Milvus core provides transparency and flexibility, allowing for self-hosting or managed service use.
• Cost-Effective ScalingSupport for DiskANN indexing reduces memory costs, making it more economical to scale to very large datasets.

• Complexity for Simple NeedsFor basic vector search with a few thousand vectors, the platform's full feature set may be overkill compared to simpler solutions.
• Pricing Model NuanceThe compute-unit-hour pricing model can be difficult to estimate for new users, requiring careful monitoring to manage costs.
• Managed Service DependenceWhile self-hosting is possible, using Zilliz Cloud means relying on a third party for critical infrastructure, which may not suit all organizations.
• Professional RealityBuyers need to have a clear understanding of their workload patterns (e.g., QPS, data volume) to accurately forecast costs and choose between Serverless and Dedicated tiers.

Enterprise RAG Pipelines

Businesses building question-answering systems over their proprietary knowledge bases can use Zilliz to store and retrieve document embeddings with high accuracy and speed. It integrates well with frameworks like LlamaIndex, which have dedicated courses on building applications with vector databases.

High-Performance Semantic Search

E-commerce platforms and content sites can implement 'search by meaning' features, allowing users to find products or articles based on concepts rather than just keywords, improving discovery and conversion.

Real-Time Recommendation Engines

Streaming inserts allow for recommendations to be updated in real-time based on user behavior. This enables platforms to suggest the most relevant items at the exact moment of interaction, increasing engagement and average order value.

Large-Scale Image Similarity

For applications like visual search or duplicate detection, Zilliz can index millions of image embeddings to enable fast and accurate similarity searches, powering features that would be impossible with traditional databases.

Sign up for a free Zilliz Cloud account to access the free tier and explore the console.

Create a new cluster and choose the Serverless or Dedicated tier that best fits your initial workload and performance needs.

Install the official Zilliz SDK (e.g., pymilvus) and connect to your cluster using the provided credentials.

Create a collection, define your vector schema, and start ingesting your embeddings to begin querying.