Zilliz

What is Zilliz AI

Zilliz AI is a fully managed vector database platform built on the open-source Milvus engine. It powers enterprise-grade AI applications by delivering billion-scale vector search, low-latency similarity retrieval, and seamless support for Retrieval Augmented Generation (RAG) with large language models. Offered as Zilliz Cloud, it removes the burden of infrastructure provisioning, tuning, and scaling, so teams can focus on building semantic search, recommendations, and intelligent assistants backed by reliable, high-performance vector indexing and querying.

Zilliz AI Main Features

• Managed vector database: Production-ready Milvus under the hood, delivered as a fully managed cloud service.
• Billion-scale ANN search: High-throughput, low-latency similarity search optimized for large embedding collections.
• RAG-ready retrieval: Vector search that plugs into LLM pipelines for Retrieval Augmented Generation.
• Hybrid querying: Combine vector similarity with metadata filtering for precise, context-aware results.
• Flexible schemas: Collections, vector fields, and scalar attributes to organize multi-modal data.
• Robust ingestion: Batch and streaming-friendly data loads for continuous updates.
• Scalability and reliability: Elastic capacity and high availability for mission-critical workloads.
• APIs and SDKs: Client libraries for popular languages and frameworks to speed up integration.
• Observability: Metrics, logs, and performance insights to tune indexes and queries.
• Security and governance: Access controls and enterprise-grade protections for sensitive data.

Who Should Use Zilliz AI

Zilliz AI suits ML engineers, data engineers, and AI platform teams building semantic search, recommendations, personalization, deduplication, and RAG applications with LLMs. It is ideal for startups wanting a managed vector database without ops overhead, and enterprises that need reliable, scalable, and secure vector search across massive embedding datasets.

How to Use Zilliz AI

1. Sign up for Zilliz Cloud and create a project or workspace.
2. Create a collection, define vector dimensions and any scalar metadata fields.
3. Select a distance metric (e.g., cosine, Euclidean, inner product) and index configuration.
4. Generate embeddings from your preferred model and prepare data for ingestion.
5. Ingest vectors and metadata using the SDKs or REST APIs.
6. Run similarity queries with optional filters; tune index/search parameters for latency and recall.
7. Integrate results into your app or LLM pipeline for RAG, re-ranking, or recommendations.
8. Monitor performance, scale capacity as needed, and set up backups and access policies.

Zilliz AI Industry Use Cases

E-commerce teams deliver semantic product search and personalized recommendations. Customer support platforms build RAG chatbots that ground LLMs in knowledge bases. Media companies enable image and video similarity search. Fintech teams detect fraud and perform entity deduplication via vector similarity. Healthcare and research organizations accelerate literature and case retrieval while enriching search with domain-specific embeddings.

Zilliz AI Pricing

Zilliz AI is offered as a managed cloud service with usage-based options and enterprise plans. Pricing typically reflects storage, compute, and data throughput. For up-to-date tiers and capabilities, consult the official Zilliz Cloud pricing page.

Zilliz AI Pros and Cons

Zilliz AI FAQs

• What is the difference between Zilliz AI and Milvus?

  Milvus is the open-source vector database engine. Zilliz AI (via Zilliz Cloud) provides a fully managed, production-ready service powered by Milvus, adding cloud operations, scaling, security, and observability.

• Does Zilliz AI support RAG with large language models?

  Yes. It provides fast vector search and filtering to retrieve context for LLM prompts, enabling reliable Retrieval Augmented Generation workflows.

• How do I migrate from self-hosted Milvus?

  You can export data from your Milvus instance and ingest it into Zilliz Cloud collections via SDKs or APIs, leveraging API compatibility to ease migration.

• What search modes and metrics are available?

  Zilliz AI supports approximate nearest neighbor similarity search with common distance metrics such as cosine, Euclidean (L2), and inner product, alongside metadata filtering for hybrid queries.

• Can I combine keyword and vector search?

  You can use vector search with scalar filters in Zilliz AI and complement it with keyword search in your application layer or via external text search tools for blended retrieval.