Build Hybrid Search That Finds What Keywords Alone Cannot
Zilliz Cloud combines dense vector search, sparse keyword matching, and metadata filtering in a single query — so your applications return results that are both semantically relevant and precisely filtered, in under 10ms.
Hybrid Search Applications Powered by Zilliz Cloud
Build search experiences that combine semantic understanding with keyword precision across every industry and data type.
E-Commerce Product Search
Build product search that understands shopper intent — not just keywords. Combine text embeddings with brand, price, and category filters to surface the right products from millions of listings, even when queries are vague or misspelled.
Legal Document Discovery
Enable attorneys to search across depositions, contracts, and medical records using natural language. Combine semantic understanding with exact-match filters on case number, date, and document type — returning precise results across millions of files.
Retrieval-Augmented Generation
Build RAG pipelines that retrieve the most relevant context for your LLM. Combine vector similarity with keyword matching and metadata filters to reduce hallucinations and ground every response in your actual data.
Customer Support Search
Let support agents and chatbots search across tickets, knowledge bases, and product docs simultaneously. Hybrid search surfaces answers that match both the meaning of the question and the specific product or account context.
Clinical & Biomedical Search
Build search systems that find relevant clinical trials, research papers, and patient records by combining medical concept embeddings with exact filters on diagnosis codes, dates, and study parameters — no missed results.
Content & Media Discovery
Enable users to search across articles, videos, and images using natural language queries. Combine multimodal embeddings with metadata like topic, publish date, and source to deliver highly relevant content recommendations.
Codebase & Documentation Search
Build developer tools that search across codebases, API docs, and error logs using semantic understanding. Combine code embeddings with language, repo, and version filters to surface the exact snippet developers need.
Financial Research & Compliance
Automate research across SEC filings, earnings transcripts, and market reports. Combine semantic search with entity, date, and filing-type filters to surface relevant disclosures and compliance risks — instantly, across millions of documents.
Why Zilliz?
Why AI Teams Choose Zilliz Cloud for Hybrid Search
Hybrid search demands a database that handles dense vectors, sparse vectors, and scalar filters in a single query path — without stitching together separate systems. Zilliz Cloud delivers all three natively, with sub-10ms latency at billion-vector scale.
100K+QPS
Serve high-traffic search without compromising result quality
Hybrid search queries are computationally heavier than single-vector lookups — every query runs dense retrieval, sparse matching, and filtering in parallel. Zilliz Cloud sustains 100K+ queries per second with stable p99 latency, so your search stays fast even at peak traffic.
10B+Vectors
Index your entire catalog without sharding or splitting
Product catalogs, document archives, and knowledge bases grow continuously. Zilliz Cloud handles 10B+ vectors in a single deployment — so your hybrid search covers every item, every document, and every record without manual partitioning.
-10xCost
Replace your Elasticsearch-plus-vector-database stack
Running separate systems for keyword search and vector search means double the infrastructure cost and double the operational complexity. Zilliz Cloud's native hybrid search eliminates the need for a parallel Elasticsearch deployment — cutting search infrastructure costs by 10x.
< 10msLatency
Sub-10ms results even with multi-signal retrieval
Hybrid queries combine multiple retrieval signals — dense, sparse, and filtered — in a single call. Zilliz Cloud executes all three in parallel and re-ranks results in under 10ms, fast enough for real-time search, autocomplete, and interactive applications.
Hybrid search out of the box
Combine dense vector search, BM25 sparse retrieval, and metadata filtering in a single query — with built-in re-ranking via RRF or weighted scoring. No external search engine required.
Automatic and elastic scaling
Automatically scales compute and storage as your search index grows — handling traffic spikes and catalog expansions with no capacity planning, index rebuilding, or sharding required.
Native multi-tenant architecture
Built-in tenant isolation lets you run hybrid search for millions of users, stores, or applications on the same platform — without noisy-neighbor slowdowns or data leakage between tenants.
Ease of use
Go from zero to production-ready hybrid search in minutes. Zilliz Cloud manages the infrastructure, handles scaling, and runs the Ops — so your team focuses on search quality, not cluster management.
Multi-cloud flexibility
Run on AWS, Azure, or GCP across 30+ regions worldwide — keeping your hybrid search infrastructure close to your users and within your cloud strategy.
Enterprise-grade reliability and compliance
99.95% SLA with SOC 2, ISO 27001, GDPR, and HIPAA compliance — plus regional failover and BYOC support for enterprise search workloads.
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Everything you need to master hybrid search
Tutorials, deep dives, and practical guides for building hybrid search