Zilliz
Free tierThe Vector Lakebase for AI — real-time serving, iterative discovery, and batch analytics at hundred-billion scale
Free tier available·All audiences·API available·Open source
Key strengths
Billion-scale vector similarity searchFully managed Vector Lakebase (real-time serving, iterative discovery, batch analytics)Lake-native storage on S3 with up to 90% cost reductionFull-spectrum search (vector, full-text, JSON, geospatial, hybrid retrieval)High reliability with 99.95% uptime SLABYOC and self-hosted (Milvus open-source) deployment optionsProduction-tested across 10,000+ enterprises
Free tier + paid plans
San Francisco, USA
Founded 2017
Self-hostable
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Recent changes
- Open source: now available
- Plans updated: Free tier at $0 (5 GB storage, 2.5M vCUs/month, up to 5 collections). Standard from $0/month (Serverless) or from $126/GB/month (Dedicated). Enterprise from $197/month (Dedicated). Business Critical: custom pricing. BYOC available.
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Zilliz is purpose-built for AI infrastructure workloads that require high-throughput, low-latency vector retrieval at scale:
- RAG (Retrieval-Augmented Generation): Serve as the vector store in LLM pipelines, enabling semantic document retrieval with hybrid dense + sparse search and JSON metadata filtering.
- Billion-scale ANN search: Run approximate nearest-neighbor queries across billions of embeddings with production-grade 99.95% uptime SLA.
- Multimodal search: Index and retrieve across text, image, audio, and other embedding types within a single collection.
- Batch analytics + real-time serving: The Vector Lakebase architecture supports both OLAP-style batch analytics and real-time query serving from the same S3-backed storage layer.
- Regulated industry deployments: BYOC and self-hosted Milvus options satisfy data residency and compliance requirements in healthcare and finance.
- Data lake integration: Native support for Iceberg, Lance, and Parquet enables vector search directly over existing lake assets without ETL duplication.
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