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Enterprise Layer Intelligence
Categories/Data Platform & Integration/Zilliz
Zilliz

Zilliz

Founded 2017

Store and search billions of high-dimensional vectors for enterprise artificial intelligence applications

Pricing model

Free Tier, Paid

You can use Zilliz to store, manage, and search massive vector datasets for production artificial intelligence systems. Built on Milvus, it lets you index billions of multi-modal embeddings alongside text, JSON, and geospatial data in cloud object storage like Amazon S3. You can execute low-latency similarity queries, perform hybrid retrieval with BM25 keyword matching, and rerank results without maintaining complicated infrastructure. It handles automatic caching, backfilling, and schema updates while letting you pay per query or run dedicated clusters across major cloud providers.

What Zilliz does

Index high-dimensional vector embeddings into cloud clustersQuery vector datasets using similarity metrics and hybrid filtersDeploy managed vector clusters on AWS, Azure, or Google CloudManage vector schemas and indexes via terminal command line interfaceConnect S3 data lake storage directly to vector indexing pipelinesRerank query outputs combining BM25 keyword and vector scoresReal-time vector search and retrieval across billions of embeddingsHybrid search combining dense vectors, BM25 full-text, and JSON filteringLake-native storage supporting direct indexing on Amazon S3 bucketsTiered storage architecture separating hot cache from cold object dataOn-demand compute mode to pay per query instead of provisioned capacityMulti-region cluster deployments with automated failover and data replicationOnline schema updates and data backfilling without service interruption

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
FreeFree
StandardFree
Enterprise$197 / mo

Frequently asked

AWSAWSGoogle CloudGoogle CloudMicrosoft AzureMicrosoft Azure

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Zilliz, vector database, Milvus, vector search, embeddings, RAG, hybrid search, BM25, semantic search, AI data lake, S3 storage, machine learning, reranking, multi-modal search, high QPS, low latency