Zilliz Cloud

Zilliz Cloud runs the same engine as open-source Milvus but handles upgrades, scaling, and monitoring for you. That matters if your team wants vector search without hiring a specialist to babysit clusters. Automated deployment and built-in tooling replace months of manual setup and tuning.

The platform picks an indexing strategy for each dataset automatically through AutoIndex and its Cardinal search engine. You don't hand-tune parameters to get good recall. For teams without deep vector-search expertise, this removes the hardest part of the job.

You can query across multiple vector fields in one request, mixing sparse and dense vectors or combining dense vectors with text. That makes results more accurate than pure semantic search when exact terms matter, like product codes or names. Ever searched for a specific part number and gotten fuzzy nonsense back? Hybrid search fixes that.

A component-based architecture lets you scale horizontally as your data grows. Performance stays steady across a small prototype or a production workload with fluctuating traffic. You pay for the compute you actually use on the serverless tier.

The higher tiers add SSO with SAML 2.0, granular role-based access control, audit logs, and private endpoints with VPC peering, giving security teams the controls they expect before signing off on a new data store. The Business Critical plan goes further with customer-managed encryption keys and HIPAA-eligible controls, which matters for healthcare and finance teams.

You can run fully managed on Zilliz's cloud, keep it inside your own infrastructure with BYOC, or use dedicated clusters with reserved compute. Each option carries the same core feature set, so you're choosing where it lives, not what it does.

Multiple consistency levels let you decide how fresh your search results need to be, balancing accuracy against speed. You also pick the similarity metric, like cosine or Euclidean distance, to match how your embeddings were trained.