Simpler, Faster Vector Search than Milvus

Comparing Milvus and Zilliz Cloud? Milvus expects a multi-service K8s stack. Zilliz hides that behind pod-based billing. LanceDB is a lightweight alternative that runs embedded or serverless.

Why teams switch

Compute-storage separation

Storage on object storage. Compute scales independently. No multi-container clusters.

One table, not six systems

Raw data, embeddings, and features together. No proxies, coordinators, index nodes, data nodes.

Schema evolution without rebuilds

Add columns without rewriting tables. No Milvus migration complexity.

Full-text + hybrid search, native

Vector, full-text, and SQL queries in one system. Built in, not bolted on.

Comparison

	Milvus / Zilliz	LanceDB
Cost	Milvus - K8s ops burden. Zilliz - pod-based premium.	Object storage with compute-storage separation. Up to 100x savings.
Scale	Strong at scale, but needs K8s expertise or Zilliz premium.	20 PB largest table. 20K+ QPS. Billions of vectors.
Search	Vector search. Text search via plugins.	Native vector, full-text, and SQL hybrid search in one query.
Data model	Embeddings in Milvus. Raw data elsewhere.	Raw data, embeddings, and features in one table.
Purpose	Service-oriented vector database.	Embedded to serverless vector database.
Best for	Teams with K8s expertise or Zilliz budget.	Teams wanting simplicity without sacrificing scale.

The Power of the Lance Format

Vector Search

Fast scans and random access from the same table — no tradeoff
Zero-copy access for high throughput without serialization overhead

Multi-Modal

Raw data, embeddings, and metadata in one table — not pointers to blob storage
No separate metadata store to keep in sync

Enterprise-Grade Requirements

Security

Granular RBAC, SSO integration, and VPC deployment options.

Governance

Data versioning and time-travel capabilities for auditability.

Support

Dedicated technical account management and guaranteed SLAs.