Evaluating Databricks vector search? LanceDB delivers vector search on an open columnar format. Full portability. No runtime lock-in.
Open Lance format on object storage. No Databricks runtime required. Up to 100x savings.
Raw data, embeddings, and features together. Works with Spark, Trino, Ray, and Python natively.
New embedding model? Add a column. No runtime dependency. No lock-in.
Vector, full-text, and SQL queries in one system. Query from any engine.
| Databricks | LanceDB | |
|---|---|---|
| Cost | Vector search within Databricks runtime pricing. | Open format on object storage. Up to 100x savings. |
| Scale | Databricks-managed scaling. | 20 PB largest table. 20K+ QPS. Billions of vectors. |
| Search | Vector search within Databricks notebooks. | Native vector, full-text, and SQL hybrid search from any client. |
| Data model | Within Databricks ecosystem. | Open Lance format. Portable across Spark, Trino, Ray, Python. |
| Portability | Databricks-integrated workflows. | Same data works embedded, self-hosted, or managed LanceDB Cloud. |
| Best for | All-in-one Databricks shops. | Portable, cost-efficient vector search with open format. |
Granular RBAC, SSO integration, and VPC deployment options.
Data versioning and time-travel capabilities for auditability.
Dedicated technical account management and guaranteed SLAs.
Or try LanceDB OSS — same code, scales to Cloud.