Databricks Vector Search alternative. Open Lance format. Works with Spark. Runs anywhere.
Evaluating Databricks vector search? LanceDB delivers vector search on an open columnar format. Full portability. No runtime lock-in.
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Tomorrow's AI is being built on LanceDB today
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Why teams switch
Compute-storage separation
Open Lance format on object storage. No Databricks runtime required. Up to 100x savings.
One table, not six systems
Raw data, embeddings, and features together. Works with Spark, Trino, Ray, and Python natively.
Schema evolution without rebuilds
New embedding model? Add a column. No runtime dependency. No lock-in.
Full-text + hybrid search, native
Vector, full-text, and SQL queries in one system. Query from any engine.
Comparison
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.
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.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.
Schedule a Technical Consultation