Qdrant alternative. Vector search without the RAM tax.
Evaluating Qdrant? LanceDB scales on object storage with compute-storage separation. No HNSW parameter tuning. No segment optimization. No shard management.
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Why teams switch
Compute-storage separation
Complete database on object storage. No DIY persistence layer. Up to 100x savings at scale.
One table, not six systems
Raw data, embeddings, and features together. No custom serialization, no external metadata store.
Schema evolution without rebuilds
New embedding model? Add a column. No index rebuild, no custom migration code.
Full-text + hybrid search, native
Native full-text search integrated with vector search. Qdrant requires external sparse vectorization for text search.
Comparison
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.
