Evaluating Qdrant? LanceDB scales on object storage with compute-storage separation. No HNSW parameter tuning. No segment optimization. No shard management.
Complete database on object storage. No DIY persistence layer. Up to 100x savings at scale.
Raw data, embeddings, and features together. No custom serialization, no external metadata store.
New embedding model? Add a column. No index rebuild, no custom migration code.
Native full-text search integrated with vector search. Qdrant requires external sparse vectorization for text search.
| Qdrant | LanceDB | |
|---|---|---|
| Cost | RAM-heavy architecture with full shard replication. $3-5/GB/month. | Object storage at $0.02/GB/month with compute-storage separation. Up to 100x savings. |
| Scale | Requires shard management, segment rebalancing, storage amplification. | Stateless scaling. No sharding decisions, no rebalancing. 20 PB largest table. |
| Search | Vector only. No native full-text search. Requires external sparse vectorization. | Native vector, full-text, and SQL hybrid search in one query. |
| Metadata | Metadata in RocksDB. Must be fully indexed in RAM. | Arrow-native columnar format. Efficient WHERE filtering and analytics. |
| Operations | Continuous tuning - segment sizes, HNSW params (ef, m), optimizer thresholds. | Zero tuning. Asynchronous indexing handles balance automatically. |
| Best for | When tuning for peak single-query latency is everything. | Cost-efficient scale with zero operational complexity. |
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.