Replace Iceberg, your vector database, and your feature store with one AI lakehouse
Apache Iceberg is great for BI tables. AI teams need more: raw assets, metadata, features, embeddings, vector search, full-text search, and training data in one place. LanceDB consolidates the AI data stack into one multimodal lakehouse.
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Why teams switch
100x the random access performance
Lance delivers 100x the random access performance of Parquet, with native vector, full-text, and scalar indexes in the same table format.
One table, not four systems
Raw assets, metadata, features, and embeddings live together. No object-store pointers, vector DB sync, or feature-store handoffs.
Schema evolution without rebuilds
New embedding model? Add a column. No document migrations, no re-indexing.
Full-text + hybrid search, native
Vector, full-text, and scalar indexes live in the table format instead of a separate search stack.
Iceberg was built for BI not multimodal AI
Iceberg
LanceDB
Storage model
Table/catalog layer over files such as Parquet. Excellent for structured analytical tables; raw videos, images, and large blobs usually live somewhere else.
File + table format built for multimodal AI data: blobs, tensors, vectors, text, scalars, and features together in one table.
AI search
No native vector or full-text index in the Iceberg table format. AI search usually means adding and operating a separate vector DB or search engine.
Native vector, full-text, and scalar indexes with hybrid queries on the same rows. Search is part of the table, not another system to sync.
Feature evolution
Adding AI features often means Spark jobs, Parquet rewrites or copy-on-write maintenance, and re-syncing downstream indexes.
Zero-copy column additions. New embeddings, captions, labels, and scores land as new columns in the same dataset.
Performance fit
Optimized for BI scans and structured analytics. Random access over Parquet-backed AI data is the weak point.
Benchmarks cite 100x the random access performance of Parquet, while still supporting efficient scans.
Operations
Mature ecosystem for BI interoperability. For AI, platform teams still maintain the surrounding feature, asset, and vector-search stack.
Fewer moving parts for AI data: storage, search, feature engineering, branching, lineage, and training from one dataset.
Cost / TCO
Efficient open lakehouse table format for BI storage and analytics. For AI workloads, the cost usually moves into extra systems: object storage for assets, a vector DB or search index, feature pipelines, ETL, and sync jobs.
Object-store-native AI lakehouse that consolidates raw assets, metadata, features, embeddings, and indexes in one dataset. Fewer duplicate stores, fewer sync jobs, and less re-indexing work.
Best for
BI, OLAP, structured analytics, and open-table lakehouse interoperability. Keep Iceberg where it works.
AI data infrastructure: multimodal search, curation, feature engineering, and training from one dataset.
