Lance

Lance is not another parquet alternative—it's a purpose-built format for AI data pipelines where random access and hybrid search matter more than columnar scan throughput. If your ML workflows involve frequent row lookups for real-time serving, random sampling for training, or multimodal data like images and embeddings, Lance will feel like a breath of fresh air. Its 100x faster random access compared to Parquet is not marketing fluff; the VLDB 2025 paper backs it. Hybrid search (vector + FTS + SQL) out of the box is rare at the lakehouse format level. Data evolution with backfill—adding a new column and populating it without rewriting everything—solves a practical pain point in feature engineering. Where Lance may disappoint: it's not a SaaS product. You'll need to manage object storage and orchestrate compute yourself. OLTP-heavy use cases (frequent row updates/deletes) aren't its forte—Lance is optimized for append-heavy ML workloads. Compared to alternatives like Apache Iceberg or Delta Lake, Lance is less mature for traditional analytics but significantly faster for AI-specific access patterns. For teams already using PyTorch, DuckDB, or Spark, integration is straightforward. We'd recommend Lance for ML engineers building retrieval-augmented generation (RAG) systems, embedding stores, or large-scale multimodal datasets. Pass if you want a fully managed lakehouse or your data is purely tabular with no need for fast random access. In practice, users may find the ecosystem smaller than Iceberg/Delta—check driver availability for your preferred query engine.