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LanceDB

LanceDB is an open-source, AI-native multimodal lakehouse designed for billion-scale vector search. Built on the Lance columnar format, it combines embedded simplicity with cloud-scale performance. LanceDB's disk-based architecture with compute-storage separation enables up to 100x cost savings compared to memory-based solutions while supporting multimodal data (text, images, video, audio).

Rank: #2License: Apache 2.0Cost: low

Deployment

Embedded/Local, Self-Hosted, Managed Cloud (LanceDB Cloud)

Cost

OSS: Free; Cloud: usage-based with $100 free credits; Enterprise: custom pricing

Index Types

IVF-PQ, IVF-HNSW-PQ, BTree

Deployment

Infrastructure Options

Deployment Types

  • Embedded/Local
  • Self-Hosted
  • Managed Cloud (LanceDB Cloud)

Cloud Providers

  • AWS
  • Azure
  • GCP
  • Any (self-hosted)

Strengths

What LanceDB Does Well

  • 100% open source (Apache 2.0) with no vendor lock-in
  • Disk-based storage dramatically reduces costs vs in-memory DBs
  • Compute-storage separation for up to 100x cost savings
  • Embedded mode for serverless and edge deployments
  • Excellent for multimodal data (images, video, audio, text)
  • Zero-copy operations and automatic versioning
  • Native SQL support for multimodal data
  • Fast iteration with columnar format (no full dataset rewrites)
  • PyTorch and JAX integration for training pipelines
  • Scales to billions of vectors efficiently
  • Great local development experience
  • SOC2, GDPR, and HIPAA compliant
  • Strong Python, Node.js, and Rust SDK support

Weaknesses

Potential Drawbacks

  • Disk-based means higher latency than pure in-memory solutions
  • Index creation can take hours for very large datasets (700M+ vectors)
  • Storage backend choice critically impacts performance (object storage slowest)
  • IVF-PQ indexing uses /tmp which can run out of space on large datasets
  • Less mature ecosystem compared to Pinecone or Elasticsearch
  • Requires understanding of storage backend trade-offs (EBS vs EFS vs S3)
  • Memory leaks in older versions (<0.25.0)
  • Newer project with smaller community
  • Documentation less comprehensive than established alternatives

Use Cases

When to Choose LanceDB

Ideal For

  • Cost-conscious projects at billion-vector scale
  • Multimodal AI applications (video, audio, image search)
  • Embedded and serverless deployments
  • ML training pipelines needing integrated dataloading
  • Teams wanting open-source with no vendor lock-in
  • Applications tolerant to moderate latency (10-100ms)
  • RAG systems with large document corpuses
  • Research and experimentation with disk-based architectures

Not Ideal For

  • Applications requiring sub-10ms latency consistently
  • Use cases needing pure in-memory speed
  • Teams without infrastructure expertise (object storage, EBS/EFS)
  • Small datasets where cost savings don't matter
  • Production systems needing mature, battle-tested solutions
  • Applications requiring extensive compliance certifications

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See how it stacks up

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