vs

Deep Lake vs Qdrant

Deep Lake: Activeloop's tensor-native data engine for AI: vectors, multimodal data and versioning on object storage. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud.

key differences

  • Transactions: Deep Lake offers limited ACID; Qdrant offers none.
  • Only Deep Lake has columnar storage, time travel, branching.
  • Only Qdrant has secondary indexes, geospatial, role-based access.
  • Deep Lake has a serverless option; Qdrant does not.
  • Deep Lake can run embedded in your application process.

choose deep-lake for

  • +Multimodal datasets shared by training and RAG
  • +Versioned computer-vision datasets
  • +Agent memory over documents and images

best at · Vector search, RAG, Lakehouse

choose qdrant for

  • +RAG retrieval with metadata filtering
  • +Recommendation and similarity search
  • +Image and multimodal search
  • +Multi-tenant semantic search

best at · Vector search, RAG

overview
Primary model
Vector
Vector
Also supports
Array / Scientific, Columnar / OLAP
Document
Type
Database engine
Database engine
Status
Active
Active
Developer
Activeloop
Qdrant Solutions GmbH
First release
2019
2021
Latest version
4.7.4 (Aug 25, 2026)
1.20.0 (Oct 22, 2026)
License
Open source (permissive)Apache License 2.0
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
12.2
35.9
Rank
#273
#55
GitHub stars
9.3K
35.3K
Stack Overflow questions
–
60
scale & guarantees
Comfortable data size
Large (≤ 1 PB)
Large (≤ 1 PB)
Horizontal scaling
Native
Native
Multi-region
None
None
ACID
Limited
None
Consistency
Eventual
Tunable, Eventual
Schema
Flexible
Schemaless
Architecture
Embedded, Serverless, Shared storage
Distributed, Client server
deployment
Self-hosted
✓
✓
Vendor-managed cloud
✓
✓
Serverless
✓
—
Embedded
✓
—
Edge
—
—
Clouds
AWS, Google Cloud, Azure
AWS, Google Cloud, Azure
capabilities
Transactions
—
—
Joins
—
—
Secondary indexes
—
✓
Full-text search
✓
✓
Vector search
✓
✓
Geospatial
—
✓
JSON
✓
✓
Change data capture
—
—
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
—
—
Pub/Sub
—
—
Columnar storage
✓
—
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
—
✓
Time travel
✓
—
Branching
✓
—
Online schema changes
—
—
Streaming ingest
—
—
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
TQL (Tensor Query Language), SQL (Postgres interface)
Qdrant Query API (JSON filters)
Client languages
Python, JavaScript
Python, JavaScript, Rust, Go, Java, .NET
Best for
Vector search, RAG, Lakehouse, Feature store
Vector search, RAG

Deep Lake vs Qdrant: FAQ

What is the difference between Deep Lake and Qdrant?

Deep Lake: Activeloop's tensor-native data engine for AI: vectors, multimodal data and versioning on object storage. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud. Transactions: Deep Lake offers limited ACID; Qdrant offers none. Only Deep Lake has columnar storage, time travel, branching. Only Qdrant has secondary indexes, geospatial, role-based access.

When should I choose Deep Lake over Qdrant?

Choose Deep Lake for multimodal datasets shared by training and RAG; versioned computer-vision datasets; and agent memory over documents and images. Choose Qdrant for RAG retrieval with metadata filtering; recommendation and similarity search; and image and multimodal search.

Is Deep Lake or Qdrant more popular?

Qdrant ranks #55 on the WhichDB Score (35.9/100) and Deep Lake ranks #273 (12.2/100). Popularity is not fit: check the requirements that matter for your project.

Are Deep Lake and Qdrant open source?

Yes. Deep Lake is open source. License: Apache License 2.0. Yes. Qdrant is open source. License: Apache License 2.0.