Deep Lake 4.0

Overview

Deep Lake 4.0 is the fastest multi-modal AI search platform on data lakes, featuring revolutionary index-on-the-lake technology. Enables sub-second scalable search directly from object storage with lightweight compute and minimal memory.

Index-on-the-Lake Innovation

First database to store indexes on the lake without requiring cache:

• Pioneering 'Deep Lake' category alongside data warehouses and lakehouses
• Sub-second queries directly from S3-compatible storage
• No expensive in-memory requirements
• Maintains index on lake without cache layer

Performance Benefits

Speed

• Sub-second latency from object storage
• 2x faster than other object storage alternatives
• 5x faster setup (removed all dependencies except NumPy)
• 10x faster reads/writes (C++ migration for low-level code)

Cost Efficiency

• 10x more cost efficient than in-memory databases
• Eliminates costly in-memory storage requirements
• No large clusters needed
• Lightweight compute with minimal memory

Key Features

Multi-Modal Support

• Embeddings and vectors
• Audio, text, videos, images
• DICOM medical imaging
• PDFs and documents
• Annotations and metadata

Core Capabilities

• Storage for all AI data types
• Querying and vector search
• Data streaming for model training
• Data versioning and lineage
• Multiple indexing strategies

Deep Lake 4.0 Enhancements

Eventual Consistency

• Enables concurrent workloads
• Better multi-user support
• Improved collaboration

Faster Setup

• Only NumPy dependency
• Minimal installation overhead
• Quick getting started

Performance Improvements

• C++ migration for speed
• Optimized read/write operations
• Better resource utilization

Indexing Technology

Multiple Index Types

• Embedding with quantization
• Lexical indexes
• Inverted indexes
• Combined strategies

Accuracy

• High recall rates
• Precise retrieval
• Quality results

Architecture

Natively built around object storage:

• Accessed as if local
• Index stored on lake
• No separate cache layer
• Distributed architecture

Enterprise Features

• Petabyte-scale data management
• SOC 2 Type 2 certification
• VPC deployment available
• Data lineage tracking
• Scalability through compute/storage isolation

Use Cases

• LLM-based product deployment
• Enterprise-grade AI applications
• Multi-modal search
• ML model training at scale
• Data versioning for AI
• Streaming data pipelines

Integration

Framework Support

• LangChain integration
• PyTorch streaming
• TensorFlow streaming
• LlamaIndex support

Platform Compatibility

• AWS S3
• Google Cloud Storage
• Azure Blob Storage
• MinIO and S3-compatible storage

Pricing

Free Tier

• 100MB data ingested
• 3 queries per day
• Development and testing

Pro Plan

• $40/month per seat
• 10GB storage included
• $0.99 per additional GB
• Ideal for teams

Enterprise Plan

• Custom pricing for large organizations
• Petabyte-scale capabilities
• VPC deployment
• SOC 2 Type 2 compliance
• Dedicated support
• Volume discounts

Data Storage Model

• Data stored on customer's own cloud storage
• Cost efficiency through native object storage
• No vendor lock-in for data
• Complete data control

Y Combinator Backing

Activeloop is Y Combinator backed (S18), demonstrating:

• Strong validation
• Solid funding
• Growth trajectory
• Innovation focus

Open Source Components

GitHub: activeloopai/deeplake

• Core library open source
• Community contributions
• Transparent development
• Apache 2.0 license

Documentation

• Comprehensive docs at docs.activeloop.ai
• Tutorials and examples
• API reference
• Best practices guides