Rockset

Overview

Rockset was a real-time analytics database built for the cloud with native vector search capabilities. Acquired by OpenAI in 2024 to power retrieval infrastructure across OpenAI's products, demonstrating its production-grade vector search capabilities.

Key Features (Historical)

Vector Search

• Native vector embedding support
• Similarity search indexes
• Hybrid search combining vectors with filtering and aggregations
• Billions of vectors at scale
• Thousands of queries per second

Real-Time Analytics

• Sub-second ingest latency
• Millisecond query latency (P50: 10ms on customer workloads)
• 20,000+ QPS sustained performance
• Immediate data availability in searches

Converged Index

• Unified index combining:
  • Search index
  • Columnar store
  • Row store
  • Similarity index (vector)
• Scales to billions of vectors and terabytes of data

Technical Architecture

• Built on top of RocksDB
• FAISS Inverted File Indexes for ANN
• Distributed architecture for scale
• Real-time data ingestion
• Cloud-native design

Performance Characteristics

• Ingest Latency: Sub-second
• Query Latency: Milliseconds (P50: 10ms)
• Throughput: 20,000+ QPS
• Scale: Billions of vectors
• Data Freshness: Real-time visibility

Use Cases (Historical)

• Real-time vector search at scale
• Hybrid search with filtering
• High-throughput production applications
• Machine learning applications
• Personalization engines
• Real-time recommendations

Hybrid Search Capabilities

Rockset excelled at combining:

• Vector similarity search
• SQL filtering and aggregations
• Real-time analytics
• Complex joins and transformations

This enabled sophisticated search experiences beyond pure vector similarity.

OpenAI Acquisition (2024)

OpenAI acquired Rockset to:

• Power retrieval infrastructure across products
• Leverage world-class data indexing and querying
• Enhance vector search capabilities
• Support production-scale AI applications

The acquisition validates Rockset's technology as production-ready for demanding AI workloads.

Historical Pricing

Commercial service with usage-based pricing. Specific pricing details are no longer publicly available following the OpenAI acquisition.

Current Status

Following the OpenAI acquisition:

• Technology integrated into OpenAI infrastructure
• Public service availability has changed
• Contact OpenAI for information about retrieval capabilities

Legacy and Impact

• Demonstrated viability of converged analytics + vector search
• Proved real-time vector search at scale
• Influenced subsequent vector database architectures
• Validated hybrid search approach

Technical Resources

Historical technical blog posts and documentation showcase:

• Converged index architecture
• Billion-scale vector search implementation
• Real-time ingest and query optimization
• Production deployment best practices