Elasticsearch vs Rockset Selecting the Right Database for GenAI Applications

Elasticsearch and Rockset are two prominent databases with vector search capabilities that play a crucial role in AI applications such as recommendation engines, image retrieval, and semantic search. Both offer robust capabilities for handling vector search but have different strengths and weaknesses. Elasticsearch is built on Apache Lucene and is known for real-time indexing and full-text search, while Rockset is a search and analytics database designed for structured and unstructured data, including vector embeddings. When choosing between the two, it depends on your use case, technical requirements, and constraints. Elasticsearch is good for its maturity, hybrid search, and text-heavy workloads, making it suitable for e-commerce, log analytics, and document retrieval where you need hybrid searches that combine full-text search and vector similarity. On the other hand, Rockset is better for real-time analytics and applications that require low latency updates, making it ideal for dynamic environments like event-driven architectures, live dashboards, and AI-powered applications. In conclusion, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.