CockroachDB for
CockroachDB’s Vector Search capabilities power semantic search, recommendation systems, and natural language processing, providing a resilient data foundation for the age of AI.
In preview, CockroachDB 24.2 and later
Efficiently retrieve information through vector data types and similarity search functions as part of a Retrieval Augmented Generation (RAG) framework.
Store and manipulate vector data to provide personalized recommendations for users without the need for constant fine-tuning of models.
Represent and query multi-dimensional vector data to enhance AI-driven language models by enriching user prompts with specific context.
CockroachDB ensures high availability by rerouting queries automatically, preventing outages for AI applications. This guarantees real-time, uninterrupted access to critical datasets, including models, embeddings, and AI-driven insights.
CockroachDB efficiently distributes massive quantities of AI-related data — such as embeddings and vector representations — across clusters. It supports advanced AI workloads like semantic search, NLP, and LLMs, allowing seamless scaling as models and datasets grow.
Deploy AI applications and data across regions to achieve near-zero latency. CockroachDB ensures fast, consistent access to AI models, embeddings, and training data, improving response times and delivering accurate insights from anywhere in the world.
CockroachDB ensures that AI data, including sensitive models and datasets, remain secure. It helps users comply with regional regulations. It integrates AI-centric workloads, like retrieval-augmented generation (RAG) and machine learning, with traditional SQL tasks, simplifying infrastructure and accelerating innovation.
Start a free trial of CockroachDB or contact sales to learn more.