AI applications have moved beyond simple RAG demos. Today teams are building production AI apps that depend on fast retrieval and durable, queryable memory—not just within a single chat, but across sessions, systems, and real business workflows. At the center of it all are AI vectors, turning documents, call transcripts, and knowledge bases into something AI models can reliably use at speed.
Specialized vector databases can be a great way to spin up a pilot, but once you move into production, “easy to start” isn’t enough. Enterprise teams need to have confidence in system availability, security controls, governance, and operational maturity to run AI at scale. Organizations also need to know that there is a path beyond vectors when application demands require integration with additional types of data. To help developers and their organizations meet all these needs—and more, we are pleased to announce the Limited Availability of Oracle Autonomous AI Vector Database. Autonomous AI Vector Database is a fully managed vector database that helps AI developers and data scientists quickly build semantic search, RAG, and agentic applications using easy-to-use vector APIs—with enterprise-grade reliability, security, and stock-exchange robustness to help you meet compliance requirements.
Quickly build apps with easy-to-use vector APIs
Autonomous AI Vector Database gives you multiple ways to quickly develop AI applications. Our Python SDK, REST APIs or PL/SQL let developers create new AI applications or incorporate vector capabilities into existing applications.
Autonomous AI Vector Database’s APIs provide an opinionated schema that incorporates Oracle-recommended design for storing vectors, metadata, and vector indexes. You can use auto-generated vectors and metadata indexes, or pick which indexes you would like to use. Choose from HNSW or IVF indexes and modify their parameters to meet your specific needs. The metadata indexes in Autonomous AI Vector Database can also be configured to meet your specific needs.
Autonomous AI Vector Database’s APIs provide semantic search with tunable parameters like the distance metric and accuracy while supporting additional metadata filtering capabilities. It uses optimized algorithms that allow you to filter your data across both vectors and metadata. Search results can then be reranked using built-in or uploaded reranking functions.
Easily use AI vectors with any business data
Many enterprise AI use cases require more than just similarity search, they need to incorporate vector capabilities along with business data search to meet business needs. For example, a RAG query may need to filter results using geospatial coordinates stored in metadata, which requires spatial distance functions (e.g., finding a product in a store within five miles of a customer). Agent memory applications may also require graph traversal in addition to vector similarity search. Autonomous AI Vector Database provides these capabilities in one place, so you can build sophisticated AI applications without complex logic to stitch together multiple data engines.
Meet compliance requirements with stock-exchange robustness
When it’s time to move an AI application into production, navigating compliance reviews can be challenging. Autonomous AI Vector Database combines the simplicity of modern vector databases with Oracle’s autonomous infrastructure, giving developers and IT the confidence to deploy into production. Autonomous AI Vector Database is built on Autonomous AI Database and inherits its auto-scaling, self-driving, and self-repairing characteristics designed to keep your applications running without manual intervention. It has built-in encryption, access control, and observability to provide strong security by default—capabilities used by the most demanding organizations in the world.
How to Get Started
Autonomous AI Vector Database is in Limited Availability now, and we’re inviting you to try it. Reach out to your Oracle account team, and we’ll help get you set up in the OCI region and tenancy that works for you.