AI designed for data
Foundational AI technologies
- Unified hybrid vector search
Combines AI Vector Search with relational, text, JSON, knowledge graph, and spatial searches—allowing retrieval of related documents, images, videos, audio, and structured data. Customers can easily combine AI Vector Search with LLMs to search for private data that an LLM can combine with public data to answer business questions.
- Model Context Protocol (MCP) Server support
Enables AI agents powered by LLMs to access an organization’s database to answer questions using iterative reasoning. AI agents can explore multiple solution paths and request additional data during their analysis to produce better and more accurate results.
- Built-in data privacy protection
Enforces sophisticated security, privacy, and compliance rules in the database. Measures include end user–specific row, column, and cell-level data visibility as well as dynamic masking of unauthorized data. In addition, it helps AI to access the database directly using SQL or other APIs without exposing private data.
- Oracle Unified Memory Core
Lets users store context for AI agents in a single system. It uniquely enables low-latency reasoning across vector, JSON, graph, relational, text, spatial, and columnar data in one converged engine, with consistent transactions and security.
- Oracle Exadata for AI
Accelerates AI at scale by delivering hardware and software engineered together for maximum performance and availability. Exadata can significantly accelerate AI vector queries by offloading them to Exadata intelligent storage. Vector offload also works with the new Exadata Exascale software architecture, which brings extreme elasticity and lower cost—extending Exadata benefits to smaller workloads and organizations.
- AI Database acceleration with NVIDIA
Oracle AI Database APIs that enable integration with LLM providers also support integration with NVIDIA NIM containers. Using this feature, Oracle AI Database can run vector embedding models or implement RAG pipelines using NVIDIA NIM containers. In addition, Oracle Private AI Services Container, which currently supports execution on CPU resources, has been designed to support the future use of NVIDIA GPUs for vector embedding and index generation using NVIDIA CAGRA (CUDA Anns GRAph-based) and cuVS (CUDA Vector Search).
AI for application development
- Comprehensive, scalable app development
Developers can quickly create scalable, high performance AI-powered applications using SQL, JSON, XML, and a range of procedural languages. Oracle AI Database 26ai offers a range of built-in development tools, such as APEX, and converged database capabilities along with the following capabilities.
- AI Semantic Modeling
Helps explain the purpose, characteristics, and semantics of data to AI. This additional information helps AI generate better applications and provide more accurate responses to natural language questions.
- Unified data model
The relational, JSON, and graph data models have been unified, providing massive simplification. This accelerates developer productivity by enabling applications to access the same data in relational format via SQL, as a JSON document, or as a graph.
- Private Agent Factory
A no-code platform that enables business analysts and domain experts to quickly build, scale, and safely deploy agents and workflows. The Oracle AI Database Private Agent Factory framework runs as a container in public clouds or on-premises, maintaining data security by enabling you to develop and orchestrate AI agents without having to share data with any third-party. An AI agent builder with a visual interface and a template library makes it simple to take advantage of the full power of Oracle AI Database in creating and managing intelligent, data-centric agents. Private Agent Factory includes several pre-built AI agents such as a Database Knowledge Agent, a Structured Data Analysis Agent, and a Deep Research Agent.
- Select AI Agent
Build, deploy, and manage AI agents within Oracle Autonomous AI Database with a simple, secure, and scalable in-database framework. It supports custom and prebuilt in-database tools, external tools via REST, and MCP Servers, enabling the automation of multistep agentic workflows, accelerating innovation and helping organizations keep their data safe.
End data chaos
Converged data architecture
- Optimized for all workloads and data types
Get native support for all modern data types, workloads, and analytics built into a single service, giving developers a platform with fewer moving pieces and less complexity. Take advantage of an environment that supports applications or analytics at any scale or criticality without requiring time-consuming integration of multiple services or multiple specialty databases.
- All modern data formats are supported: relational, vector, graph, geospatial, JSON, text, and more. There’s no need to use numerous specialty databases for different data types.
- All modern workloads are supported: OLTP, AI Vector Search, Agentic AI, IoT, temporal, ledger/blockchain, streaming, analytic, and more. There’s no need for different specialty databases to support different workloads.
- All modern analytics are supported: generative AI, advanced SQL, machine learning, graph analytics, text/search analytics, geospatial analytics, and more—all including data lake access. There’s no need to move data to specialty databases for analytics.
Database manageability
- Integrated, enterprise-wide Oracle AI Database management
Increase enterprise-wide database performance and availability with consistent management processes via a single-pane-of-glass management dashboard. DBAs reduce their workloads by consolidating the monitoring and management of databases running on premises, in Oracle Cloud Infrastructure, and in third-party clouds with Oracle database management solutions.
Explore Oracle Enterprise Manager
End data lock-in
Enterprise-wide analytics and data lakehouse
- Build a data lakehouse and warehouse, on-premises or in the cloud
Advanced data lake and warehouse technologies, such as Oracle Database In-Memory and Oracle Multitenant, enable analytics teams to complete more in-depth analyses at scale in less time. Customers develop deeper, data-driven insights using Oracle AI Database technologies on-premises, in Oracle Cloud Infrastructure, or on leading cloud hyperscalers.
- Unlock a multicloud, open data lakehouse
Autonomous AI Lakehouse supports the Apache Iceberg open table format, enabling true, enterprise-wide AI and analytics. Availability on all four major hyperscalers—Oracle Cloud Infrastructure, Amazon Web Services, Microsoft Azure, and Google Cloud—along with interoperability with Databricks and Snowflake on the same clouds, enables customers to leverage their existing investments and gain the net incremental benefits of Autonomous AI Lakehouse with the latest AI technologies for their business needs. Oracle Autonomous AI Lakehouse delivers this securely with Exadata-powered performance and pay-per-use, serverless scaling.
- Oracle Vectors on Ice
Provides customers with native support for vector data that is stored in Apache Iceberg tables. AI Vector Search can read vector data directly from Iceberg tables, create vector indexes to accelerate vector search, and automatically update these indexes as the underlying vector data changes. Oracle Vectors on Ice allows AI search on Data Lake data and enables unified search across business data in the database and vectors stored in a Data Lake. This enables customers to achieve unified intelligence across databases and Data Lakes.
- Use simple SQL to query property graphs
Build graph analytics and applications with SQL using existing SQL development tools and frameworks. Oracle AI Database 26ai is the first commercial database to implement the new SQL:2023 standard, making it simple for anyone with SQL knowledge to define and query property graph models.
Database cloud services
- Agile, flexible, and secure database management
Oracle Cloud Infrastructure’s suite of optimized database capabilities enable customers to operate efficiently and at low cost, by closely aligning resources to their specific needs.
Companies implement digital transformations with Oracle’s full portfolio of database cloud services, including the fully managed Autonomous AI Database and the automated Exadata Database Service, with Exadata Cloud@Customer in distributed data centers and in Microsoft Azure, Amazon AWS, and Google Cloud using deeply integrated Oracle AI Database services in OCI that are collocated in those data centers.
Oracle Database Zero Data Loss Cloud Protect guards on-premises Oracle Databases with Oracle Zero Data Loss Recovery Service running in OCI, AWS, Azure, and Google Cloud multicloud environments. This feature includes real-time protection of database changes and enables fast recovery to any point-in-time and in any location.
Explore Oracle AI Database Cloud technologies
Explore Oracle Autonomous AI Database
Features include:
- Autonomous AI Lakehouse
- Autonomous AI Transaction Processing
- Autonomous AI JSON Database
- Oracle APEX
- Oracle AI Database@Azure
- Oracle AI Database@AWS
- Oracle AI Database@Google Cloud
- Oracle AI Database 26ai on Exadata Database Service with Exadata System Software 24ai
- Oracle AI Database 26ai on Exadata Cloud@Customer with Exadata System Software 24ai
- Oracle AI Database 26ai on Base Database Service
- Oracle Zero Downtime Migration
End data risk
Mission-critical innovations
- Oracle Database Zero Data Loss Cloud Protect
Protects on-premises Oracle Databases from data loss and ransomware using Oracle Zero Data Loss Recovery Service running in OCI, AWS, Azure, or Google Cloud. Includes real-time protection of database changes and enables fast recovery to any point-in-time.
- Globally Distributed Database
Supports ultra-scalability and data sovereignty by enabling a single logical database to be split into multiple parts and stored on different servers. Built-in RAFT-based replication enables multi-master, active-active distributed databases to fail over with zero data loss in less than three seconds.
- True Cache
Provides unique application-transparent middle-tier cache that automatically ensures transactional consistency. Developers don’t need to write code to populate and manage the data in the cache. True Cache brings the rich functionality of Oracle AI Database to mid-tier caches. All Oracle SQL, Vector, JSON, Spatial, and Graph query capabilities are also available via True Cache.
- SQL Firewall
Delivers in-database scalable protection against unauthorized SQL activity and injection attacks, enhancing security for all data in the database.
- Trusted Answer Search
Since probabilistic LLMs can occasionally hallucinate, enterprises can instead rely on Trusted Answer Search when answers must be deterministic. It uses AI Vector Search rather than an LLM to find the best matching resource in response to natural language questions.
- Deep Data Security
Helps protect against new AI-era threats, such as prompt injection, using declarative, database native controls that enforce end-user access privileges at the row and column level. By centralizing and decoupling security from application code, it enables customers to easily determine who can see what data, continuously update access rules as new threats emerge, and effectively provides guard rails for agents working within Oracle AI Database.
- Private AI Services Container
Enables customers with stringent security requirements to run private instances of AI models while avoiding sharing of data with third-party AI providers, or sending data outside of their firewall. In addition, it helps mitigate performance bottlenecks by allowing customers to securely offload compute-intensive AI tasks, such as vector embedding generation, outside the database, helping keep all data secure within their environment. The container can be deployed in the public cloud, on private clouds, or on-premises, including in air-gapped environments.
Data sovereignty and data residency
- Addressing data sovereignty and residency challenges
Store data in the cloud across multiple physical databases in multiple locations instead of one database while exposing a single database image to applications. Oracle Globally Distributed Database is used to achieve hyperscale and to help fulfill data residency and data sovereignty requirements. RAFT replication between the physical databases enables automatic failover with zero data loss in single-digit seconds, simplifying the creation and administration of fault-tolerant distributed databases and eliminating the need for manual processes to maintain active-active availability.
Database security
- Protect crucial data
Oracle’s database security solutions reduce the risk of data breaches and make it easier for customers to meet compliance requirements. Encryption, data masking, privileged user access controls, activity monitoring, and auditing enable IT teams to strongly secure Oracle AI Database environments and understand potential vulnerabilities.
Explore database security technologies
Features include:
High availability
- Comprehensive, high availability capabilities
Oracle AI Database includes built-in capabilities and options, including Oracle Active Data Guard and Oracle Real Application Clusters, that enable efficient, scaling and consolidation of customer databases. IT teams use the same capabilities on-premises and in Oracle Cloud Infrastructure to protect crucial customer databases and maximize their availability.
Explore high-availability technologies
Explore Oracle Maximum Availability Architecture
Features include:
Performance and scalability
- Optimized database performance throughout the enterprise
Meet stringent performance requirements in real-time environments and data center deployments, with a range of capabilities designed for optimized low-latency and high-throughput.