Oracle AI Database 26ai Powers the AI for Data Revolution

Major release of Oracle's flagship database architects AI into its core, seamlessly integrating AI across all major data types and workloads

Enables customers to achieve breakthrough insights, innovations, and productivity across multicloud and on-premises environments

New Oracle Autonomous AI Lakehouse supports the Apache Iceberg open table format, enabling customers to use the power of Oracle AI Database for their data lake data

LAS VEGAS, Oct. 14, 2025 /PRNewswire/ -- Oracle AI World -- Oracle AI Database 26ai architects AI into the core of data management, furthering Oracle's commitment to help customers securely bring AI to all their data, everywhere. This milestone advances Oracle's "AI for Data" vision of a next-generation AI-native database with use of AI across the entire data and development stack, including AI Vector Search, AI for Database Management, AI for Data Development, AI for Application Development, and AI for Analytics. Customers can now run dynamic agentic AI workflows to provide sophisticated answers and actions that combine private database data with public information.

"By architecting AI and data together, Oracle AI Database makes 'AI for Data' simple to learn and simple to use," said Juan Loaiza, executive vice president, Oracle Database Technologies, Oracle. "We enable our customers to easily deliver trusted AI insights, innovations, and productivity for all their data, everywhere, including both operational systems and analytic data lakes."

Oracle's "AI for Data" strategy is open and ubiquitous. Oracle AI Database's built-in AI capabilities provide customers wide freedom of choice when building and deploying AI applications including support for: the Apache Iceberg open table format; Model Context Protocol (MCP); industry-leading LLMs; popular agentic AI frameworks; and Open Neural Network Exchange (ONNX) embedding models. Oracle AI Database's mission-critical functionality brings AI to data securely, efficiently, and reliably wherever it resides—Oracle Cloud, leading hyperscale clouds, private cloud, or on-premises.

Oracle AI Database implements NIST-approved quantum-resistant algorithms (ML-KEM) to encrypt data-in-flight. Combined with the existing support for quantum-resistant encryption for data-at-rest, Oracle AI Database's data protection approach is designed to prevent hackers from harvesting organizational data now and decrypting it using quantum computers later. Other vendors have implemented quantum-resistant algorithms either in their network and storage architectures or in their database services, but not both.

"Great AI needs great data. With Oracle AI Database 26ai, customers get both. It's the single place where their business data lives—current, consistent, and secure. And it's the best place to use AI on that data without moving it," said Holger Mueller, vice president and principal analyst, Constellation Research. "To help simplify and accelerate AI adoption, AI Database 26ai includes impressive new AI features that go beyond AI Vector Search. A highlight is Oracle's architecting Agentic AI into the database, enabling customers to build, deploy, and manage their own in-database AI agents using a no-code visual platform that includes pre-built agents. As Oracle's converged database leadership in transaction processing goes unchallenged, its leadership position in the data and AI space continues to rise sharply as well."

Oracle AI Database 26ai is a long-term support release that replaces Oracle Database 23ai. Customers can simply apply the October 2025 release update to transition from 23ai to the currently available features of 26ai. Customers will receive the immediately available features and will be ready for additional features as they are released. There is no database upgrade or application re-certification required. Advanced AI features such as AI Vector Search are included at no additional charge.

Oracle AI Database 26ai features that are planned include:

Enterprise-wide AI and Analytics

• Oracle Autonomous AI Lakehouse: Supports the Apache Iceberg open table format, enabling true, enterprise-wide AI and analytics. It is now available on all four major hyperscalers—Oracle Cloud Infrastructure (OCI), Amazon Web Services, Microsoft Azure, and Google Cloud—and interoperable with Databricks and Snowflake on the same clouds. Autonomous AI Lakehouse enables customers to leverage their existing investments and gain the AI benefits of Autonomous AI Lakehouse for their business needs. Autonomous AI Lakehouse delivers Exadata-powered performance and pay-per-use serverless scaling. Learn more about Oracle Autonomous AI Lakehouse.

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.
• 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 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. In addition, unique Remote Direct Memory Access (RDMA) algorithms further accelerate AI by enabling ultra low-latency and high-throughput data access from storage and across nodes in a cluster. Automatic data tiering delivers the low latency of memory, the high IOPS of flash, and the capacity of disk, while also reducing storage footprint using hybrid columnar compression (HCC). Finally, Exadata Database Service on Exascale Infrastructure supports Oracle Database 19c in OCI, Azure, and Google Cloud, enabling a much broader range of workloads to leverage Exadata's superior performance and scale.
• Private AI Services Container: Provides a prebuilt and tested environment for running private instances of AI models such as embedding models, open-weight LLMs, and Named Entity Recognizers. Use of this container helps enhance AI workload security since customers can avoid sharing data with third-party AI providers. The container can be deployed anywhere the customer chooses, within the customer's tenancy in the public cloud, on private clouds, or on-premises.
• AI Database Acceleration with NVIDIA: Oracle AI Database 26ai APIs that enable integration with LLM providers also support integration with NVIDIA NeMo Retriever microservices. Using this feature, Oracle AI Database 26ai can run vector embedding models or implement RAG pipelines using previously provisioned NVIDIA NIM microservices. In addition, Oracle Private AI Services Container, which currently supports execution on CPU resources, has also been designed to support the future use of NVIDIA accelerated computing for vector embedding and index generation using CAGRA (CUDA ANN GRAph-based algorithm) in the NVIDIA cuVS (GPU-Accelerated Vector Search) library.

AI for Application Development

• Data Annotations: Help 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.
• 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 pre-built in-database tools, external tools via REST, and MCP servers, enabling the automation of multi-step agentic workflows, accelerating innovation, and helping organizations keep their data safe.
• AI Private Agent Factory: Provides a no-code AI agent builder and deployment framework. These agents benefit from the full power, performance, scalability, and security of the converged data architecture of Oracle AI Database. It runs as a container in any environment of the customers' choosing to enhance data security—without customers having to share data with agentic frameworks on third-party clouds.
• APEX AI Application Generator: To boost developer productivity, Oracle plans to deliver next generation APEX development tools that use natural language interfaces to provide trusted answers to user questions and to generate enterprise-grade business applications.

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. This 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 a 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.

Additional Resources

• Watch Juan Loaiza's keynote at Oracle AI World
• Read the technical blog about Oracle AI Database 26ai
• Read what industry analysts are saying about Oracle AI Database 26ai

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SOURCE Oracle