AI Data Platform
Oracle AI Data Platform unifies your enterprise data, applies the business context AI needs to act accurately, and deploys agents that automate workflows, decisions, and processes—across every function, at enterprise scale. It’s one platform, end to end, with a governed data foundation and embedded intelligence.
See AI deliver business outcomes
Join us at Oracle AI World in Las Vegas from October 25 to 28 to see how Oracle’s AI and other technology innovations are helping organizations solve challenges across industries. Hear from Oracle leaders, explore demos and case studies, and connect with experts and peers.
What's new in Oracle AI Data Platform?
-
Build AI That Works for Business
AI is only as good as its foundation. Discover how Oracle AI Data Platform provides a trusted, governed environment to help build accurate AI at scale.
-
3 Ways Enterprise AI Helps Pay Off for Financial Services Firms
Discover how Oracle AI Data Platform helps financial services firms unify trusted data, apply industry models, and embed AI into core workflows so teams can act on insights faster.
-
5 Ways Oracle AI Data Platform Can Benefit Your Business
Discover five key ways Oracle AI Data Platform can transform your enterprise data and AI initiatives.
Why Oracle AI Data Platform?
-
Unify enterprise data for AI
AI Data Platform unifies all types of data—structured, unstructured, batch, and real-time—across your enterprise into an open, and connected platform, laying the foundation for trusted and AI-ready data pipelines.
-
Accelerate AI development
An integrated environment with built-in tools and GenAI agent frameworks empowers your teams to build and deploy AI-powered applications faster—without the complexity of connecting separate tools.
-
Innovate with AI at scale
AI Data Platform enables cost-effective, enterprise-wide AI by harnessing OCI’s optimized infrastructure, minimizing data movement, and orchestrating AI solutions across Oracle and third-party environments so you can scale innovation and intelligence everywhere your business operates.
Oracle AI Data Platform customer success stories
-
University College Dublin aims to improve chronic care with Oracle AI Data Platform
Using Oracle AI Data Platform, the UCD Clinical Research Centre is transforming respiratory care—turning unstructured clinical data into actionable insights. By securely combining synthetic and open data sets, they built a decision-support tool that helps clinicians manage chronic disease more effectively and ultimately improve patients’ lives.
-
Clopay drives manufacturing success with Oracle AI Data Platform
Clopay® Garage Doors is using Oracle AI Data Platform to transform how it understands and serves its customers. With millions of unique SKUs across its product line, Clopay replaced manual spreadsheet analysis with AI-powered insights that accurately predict dealer churn and reveal trends before they happen—driving stronger business performance, better decisions, and improved profitability.
Explore Oracle AI Data Platform
Autonomous AI Database
Oracle Autonomous AI Database is a fully managed, cloud native database that automates provisioning, tuning, scaling, and patching—requiring no manual effort from DBAs or engineers.
Its role in AI Data Platform
Autonomous AI Database serves as the gold medallion layer within the platform’s architecture, housing the most trusted, query-ready data products for enterprise analytics and AI. It helps ensure that all downstream use cases start from governed, high-quality data.
- Automates data management, freeing up engineering time
- Delivers fast, query-ready data for analytics, ML, and GenAI
- Enforces lineage and role-based accessed control via the master catalog
- Powers reusable, trusted data sets as part of AI Data Platform’s medallion model
- Seamlessly connects to pipelines, notebooks, and AI services
- Scales elastically to meet dynamic AI and analytic workloads
Analytics
Oracle Analytics Cloud is a fully managed, cloud native analytics platform that empowers business users and data professionals to model, visualize, and explore data through self-service dashboards, pixel-perfect reports, and governed semantic models.
Its role in AI Data Platform
Analytics Cloud is the visual intelligence layer—transforming trusted data, such as gold medallion Autonomous AI Database assets, into actionable insights and AI-driven analytics. It closes the loop by enabling data consumption, data-driven business decisions, and feedback into the AI pipeline.
- Self-service analytics: Business users can build reports while administrators manage roles, permissions, and access via built-in role-based access control.
- Semantic modeling: Create reusable, business-friendly data models that reduce complexity and support consistency across reports.
- Seamless integration: Connects directly to AI Data Platform’s unified data layer and AI pipelines for real-time or batch analytics.
- Supports AI-derived visualizations: Use embedded ML, smart insights, and natural language queries to amplify data-driven decisions.
Generative AI service
A fully managed, enterprise-ready generative AI service on Oracle Cloud Infrastructure. It offers pretrained foundational models for chat, summarization, and embeddings plus fine-tuning capabilities and hosted endpoints on dedicated AI clusters.
Its role in AI Data Platform
Generative AI service acts as the intelligence engine within AI Data Platform, transforming governed data into smart, interactive AI experiences and closing the loop for insight-driven action.
- Plug-and-play LLMs: Accessible via APIs, CLI, and AI playgrounds that support chatbots, summarizers, classification, RAG, and embeddings.
- Custom tuning: Tailor pretrained models to your data using dedicated clusters for enterprise-grade customization with tight controls over data security.
- Enterprise-grade delivery: Enables reliable hosting with dedicated infrastructure, granting predictable performance and isolation.
- Seamless platform integration: Connects directly to AI Data Platform's data layers as well as embedding, summarization, generation, and question-answer workflows. Generative AI service plugs into gold medallion data sets for grounded, compliant outputs.
- Rapid ROI: Enables fast deployment of GenAI apps for synthesizing insights, automating workflows, and generating value from day one.
Object Storage
Oracle Cloud Infrastructure (OCI) Object Storage provides scalable, durable, low-cost storage for any type of data. Benefit from 11 nines of durability. Scale storage to nearly unlimited capacity for your unstructured data.
Its role in AI Data Platform
Object Storage acts as a foundational data lake layer within the platform’s architecture, enabling efficient storage and management of massive data volumes needed by AI and analytics pipelines. It enables any type of data to be reliably preserved, accessible, with security measures across all workloads.
- Seamlessly integrates with AI, analytics, and data engineering pipelines.
- Delivers virtually unlimited, scalable storage for any data type, including structured and unstructured data sets.
- Improves durability and redundancy with built-in data replication across availability domains.
- Provides access-controlled storage with encryption by default.
- Supports simple data ingestion and retrieval via standard APIs and SDKs.
- Scales elastically to meet dynamic data growth and performance needs.
AI Data Platform Workbench
Oracle AI Data Platform Workbench provides a unified development environment for building, deploying, and managing AI applications. It brings together data engineering, machine learning, and analytics workflows into one governed workspace that accelerates innovation while ensuring enterprise-grade security and control.
A collaborative user interface and dev environment for AI Data Platform
AI Data Platform Workbench acts as the collaborative development layer within the platform’s architecture, enabling teams to prepare data, train models, and operationalize AI in a single, governed environment. It connects data scientists, engineers, and analysts through shared tools, notebooks, and pipelines—all powered by integrated access to the platform’s underlying services and catalog.
- Provides a unified workspace for end-to-end data and AI development
- Seamlessly integrates with Autonomous AI Database, Object Storage, and Oracle Cloud Infrastructure (OCI) AI Services
- Enables collaborative workflows with shared notebooks and reproducible pipelines
- Automates data preparation, model training, and deployment through orchestration tools
- Ensures governance with catalog-based lineage, versioning, and access control
- Accelerates AI innovation by reducing complexity across the data-to-AI lifecycle
See AI Data Platform in Action
Data Engineer
Build a complete lakehouse from raw data to AI-ready gold
Oracle AI Data Platform gives data engineers one path from ingestion to consumption across structured and unstructured data. Raw data lands in object storage, is refined with Spark into trusted silver data sets, and is delivered as curated gold data ready for analytics, AI agents, and downstream applications.
Design pipelines in one managed workspace
Data engineers can build ingestion, transformation, and enrichment pipelines via workflow jobs without switching platforms. This shortens development cycles while keeping every stage access-controlled, reusable, and easier to operate at enterprise scale.
Run Spark and SQL where your data already lives
Teams can use Spark for large-scale processing and SQL for fast exploration and reporting, choosing the best engine for each workload. With in-place access across object storage and connected databases, engineers can analyze and prepare data without unnecessary movement or duplication.
Accelerate pipeline delivery with AI-assisted engineering
AI-assisted development helps engineers move from source connection to production-ready pipeline faster with code generation, smart recommendations, and guided workflow creation. Combined with direct Oracle Fusion data integration, teams can quickly transform ERP, HCM, SCM, and CX data into managed AI-ready assets.
Data Steward
A unified metadata layer across your entire Oracle Cloud data estate
The master catalog serves as the central metadata layer, registering and organizing metadata without moving or copying underlying data. It connects to data where it resides across Autonomous AI Database, Oracle Database, and OCI Object Storage, covering both structured assets, such as tables, views, and schemas, and unstructured data.
Consistent access control across all AI and data workloads
A centralized role-based access management framework designed to support secure data usage. Oracle AI Data Platform Workbench provides fine-grained access controls and policy management at the data layer, helping organizations manage how users and AI workloads access data while supporting scalable AI initiatives.
Complete visibility into data access across all AI and data workloads
Provide transparency and security with audit logs that track who accessed what data, when, and how. Gain end-to-end visibility across Oracle AI Data Platform Workbench for auditing and operational oversight.
Role-based access management across AI and data workloads
Define and manage roles across Oracle AI Data Platform Workbench to ensure users and AI workloads have appropriate access to data, tools, and platform resources. Granular role-based controls simplify administration, strengthen security, and make it easier to apply access policies.
Seamless data sharing
Securely share data across teams and projects without unnecessary duplication or data movement. Oracle AI Data Platform Workbench enables controlled, policy-driven data sharing that improves collaboration, accelerates access to trusted data, and simplifies enterprise-scale data operations.
Data Scientist
Experiment tracking for every team workspace
Experiments in Oracle AI Data Platform Workbench keep teams separated by workspace while autologging captures parameters, metrics, and artifacts, creating a reproducible history that reduces rework and makes it easy to rerun past experiments with controlled changes. AI Data Platform Workbench handles the administrative tasks so data scientists can focus on the science.
Promote the winning models to the registry with a one-click run comparison
Data scientists can filter and compare runs to identify the top performer, then register it from the experiment run into the master catalog–backed model registry with versions, tags, and custom fields, turning “best run” into a shared, discoverable champion asset. AI Data Platform automatically manages versions when improvements are made.
From best model to inference, no packaging overhead
Promote the best model into the registry with automatic versioning, tags, and custom metadata, making it easy to discover and reuse the right model without relying on institutional knowledge or side channels. Load the latest or a specific version directly into notebooks and run batch inference. AI Data Platform keeps everything from experimentation to inferencing simple, consistent, and repeatable.
End-to-end lineage helps to make models generated explainable and auditable
Registered models in AI Data Platform trace back to the exact experiment run that produced them, surfacing lineage and run conditions, such as hyperparameters, environment variables, metrics, and artifacts. Teams can understand what was built, how it was built, and why it performs the way it does.
AI Developer
A unified platform for building, managing, and deploying enterprise AI (Coming soon)
Oracle AI Data Platform brings together the tools, integrations, access management and audit capabilities teams need to take AI from development to production. Build agents and applications using visual low-code tools or code-first notebooks, grounded in enterprise knowledge through native vector store integration linked to the master catalog. Best-in-class LLMs are available via OCI Generative AI and Oracle AI Database 26ai, with full lifecycle management ensuring every AI asset is registered, versioned, and tested. Fusion AI Agent Studio integration lets custom agents embed directly into Oracle SaaS applications—closing the loop between AI development and real-world business workflows.
High-code agent development with full platform power (coming soon)
Define agent flows in code using LangGraph, open source frameworks, and third-party Python libraries within AI Data Platform Workbench. The built-in utilities library gives agents seamless access to model configuration, guardrails, and system tools, such as RAG, MCP, SQL, and prompt management. Code-built or canvas-built, all flows test through the same unified playground.
End-to-end observability built into the platform (coming soon)
Oracle AI Data Platform provides built-in observability tools that give teams full visibility into how their AI agents are performing in production. From tracing individual runs to monitoring outputs and surfacing issues in real time, teams can detect, diagnose, and resolve problems without leaving the platform.
Flexible agent building, testing, and debugging with traceability and an integrated playground (coming soon)
Oracle AI Data Platform supports both no-code, canvas-style agent flow definition and code-based development through LangGraph, giving teams the freedom to build the way they work best. Dedicated build and test panels let developers iterate quickly, with changes made in the build phase immediately available in a comprehensive testing playground so teams can move from idea to working agent flow without unnecessary friction.
Business User
All your agents in one place (coming soon)
Every AI agent your organization uses, whether built in-house, embedded in Oracle Fusion, or connected from third-party systems, will be accessible from a single conversational interface. Ask a question, get automatically routed to the right agent, and pick up exactly where you left off with full session history preserved across every interaction.
Featured AI Data Platform blogs
Agents vs. Workflows: Where Does the ROI Actually Live?
Most enterprises aren’t failing with AI agents because of the technology—they’re failing because they’re using agents where simple workflows would do the job better. This blog explains the key differences between workflows and autonomous agents, and how choosing the right approach impacts ROI, scalability, governance, and cost. It also provides a practical framework for deciding when agentic AI is truly worth the investment.
-
May 12, 2026
You Didn’t Sign Up to Be an Infrastructure Engineer
-
February 13, 2026
The 3 Building Blocks of Enterprise AI
-
December 8, 2025
Getting Started on your Oracle AI Data Platform Journey
