Oracle’s mult-icloud strategy turns AI inside-out, bringing intelligence to enterprise data everywhere
“Enterprises don’t need to move data to AI, on the contrary bring AI to the data,” says Harsh Birmi, Vice President, Oracle Cloud Infrastructure – Specialist Sales, APAC. “Oracle architects AI into the core of data management, furthering Oracle’s commitment to help customers securely bring AI to all their data, everywhere (on-premises & cloud (OCI, AWS, Azure & Google Cloud)”.
For all the enthusiasm around artificial intelligence, most enterprises still confront the same fundamental limitation: data gravity. Corporate data, the lifeblood of AI, does not sit in one place. It spans decades-old systems, modern SaaS platforms, departmental silos, and cloud environments chosen at different times for different purposes. Moving it is slow, expensive, and risky, particularly for industries bound by compliance and privacy rules.
Oracle offers the shortest path to AI by embedding AI inside the data platforms enterprises already rely on, and delivering those platforms wherever organisations want them: Azure, AWS, Google Cloud, Oracle Cloud Infrastructure, or on-premises.
A database designed to absorb innovation rather than fragment it
This shift begins with Oracle AI Database 26ai, a next-generation evolution of Oracle’s long-running converged database strategy. Rather than fragment innovation into separate databases, blockchain when it emerged, JSON when developers demanded flexibility, spatial and graph when connections mattered, and now vectors as generative AI takes hold, Oracle has integrated each datatype as features of the core database at no additional cost.
Oracle’s next generation AI native database eliminates the sprawl and inconsistent governance & access controls across fragmented environments introduced when organisations move data across multiple datastores for their enterprise AI initiatives. To turn AI initiatives to reality, customers can now address security concerns by adopting unified security & governance across all datatypes, down to row, column, and cell-level controls, masking policies, prevent data exfiltration & protect from ransomware attacks.
With AI vectors now stored directly in the database alongside structured, semi-structured, and unstructured data, enterprises no longer need to build replication pipelines into external vector stores. The database itself captures embeddings from text, images, audio, and video, allowing context-aware semantic queries to run natively. This is a foundational shift, AI no longer lives outside business systems; it resides in the same trusted environment where enterprise data already sits. Oracle enables a unified AI Vector search with ALL types of private data including relational data. Oracle can pass the results of this unified vector search on private data to an LLM to directly answer the user’s question i.e. Retrieval Augmented Generation (RAG) pipeline can be invoked directly in a single SQL statement. Oracle integrates with all popular app-tier agentic frameworks- GCP Vertex AI, AWS Bedrock, OCI Gen AI, LangGraph etc. Oracle database goes beyond just RAG with AI agents by architecting Agentic AI into the database, providing frameworks like Select AI Agent, Private Agent factory, MCP server to build, deploy & manage in-database AI agents.
Multi-cloud becomes real when AI and data sit side by side
This AI-built-in architecture grows exponentially more valuable under Oracle’s expanding multi-cloud ecosystem. The company first partnered with Microsoft in 2019 to interconnect OCI and Azure through ExpressRoute and FastConnect. That model allowed shared workloads, but still required dual tenancies, dual consoles, and bridging networks. Customers wanted more, the ability to run databases and applications side-by-side inside a single cloud tenancy, with unified identity, security, and monitoring.
Oracle responded with something unprecedented: Oracle database running on Oracle Cloud Infrastructure inside hyperscaler datacentres, beginning with Azure. What started as a single partnership has now extended to AWS and Google Cloud. The application tier and Oracle Database now live on the same virtual network inside the hyperscaler cloud of choice, eliminating the latency that once handicapped distributed AI workloads. Customers manage the entire estate using hyperscaler consoles, Azure Portal, AWS Console, or Google Cloud Console, with no need to separately learn OCI interfaces.
The commercial and operational implications are striking. Customers can procure Oracle Database@Hyperscaler (AWS, Azure, Google Cloud) through the hyperscaler marketplaces and count the spend toward the hyperscaler cloud commitment programs like MACC for Azure. Existing Oracle customers can reuse on-premises licenses or pay through license-included models. Oracle Support Rewards lower overall TCO by reducing Oracle technology support fees. By adopting Autonomous database, organisations can automate routine database administration tasks using AI & ML resulting in reduced operational costs, enhanced security & improved performance.
Risk mitigation moves in lockstep. The Oracle multi-cloud model supports Maximum Availability Architecture deployments, the same gold-standard frameworks banks and telecom companies have trusted on-premises for decades. Enterprises can layer Data Guard, Real Application Clusters, and GoldenGate for business continuity, while Zero Data Loss Recovery protects against ransomware and sub-second data loss.
Security becomes a multi-layered pattern: encryption by default, governance enforcement in the database, native integration with hyperscaler security stacks such as Azure Sentinel, Google Cloud Defender, and AWS Identity frameworks, and shared observability policies.
Open data, governed intelligence, and a new operating model for AI
This is particularly meaningful in countries like India, where regulatory regimes mandate data residency and sectoral regulators enforce strict availability standards. Oracle has expanded Database@Hyperscaler coverage into Indian regions, giving enterprises a choice of compliant hosting models across all major clouds without compromising sovereignty. Certification coverage spans PCI DSS, multiple SOC standards, ISO 27001 suites, HIPAA, and CSA STAR, providing CIOs confidence in deployment alignment with audit requirements.
Oracle is bringing its long history of embracing open standards and open platforms to the age of AI. Customers can use Oracle AI to analyse data in any data stores- on-premises ( databases – Oracle, DB2, PostgreSQL, Teradata, MySQL etc) & Cloud data sources (Snowflake, Databricks, AWS Redshift, Salesforce, ServiceNow etc) using Oracle AI Database’s advanced Federated query capabilities to create an AI proxy database.
Perhaps the most transformative component arrives in Oracle’s Autonomous AI Lakehouse, a fully open multicloud data plane built around Apache Iceberg. Rather than forcing organisations to rip data from S3 buckets or Azure Data Lake storage into proprietary systems, Iceberg lets teams query data “in place” while applying Oracle’s full AI and analytic stack.
The lakehouse automatically discovers metadata across platforms (Iceberg data catalogues (AWS Glue, Databricks Unity, Snowflake open catalogue), Datalakes (AWS S3, Google Cloud, Azure, OCI) & Databases (Oracle, MySQL, SQL Server) using its “catalogue of catalogues,” supports high-performance scanning via Data Lake Accelerator, and connects transparently with Spark, Python, and native SQL.
The result is a long-sought promise: enterprises can unify analytics, AI inference, operational data, and unstructured content without moving data or maintaining parallel infrastructures. Workloads run where they make the most sense, and can shift over time without rearchitecting.
“With this model, cloud becomes a flexible substrate beneath an AI-native data and governance layer,” Birmi adds. “The enterprise journey toward AI transformation is less about migration and more about activation.”
In a landscape where every enterprise wants to become AI-driven but few can re-engineer systems fast enough, Oracle’s multi-cloud data strategy offers a pragmatic and technically elegant solution. It meets customers where they are, keeps data where it already resides, and brings AI directly to the systems that manage mission-critical operations.