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Oracle Database@Azure Expands Regions and Integrations for Multicloud AI

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Oracle Database@Azure is expanding rapidly — adding regions, new integrations, and production customers — and the move is reshaping how enterprises plan multicloud database migrations, sovereignty controls, and AI-ready data pipelines.

Oracle Exadata server in a cloud-connected data center with Oracle and Azure clouds.Background​

Oracle and Microsoft’s multicloud collaboration has evolved from a connectivity partnership into a production-grade service that places Oracle-managed database services physically inside Azure datacenters while exposing those services through the Azure portal. This arrangement — marketed as Oracle Database@Azure — lets organizations consume Oracle Exadata, Oracle Autonomous Database, and related Oracle database services with low-latency access to Azure-native services, unified billing options, and the procurement convenience of the Azure Marketplace.
What’s new in the latest wave of announcements is twofold: geographic expansion and expanded integration. Oracle has made Oracle Database@Azure generally available in nine Azure regions (including its first presence in South America with Brazil South and a recent addition in Italy North), and the companies plan to add scores more regions by the end of the expansion roadmap. At the same time, Oracle and Microsoft have added deeper integrations with Azure services — including identity, key management, governance, Terraform support via Azure Resource Manager, and data mirroring to Microsoft Fabric — which are designed to lower friction for migrations and speed up multicloud application development.
This article synthesizes the announcements, validates the core technical claims, and provides an operational playbook and risk analysis for Windows-era enterprise administrators, DBAs, and cloud architects considering Oracle Database@Azure for mission-critical workloads.

Overview of the announced capabilities​

What Oracle Database@Azure delivers today​

  • Oracle Exadata Database Service on Oracle Database@Azure — customers can run Exadata-backed Oracle Databases that are managed by Oracle but reachable from Azure as if they were an Azure-native offering.
  • Oracle Autonomous Database integrations with Azure — connectors and tooling to integrate Autonomous Database with Azure Entra ID (identity), Azure DevOps, Visual Studio Code, Azure API Management, Microsoft Teams, and more.
  • Key management and governance options — support for storing database encryption keys in Azure Key Vault and integration with Microsoft Purview for unified data governance across Oracle workloads.
  • Infrastructure-as-code and provisioning — Azure Resource Manager (ARM)-based Terraform support for provisioning both Exadata Database Service and Autonomous Database from Azure.
  • Data replication to Microsoft Fabric — Open Mirroring (public preview) plus Oracle GoldenGate-based approaches to keep mirrored datasets synchronized between Oracle and Azure analytics surfaces.
  • Pricing and procurement parity — marketplace buys, BYOL (Bring Your Own License) options, and mechanisms to leverage existing Azure commitments.
These features are explicitly designed to preserve the full Oracle Database feature set — including Real Application Clusters (RAC), Data Guard, GoldenGate, and Oracle’s Maximum Availability Architecture (MAA) tiers — while enabling Azure-native applications and analytics to work directly against Oracle-managed data.

Geographic reach and the regional roadmap​

Oracle Database@Azure is now available in multiple global Azure regions — including, but not limited to, Australia East, Brazil South, Canada Central, East US, France Central, Germany West Central, Italy North, UK South, and US West (DR). Oracle and Microsoft publicly stated a plan to expand into many more Azure regions (several dozen planned by the end of the announced deployment window), plus a number of disaster-recovery-only regions to support resilient architectures.

Why this matters: business and technical rationale​

For enterprises: migration friction and license economics​

  • Lower migration friction — organizations with large, complex Oracle estates can migrate databases to Oracle-managed services without wholesale refactoring. That’s a major benefit: preserving Oracle features and behavior reduces the risk and cost of application changes.
  • Procurement simplicity — purchasing from the Azure Marketplace and using familiar billing constructs reduces the procurement overhead for organizations that already consolidate spend via Azure.
  • License reuse — BYOL options let enterprises reuse existing Oracle license investments, helping control costs and easing financial approval for migrations.

For developers and AI teams: low-latency AI and analytics​

By colocating Oracle database services in Azure datacenters and exposing them through Azure tooling, organizations can build data pipelines that blend Oracle transactional/analytical engines with Azure Machine Learning, Microsoft Fabric, Power BI, and other Azure-native services — with lower network latency and simpler identity integration.

For compliance and sovereign workloads​

Local region availability and disaster-recovery placements enable regulated industries to meet data residency rules while still benefiting from cloud-native analytics and AI. The ability to keep encryption keys in Azure Key Vault is particularly important for customers with strict key custody and compliance requirements.

Strengths: what’s most compelling​

  • Preservation of Oracle capabilities: Exadata, RAC, Data Guard — all the enterprise-grade Oracle features customers depend on — remain available. That reduces risk during migration and supports complex, mission-critical workloads without rearchitecture.
  • Multicloud proximity: The physical colocation and private interconnects (OCI FastConnect paired with Azure ExpressRoute in prior interoperability models) deliver the predictable low-latency connectivity enterprises require for cross-cloud architectures.
  • Operational model: Oracle operates and manages the database tier, reducing DBA lifecycle toil (patching, major upgrades, hardware refreshes), while Azure remains the platform for application, analytics, and governance tooling.
  • Integrated governance and identity: Azure Entra ID and Microsoft Purview integration simplify enterprise governance, audit, and identity controls across Oracle workloads — a tangible win for compliance teams.
  • Infrastructure-as-code support: ARM-based Terraform support allows cloud teams to codify and automate database provisioning in line with their existing Azure IaC pipelines.
  • Flexible procurement models: Marketplace purchasing and BYOL reduce procurement friction for enterprises already committed to Azure.

Caveats and risks: what to watch out for​

Vendor-reported metrics and customer stories require scrutiny​

Many performance numbers and customer outcomes cited in vendor announcements are vendor-reported or case-study results from specific migrations. Those outcomes are useful signposts, but they are not universal guarantees. When evaluating claims (e.g., latency figures, batch-run speedups, or cost claims), organizations should insist on proof-of-value testing in their own network topology and workload mix.

Shared responsibility and operational boundaries​

This model creates a two-vendor operational plane:
  • Oracle: manages the database infrastructure, engine-level patching, and Exadata hardware.
  • Microsoft/Azure: manages the surrounding cloud stack, identity, network, and the customer’s application layer.
That split requires crystal-clear runbooks, SLAs, and escalation paths. Without defined responsibilities, incidents can become “vendor ping-pong” events, delaying resolution for mission-critical outages.

Potential for opaque pricing in edge cases​

Although the offering promotes pricing parity and Marketplace availability, enterprise procurement may still encounter custom private offers or per-region SKU differences. Total cost of ownership (TCO) modeling must include interconnect/network fees, egress patterns, managed service premiums, and potential premium tiers for high-availability MAA configurations.

Data sovereignty and key custody tradeoffs​

Support for storing encryption keys in Azure Key Vault is an important step for key custody, but customers should validate:
  • Where logs and backups are stored.
  • Whether forensics and access logs satisfy local regulatory needs.
  • That recovery demonstrations meet retention and immutability requirements for their regulatory context.
Organizations with strict sovereign or classified-data requirements should validate that the deployed architecture meets every regulatory control — not only the headline statements.

Lock-in and exit planning​

While the model reduces refactoring, it also creates subtle lock-in vectors: moving large Oracle database footprints between cloud providers or back on-prem can still be expensive. Every migration plan should include tested exit and portability playbooks (schema export/import, GoldenGate replication strategies, or validated backups that the organization can restore in alternative environments).

Practical checklist for Windows and Azure administrators​

The following pragmatic checklist is tailored for IT teams planning a migration or greenfield deployment with Oracle Database@Azure.
  • Inventory and compatibility
  • Catalog database versions, dependencies (RAC, Data Guard, GoldenGate), and enterprise features in use.
  • Cross-check version support with Oracle Exadata and Autonomous Database SKUs.
  • Network and topology testing
  • Validate private interconnect options and measure round-trip latency between Azure application tiers and Oracle-managed Exadata instances.
  • Conduct load tests that mirror peak production concurrency to validate latency and throughput.
  • Security and identity mapping
  • Design identity flows between Azure Entra ID and Oracle’s role model.
  • Decide key management: Azure Key Vault vs. customer-managed keys; build key rotation and recovery plans.
  • Backup, recovery, and compliance
  • Map backup retention, immutability, and disaster-recovery pairings.
  • Validate Zero Data Loss Recovery Service (ZDLRA) and recovery rail tests for RTO/RPO objectives.
  • Procurement and licensing
  • Confirm BYOL eligibility, Marketplace ordering options, and whether Azure consumption commitments (MACC) apply.
  • Request private offer terms in writing and model TCO across three years with realistic egress/ingress assumptions.
  • Observability and runbooks
  • Ensure monitoring integration: Azure Monitor ingestion, SIEM log forwarding, and Exadata metrics visibility.
  • Publish incident runbooks that specify whether Oracle or Microsoft owns each remediation step.
  • Proof-of-value and pilot
  • Start with non-critical workloads or representative slices of production for a full migration rehearsal.
  • Validate performance, integration with Microsoft Fabric/Power BI, and end-to-end governance.
  • Exit and portability
  • Build a tested procedure for export or replication to an alternate platform if required.
  • Ensure backups are restorable outside of Oracle-managed infrastructure if long-term mobility is a requirement.

Operational patterns and migration tactics​

Lift-and-improve, not lift-and-shift​

The recommended practical migration approach for Oracle databases is lift-and-improve: migrate to Oracle-managed Exadata to gain immediate operational and performance benefits, but take the opportunity to modernize batch job orchestration, optimize indexing and statistics, and adopt cloud-native patterns for analytics. Typical migration toolchains include Oracle Zero Downtime Migration (ZDM), Oracle Data Guard, or Oracle GoldenGate for online migrations.

Co-locate analytics, keep the database authoritative​

A common pattern is to keep OLTP systems on Exadata while streaming or mirroring analytical datasets into Microsoft Fabric or Azure Data Lake for analytics and model training. This avoids heavy analytics queries impacting transactional throughput while giving analytics teams the near-real-time datasets they need.

Use Exascale for variable workloads​

Oracle has signaled plans to offer Exadata on Exascale infrastructure with hyper-elastic, pay-per-use economics. For workloads that have large, bursty compute needs — such as training large models or seasonal analytics — Exascale can lower the barrier to scale without heavy upfront right-sizing.

Security and compliance: deeper considerations​

  • Identity federation: Ensure mappings between Azure Entra ID and Oracle DB roles are audited and reversible. Role creep and orphaned privileges are common issues in hybrid identity models.
  • Key control: When keys reside in Azure Key Vault, validate access logs, dual-control operations (if required), and recovery procedures for lost or rotated keys.
  • Auditability: Verify that audit logs from Oracle services can be centralized into the organization’s SIEM in a way that meets retention and searchability standards for compliance audits.
  • Ransomware resilience: Immutable backups, zero-data-loss recovery where available, and cross-cloud immutability controls should be validated via periodic drills.

Business impact and use cases​

  • Telecommunications and large SaaS: Operators with global footprints (examples from early adopters include telecom and healthcare SaaS providers) gain unified database operations while keeping application surfaces in Azure for regional deployments.
  • Government and regulated sectors: The combination of local Azure region availability, MAA validated architecture, and FedRAMP/sovereignty compliance options makes this model attractive for public-sector workloads.
  • AI and analytics at scale: Organizations wanting to run inferencing and model training near production data can do so without duplicating large datasets or incurring unpredictable egress costs.

Verdict: when to consider Oracle Database@Azure​

  • Consider Oracle Database@Azure when you:
  • Have a substantial Oracle footprint and need to preserve features without large refactors.
  • Want to leverage Azure-native analytics and AI services with minimal network latency.
  • Require in-region residency and integrated governance tied to Azure.
  • Prefer consolidated procurement through Azure Marketplace or need to reuse Azure purchasing commitments.
  • Reconsider or proceed cautiously when you:
  • Need absolute vendor independence and maximum portability.
  • Require native Azure PaaS (e.g., Azure SQL) instead of Oracle Database for long-term cost reasons.
  • Have limited operational maturity around multicloud incident response and cross-vendor governance.

Final thoughts and recommended next steps​

Oracle Database@Azure represents a pragmatic evolution of the Oracle–Microsoft partnership: it removes some of the biggest obstacles to moving large Oracle estates into the cloud while preserving the option to continue building on Azure’s expanding AI and analytics surface. For enterprise Windows administrators and cloud teams, the offering is worth close evaluation — but only after performing realistic proof-of-value testing, validating procurement terms, and establishing crisp operational boundaries.
Recommended next steps for teams evaluating Oracle Database@Azure:
  • Start with a pilot: choose a representative, non-critical workload and run a full migration rehearsal.
  • Model TCO: include interconnect, managed service premiums, monitoring, and DR costs — and validate assumptions with vendor quotes.
  • Test recovery: run end-to-end disaster recovery drills and data portability tests.
  • Define SLAs and runbooks: formalize which vendor handles which failures and publish escalation matrices.
  • Educate stakeholders: ensure procurement, security, DBA, and application teams share a single migration playbook.
This is a major step for organizations that need enterprise-grade databases tightly coupled with Azure’s application and AI ecosystem. With proper validation, governance, and operational discipline, Oracle Database@Azure can deliver a high-performance, compliant, and manageable path to modern, multicloud application architectures.
Source: StreetInsider Oracle Database@Azure Powers Cloud Migrations for Organizations Across the World
 

Click to expand...
Oracle’s Database@Azure has moved from a regional pilot to a global offering: the company announced that Oracle Database@Azure is now available in 28 Microsoft Azure regions worldwide, with five additional regions scheduled to come online within the next 12 months — a significant step in Oracle and Microsoft’s multi‑cloud strategy that brings Oracle Exadata performance and new AI data services directly into Azure datacenters.

Oracle Database @Azure: autonomous AI lakehouse with cloud map and server racks.Background​

Since the first announcements that Oracle would operate Oracle Cloud Infrastructure (OCI) services inside other hyperscalers’ datacenters, the goal has been clear: make Oracle’s mission‑critical database services available where customers already run their applications. The initial phase of Oracle Database@Azure focused on bringing Oracle Exadata‑class performance into Azure and enabling Azure customers to consume Oracle database services with feature parity, unified support, and simplified purchasing via the Azure Marketplace. That strategy aimed to remove one of the main blockers for large enterprises migrating Oracle workloads to public cloud: reluctance to re‑architect business‑critical systems and the perceived performance penalty of moving off on‑premises Exadata hardware.
The October 14 announcement at Oracle AI World shifts the program from a regional rollout to a global scale. Oracle characterized this expansion as a response to accelerating demand for data platforms that can support AI workloads with low latency and strong compliance controls — and named enterprise customers such as Activision Blizzard as early adopters leveraging the service to accelerate their AI initiatives.

What changed: the expansion, the services, and the partner program​

Availability footprint: 28 regions and counting​

Oracle’s new availability milestone brings Database@Azure into 28 Azure regions across North America, Europe, Asia‑Pacific, and the Middle East, and Oracle says five more regions — Brazil Southeast, France South, North Central US, South India, and West Europe — are planned over the next 12 months. For enterprises, regional availability matters for latency, data residency, and regulatory compliance; expanding to 28 regions removes many of the geographic barriers that previously slowed migrations of Oracle database workloads to public cloud.

New generally available services​

Oracle announced three new database services that are now generally available on Database@Azure:
  • Oracle Base Database Service — a pay‑as‑you‑go option offering lifecycle automation for Oracle Database Enterprise Edition and Standard Edition workloads, intended to lower the entry cost and operational friction for migrations.
  • Oracle Autonomous AI Lakehouse — an AI‑centric lakehouse combining Oracle’s Autonomous AI Database with the vendor‑independent Apache Iceberg table format to enable open, interoperable access to large datasets for analytics and AI. Oracle positions this as a bridge between trusted database capabilities and modern open data formats for ML/AI workloads.
  • OCI GoldenGate — Oracle’s real‑time data replication and change data capture technology, offered to enable low‑latency data movement and hybrid or multicloud synchronization scenarios.
Each of these services addresses a different migration or modernization need: lower‑cost virtualized databases, lakehouse‑driven AI workflows, and continuous replication for modernization and continuity.

Partner resale program: resellers, private offers, and joint go‑to‑market​

A practical, commercial change is the launch of a partner program that allows Microsoft and Oracle partners to resell Database@Azure through the Microsoft Marketplace via private offers. The program reportedly requires partners to belong to both the Microsoft AI Cloud Partner Program and the Oracle PartnerNetwork to participate — an industry‑first approach that aims to unify channel incentives across both vendors. This model mirrors parallel reseller programs Oracle has introduced with other hyperscalers and is designed to accelerate adoption by letting customers buy and receive partner services directly through the marketplace channel they already use.

Why this matters: technical and commercial implications​

Putting Exadata performance into Azure​

At the heart of Database@Azure is Oracle’s Exadata engineering: Exadata hardware and software stack provides the I/O, RDMA networking, and database optimizations that many enterprises rely on for OLTP and data warehousing. Running Exadata infrastructure managed by Oracle inside Azure datacenters reduces the latency between application and database tiers and preserves many database‑level capabilities (RAC, Data Guard, Oracle Multitenant). For organizations that need predictable performance for mission‑critical apps, this is a material advantage over generic VM‑based database hosting.

Native Azure integration for cloud‑first AI workflows​

Oracle emphasizes that customers can now integrate Oracle data directly with Azure services such as Microsoft Fabric, Power BI, and Copilot Studio, enabling AI and analytics where the data resides. This reduces the need for bulk data movement and the latency/ingestion delays that can undermine real‑time AI scenarios. For teams building retrieval‑augmented generation (RAG) pipelines, semantic search, agentic workflows, or real‑time analytics, the combination of Oracle’s vector/vector‑enabled queries and Azure’s AI stack looks to be a practical path to production. Activision Blizzard’s public endorsement underscores enterprise interest in hybrid stacks that blend Oracle’s database features with Azure’s developer and AI tooling.

Commercial flexibility and licensing complexities​

The pay‑as‑you‑go posture of the Oracle Base Database Service is designed for flexibility and cost control, but commercial complexity remains. Oracle and Microsoft say customers can leverage existing Azure and Oracle commitments and licensing benefits, and the private‑offer marketplace channel simplifies procurement. However, enterprises will still need to carefully map licensing entitlements, support‑reward programs, and reserved capacity discounts to avoid unexpected costs, especially at scale. The partner resale angle may help managed service providers package migration services, but it also introduces a multi‑vendor billing model that requires tighter contract governance.

Technical deep dive: what to expect from the new services​

Oracle Base Database Service: VM‑backed Oracle with lifecycle automation​

The Oracle Base Database Service is positioned as a lower‑cost, flexible way to run Oracle Database on virtual machines inside the Exadata‑class environment. Expect features like automated provisioning, patching, and backups, plus the ability to run both Oracle Database 19c and newer versions (Oracle’s documentation previously signposted 19c and 23c/23ai compatibility in similar services). For migrations that don’t need full Exadata offload, this service may be the quickest route to lift and shift with fewer code changes.

Oracle Autonomous AI Lakehouse: Iceberg + Autonomous AI Database​

The Autonomous AI Lakehouse bundles Oracle’s Autonomous AI Database with the Apache Iceberg open table format, aiming to deliver an “open lakehouse” that scales for analytics and AI while preserving database reliability, governance, and SQL capabilities. The lakehouse concept matters because it reduces lock‑in to a single cloud provider or proprietary format by supporting Iceberg and standard data access patterns, while allowing Oracle’s optimizer, vector search, and built‑in AI functions to operate directly on the data. Oracle claims the Autonomous AI Database executes huge query volumes and embeds AI capabilities at no additional database licensing cost, but independent, third‑party benchmarks of real‑world AI workloads remain scarce. Organizations should validate those claims against representative datasets and workloads before committing large production footprints.

OCI GoldenGate: the glue for hybrid and multicloud data mobility​

OCI GoldenGate continues to be Oracle’s mechanism for low‑latency replication and change data capture across environments. On Database@Azure, GoldenGate supports near‑real‑time synchronization between on‑premises systems, Oracle Cloud, and Azure services. This is essential for blue/green migrations, hybrid disaster recovery, and feeding streaming analytics pipelines. However, GoldenGate’s operational complexity — especially at scale and across cloud networking boundaries — means teams should plan for robust testing, monitoring, and network provisioning to meet throughput and RPO targets.

Enterprise adoption: what Activision Blizzard’s example shows​

Activision Blizzard is the marquee customer cited in Oracle’s announcement and emphasizes two practical outcomes: (1) native, real‑time access to Oracle data from Azure, and (2) accelerated insight delivery using Microsoft Fabric, Power BI, and Copilot Studio. For large gaming companies or digital media firms that have heavy transactional databases alongside modern analytics and personalization pipelines, Database@Azure presents a hybrid path to modern AI without forcing a wholesale re‑platforming of the database tier. However, Activision Blizzard’s deployment is an early example; peer organizations should regard it as a reference point, not a turnkey playbook. Due diligence, proof‑of‑concepts, and cost modeling remain essential.

Risks, trade‑offs, and verification points​

1. Vendor claims vs independent validation​

Oracle’s press materials contain specific performance and capability claims (for example, large query rates and “AI embedded at no additional cost”). These assertions should be treated as vendor statements until independently validated. Enterprises should run representative benchmarks, capacity tests, and end‑to‑end RAG trials before committing mission‑critical workloads. Vendor claims are useful directional signals but not substitutes for customer‑side verification.

2. Data residency and compliance​

Regional expansion addresses many data residency concerns, but regulatory regimes differ. Legal teams must confirm that the specific Azure region’s controls, auditability, and physical infrastructure meet local rules (for example, healthcare, finance, or public sector requirements). Availability of a region does not automatically equal regulatory approval for all data classes.

3. Network architecture and latency design​

Although Exadata inside Azure reduces application‑to‑database latency compared with cross‑cloud traffic, network architecture still matters. Customers must architect VNETs, express routes, and peering carefully and provision sufficient RDMA and I/O capacity to meet transactional SLAs. Misconfigured networking or shared tenancy at the datacenter level can still create bottlenecks.

4. Licensing and cost unpredictability​

Oracle’s licensing terms are complex and historically have been a major source of disputes during cloud migrations. Even with marketplace private offers and claimed parity, organizations must reconcile their existing Oracle licenses, commit to clear support models, and validate the TCO of moving workloads versus rehosting or refactoring to cloud‑native alternatives. The partner resale program can add convenience but also another layer of commercial complexity.

5. Operational complexity of multicloud​

Multicloud solutions increase options but also operational overhead. Monitoring, observability, backup/recovery plans, and incident response must be coordinated across Oracle and Microsoft stacks. Clear runbooks, joint support SLAs, and streamlined escalation paths are non‑negotiable for mission‑critical services.

What enterprises and partners should do next (practical checklist)​

  • Run a targeted proof‑of‑concept that mirrors production workload patterns (OLTP, OLAP, or AI queries). Measure latency, throughput, and cost under realistic load.
  • Validate the Autonomous AI Lakehouse claims on representative data: test Apache Iceberg interoperability and end‑to‑end model training and RAG pipelines.
  • Map licensing and purchasing: compare marketplace private offers vs direct Oracle contracting, and model the TCO across scenarios (lift‑and‑shift, refactor, rebuild).
  • Test GoldenGate‑based replication at scale to verify RPO/RTO targets and to understand network egress and ingress patterns.
  • Confirm compliance: legal and security teams should evaluate regional controls and contractually validate data handling, support, and audit capabilities.
  • Establish joint runbooks and support escalation processes with both Oracle and Microsoft, and consider engaging trained partners who hold membership in both ecosystems to help with migration and operations.

Channel impact: opportunity for managed service providers and systems integrators​

The joint partner resale program is a commercial lever that creates a larger addressable market for partners who qualify under both partner networks. This is an opportunity for managed service providers (MSPs) and systems integrators to package migration and managed‑DBA services, offering turnkey transitions that blend Oracle Exadata operation with Azure AI and analytics. For partners, the program also implies new requirements: certification across both vendor platforms, joint go‑to‑market capabilities, and the ability to operate in mixed‑billing marketplace models. For customers, this should mean more choices and more packaged services — but also the need to assess partner capabilities carefully.

Market context: why hyperscalers and database vendors are converging​

The Database@Azure expansion is part of a broader industry dynamic: hyperscalers want to offer enterprise databases as a service to capture higher‑value workloads, while database vendors want to keep their customer base intact by bringing managed database capability into those hyperscalers’ footprints. For enterprises, this trend reduces migration friction: instead of choosing between “Oracle on‑prem” or “rewrite for cloud,” organizations get an option that preserves database features while enabling cloud‑native analytics and AI. That said, this convergence will intensify competition on pricing, capability parity, and partner ecosystems — and customers will be the ultimate arbiters through adoption and real‑world ROI.

Conclusion​

Oracle’s announcement that Database@Azure now spans 28 Azure regions and includes new generally available services — Oracle Base Database Service, Autonomous AI Lakehouse, and OCI GoldenGate — is a noteworthy milestone for enterprise multicloud adoption. The new partner resale program and high‑profile adopters like Activision Blizzard underline the commercial and technical traction of this approach.
For IT decision‑makers, the expansion removes many geographic and procurement obstacles to moving Oracle workloads into Azure, and the Autonomous AI Lakehouse gives a compelling path to AI on governed, high‑quality data. However, the practical value depends on rigorous verification: independent performance testing, careful licensing analysis, network architecture validation, and robust operational planning. Vendors’ claims are promising, but they must be validated with real workloads to transform marketing statements into production confidence.
Enterprises should treat this offering as a strategic option — one that combines the reliability of Oracle’s database platform with Azure’s AI ecosystem — but they should approach migration as a technically detailed, contractually explicit program, not as a simple checkbox.
Source: StreetInsider Oracle expands Database@Azure service to 28 regions globally
 

Oracle’s latest push to move high‑performance Oracle database services inside Microsoft Azure datacenters marks a new phase of multicloud pragmatism: the vendor says Oracle Database@Azure is now available in 28 Azure regions, adds multiple new AI‑centric database services, and opens a resale path for Microsoft and Oracle partners through the Azure Marketplace—changes designed to accelerate cloud migrations while keeping AI “near the data.”

Futuristic data centers labeled Oracle and Azure flank a glowing Lakehouse AI hub with a world map.Background​

Oracle Database@Azure is Oracle’s managed database footprint physically colocated on Oracle Cloud Infrastructure (OCI) hardware inside Microsoft Azure datacenters. The model keeps Oracle operating and managing the database tier—Exadata‑class infrastructure, Autonomous Database, and related services—while Azure provides the application, identity, analytics, and AI surfaces. That co‑location reduces network hops between Azure compute and Oracle data and aims to remove two common migration obstacles: performance concerns and procurement friction.
The October announcements bundle three practical advances:
  • Geographic expansion to broaden in‑region choices for latency and compliance.
  • New database services aimed at pay‑as‑you‑go economics and embedding AI capabilities directly in the database.
  • A partner resale program enabling qualified Microsoft and Oracle partners to sell Oracle Database@Azure through the Microsoft Marketplace.

What Oracle announced (summary of the key news)​

  • Oracle Database@Azure is reported to be available in 28 Azure regions, with five more regions planned over the next 12 months. The new live regions list includes North America, Europe, Asia‑Pacific, and Middle East locations.
  • Oracle Base Database Service is now generally available on Oracle Database@Azure: a pay‑as‑you‑go, lifecycle‑automated offering with Oracle APEX low‑code integration and independently scalable compute and block storage.
  • Oracle Autonomous AI Lakehouse (generally available) pairs Oracle Autonomous AI Database with the open Apache Iceberg table format and claims integration paths to Microsoft Fabric and Power BI to make AI/analytics easier across data platforms.
  • OCI GoldenGate is now available for real‑time replication across environments inside Oracle Database@Azure, enabling continuous data movement and synchronization.
  • A partner program permits Microsoft and Oracle partners—members of both Microsoft AI Cloud Partner Program and Oracle PartnerNetwork (OPN)—to resell Oracle Database@Azure via private offers in the Microsoft Marketplace. Oracle frames this as an industry‑first route for integrated channel resale on Azure.

Why this matters now​

Low‑latency AI and analytics by design​

Embedding Oracle databases in Azure datacenters is primarily about latency‑sensitive AI and analytics. Putting the database closer to Azure compute and model inference surfaces reduces time spent moving data, lowers egress exposure, and shortens RAG (retrieval‑augmented generation) and inference loops—critically important for real‑time analytics and agentic workflows. Activision Blizzard’s statement highlights exactly this pattern: Oracle as the authoritative OLTP/OLAP plane and Azure as the developer and AI plane.

Procurement and operational simplification​

Selling Oracle services through the Microsoft Marketplace—combined with BYOL (Bring Your Own License) and pay‑as‑you‑go options—removes procurement friction for Azure‑centric enterprises. For organizations that consolidate spend, marketplace purchasing plus Azure Consumption Commitments makes commercial sense and reduces billing complexity during migration.

Preservation of enterprise Oracle capabilities​

The offering preserves many Oracle enterprise primitives—RAC (Real Application Clusters), Data Guard, GoldenGate, and Exadata‑class I/O and RDMA optimizations—so teams can migrate without wholesale application refactors that would otherwise delay modernization projects. Oracle and Microsoft emphasize feature parity with on‑prem Exadata behavior.

Deep dive: the new services and what they deliver​

Oracle Base Database Service (now GA)​

  • What it is: a lifecycle‑automated, pay‑as‑you‑go service for Oracle Database Enterprise and Standard Edition workloads inside Azure datacenters.
  • Key capabilities:
  • Automated patching and lifecycle management.
  • Oracle APEX built‑in low‑code environment.
  • Independently scalable compute and block storage to better fit transient or variable loads.
  • Practical value: lowers the operational entry cost for teams moving dev/test and standard workloads into the Oracle‑managed plane while preserving Oracle features where needed.

Oracle Autonomous AI Lakehouse (now GA)​

  • What it is: an AI‑centric lakehouse combining Oracle Autonomous AI Database with the open Apache Iceberg table format and integration paths to analytics layers like Microsoft Fabric and Power BI.
  • Key capabilities:
  • Open table format (Iceberg) for cross‑platform interoperability.
  • Native ties to Oracle AI Database engine features for vector processing and RAG workflows.
  • A path to enterprise‑grade analytics while keeping transactional and analytic stores aligned.
  • Practical value: reduces glue code and dataset duplication for analytics teams that want to run models across multi‑platform datasets. Vendor claims on scale and performance should be validated with a proof‑of‑value.

OCI GoldenGate (now GA on Oracle Database@Azure)​

  • What it is: Oracle’s long‑standing real‑time replication and change‑data‑capture service, now available inside Oracle Database@Azure.
  • Key capabilities:
  • Low‑latency replication across on‑prem, OCI, and Azure analytics tiers.
  • Zero‑downtime migration patterns and continuous synchronization.
  • Practical value: a pragmatic mobility and modernization layer that enables hybrid/modern architectures without downtime, especially useful for staged migrations or analytics mirroring into Microsoft Fabric.

Region expansion: what 28 regions actually means​

Oracle’s announcement updates the availability map to 28 regions with five additional regions planned within the next 12 months. The expanded footprint includes major commercial regions across North America, EMEA, Asia‑Pacific, and the Middle East, and Oracle documents show frequent incremental updates to region support—confirmable through Oracle’s public documentation and Microsoft posts announcing region launches. For enterprises, a 28‑region footprint significantly lowers geographic barriers for latency‑sensitive workloads and helps with data residency for regulated sectors.
That said, regional availability is not a binary guarantee of identical capabilities: certain SKUs, Exadata configurations, or disaster‑recovery pairings may come later or be offered only as single‑zone vs multi‑zone SKUs. Operational teams must verify which feature sets (for example, Exadata on Exascale, Autonomous Database SKUs, or GoldenGate) are available in their target region and whether local interconnects are configured to meet latency and throughput requirements. Oracle’s “what’s new” and Microsoft region posts are useful, regularly updated reference points for those checks.

The partner program: why resellability matters​

Opening marketplace resale to partners matters for procurement, professional services, and migration projects. The program requires channel partners to be members of both the Microsoft AI Cloud Partner Program and Oracle PartnerNetwork (OPN) to resell through private offers in the Microsoft Marketplace.
Why this changes the market:
  • Partners can deliver integrated migration packages that include Oracle Database@Azure procurement, professional services, and managed operations billed through Azure procurement flows.
  • Customers that prefer partner‑led transformations will have clearer procurement paths and combined support contracts—if partners negotiate effective SLAs and escalation routes with both vendors.
  • This commercial option can reduce friction for organizations that previously had to manage separate Oracle and Microsoft commercial relationships for the same multicloud project.
Caveat: partner economics and eligibility criteria will materially shape the availability and pricing of partner offers. Organizations should validate partner statuses, private offer terms, and BYOL eligibility before committing to large migrations.

Technical implications for architects and DBAs​

Latency and networking​

The core technical benefit rests on private interconnects and datacenter proximity. Historically this configuration pairs OCI FastConnect and Azure ExpressRoute patterns to deliver predictable, low‑latency connectivity. That model reduces the unpredictability of public internet hops for chatty OLTP applications and for AI inference workloads that make frequent lookups into vector indexes or RAG stores. However, real latency and throughput are site‑ and network‑design dependent—proof‑of‑value testing under realistic load is essential.

Exadata performance and feature parity​

Exadata engineering (RDMA networking, specialized I/O stack, offload optimizations) remains core to the pitch for mission‑critical OLTP and warehouse workloads. Oracle’s promise is to preserve behavior and enterprise features (RAC, Data Guard) so that migrations don’t require extensive reengineering. That preserves existing SLAs and operational models but creates a two‑vendor operational plane that requires ironclad runbooks.

Security, identity, and key management​

The offering touts integration with Azure identity (Azure Entra ID) and key custody via Azure Key Vault. For regulated customers this is helpful, but identity mapping, audit trail centralization, and proof that audit artifacts meet retention and forensic needs require explicit verification. Storing keys in Azure Key Vault reduces custody risk—but organizations must test log collection, role mappings, and key recovery procedures in full.

Operational model and shared responsibility​

The Oracle‑in‑Azure construct is a two‑vendor operational model with clear shared responsibilities:
  • Oracle: manages the Exadata hardware, database engine, and database‑plane lifecycle (patches, engine upgrades, certain backups).
  • Microsoft/Azure: handles the surrounding compute, identity, governance, network, and Azure platform services.
  • Customer: must define where application logic, access controls, monitoring, and incident management live, and enforce runbooks that prevent “vendor ping‑pong” during incidents.
A robust RACI (Responsible, Accountable, Consulted, Informed) for DBAs, cloud platform teams, vendor support teams, and application owners is non‑negotiable for mission‑critical systems.

Cost, licensing, and procurement realities​

  • Marketplace parity and BYOL reduce initial friction, but total cost of ownership models must include:
  • Managed‑service premium for Oracle‑operated Exadata.
  • Interconnect costs and any egress patterns across clouds.
  • Private offer pricing variability and partner professional services fees.
  • Procurement teams should insist on clear MACC (Microsoft Azure Consumption Commitment) treatment and confirm whether private offers count against existing commitments.
  • Exit planning matters: ensure backups and replication paths are portable and that restore plans are practiced into non‑Oracle managed environments to avoid surprise migration costs later.

Risks, caveats, and verification checklist​

  • Vendor‑reported metrics need third‑party validation.
  • Many acceleration and throughput claims in vendor announcements are useful directional signals but are not absolute guarantees. Run a proof‑of‑value for representative workloads. Flag vendor claims as vendor‑reported until validated in your environment.
  • Operational complexity from two vendors.
  • A split operational model increases the risk of unclear escalation paths. Define SLAs and test incident response scenarios in advance.
  • Potential for subtle lock‑in.
  • While migration is technically easier, moving large Oracle database estates again (off the managed Exadata plane) remains complex. Test exit workflows (GoldenGate replication, validated exports) before large migrations.
  • Regulatory nuance is not solved by presence alone.
  • In‑region availability helps residency controls, but compliance requires complete control‑mapping (logs, key custody, forensic readiness). Don’t assume “in‑region” equals compliance without legal and controls review.
  • Agentic AI safety considerations.
  • The combination of near‑data AI, Copilot tooling, and agentic workflows increases the importance of provenance, traceability, and human‑in‑the‑loop guardrails. Operational teams must codify governance policies before enabling agentic automation against financial or HR systems.

Practical migration playbook (concise)​

  • Inventory and compatibility
  • Catalog DB versions, RAC/Data Guard/GoldenGate usage, feature dependencies, and third‑party integrations.
  • Proof‑of‑value pilot
  • Pick a representative production slice; validate latency under peak concurrency, test RAG/query latency with model pipelines, and measure cost and throughput.
  • Network and security validation
  • Validate ExpressRoute/OCI FastConnect patterns, measure round‑trip latency, and audit Azure Entra → Oracle role mappings.
  • Backup and DR drills
  • Validate Zero Data Loss recovery options, test restores into alternate environments, and verify retention/immutability for compliance.
  • Operational runbooks and SLAs
  • Build incident runbooks that specify ownership, contact trees, and escalation windows to prevent ping‑pong.
  • Procurement and contracts
  • Secure written private offers, confirm MACC treatment, and document exit rights and portability guarantees.
  • Iterate to production
  • Gradually increase footprint after the pilot validates the TCO, SLAs, and technical outcomes.

What this means for Windows admins, DBAs, and cloud architects​

  • For Windows‑centric shops already invested heavily in Azure, Oracle Database@Azure creates a pragmatic path to modernize with minimal application refactor while opening Azure‑native AI/analytics tooling for insight delivery.
  • DBAs benefit from reduced hardware lifecycle management but must adopt cross‑vendor operational skills (defining SLAs and runbooks that integrate Oracle and Azure support models).
  • Cloud architects gain a faster path to AI‑near‑data architectures but must ensure that identity, logging, and governance frameworks span both vendors.

Final assessment​

Oracle Database@Azure’s expansion and new services represent a meaningful step toward pragmatic multicloud adoption: it preserves enterprise Oracle features while enabling Azure‑native AI and analytics with lower latency and simplified purchase options. The resellability via Microsoft Marketplace and the addition of pay‑as‑you‑go offerings lower the commercial barriers for Azure‑centric enterprises.
However, the offering is not a plug‑and‑play panacea. Vendor‑reported performance and cost benefits must be validated with realistic proofs‑of‑value. The dual‑vendor operational model requires rigorously documented runbooks, SLOs, and tested exit paths to avoid surprise lock‑in or operational friction. Enterprise teams should treat vendor announcements as a starting point for disciplined pilots rather than as final performance guarantees.
Organizations that combine careful pilot testing, contractual clarity, and strong cross‑vendor runbooks will find Oracle Database@Azure a powerful tool in a multicloud toolbox—especially where preserving Oracle feature sets while gaining access to Azure’s AI and analytics services is a priority.
Source: Oracle https://www.oracle.com/de/news/anno...or-organizations-across-the-world-2025-10-14/
 

Oracle and Microsoft’s multicloud play just took a decisive step: Oracle Database@Azure is now available in 28 Azure regions, ships new AI‑centric database services (including an Oracle Autonomous AI Lakehouse), and introduces a partner resale program — moves designed to accelerate cloud migrations while keeping AI “near the data.”

A neon Oracle cloud connects Azure Lakehouse to AI models in a data center.Background / Overview​

Since the first co‑location experiments, the core idea behind Oracle Database@Azure has been simple and pragmatic: run Oracle’s engineered Exadata and Autonomous Database stack on Oracle Cloud Infrastructure (OCI) hardware physically located inside Microsoft Azure datacenters, let Oracle operate and manage the database tier, and let Azure host the application, analytics, and AI plane. That separation aims to preserve Oracle’s enterprise database features while delivering low latency to Azure services used for reporting, analytics, and AI.
The October announcements (made at Oracle AI World) formalize a broader global footprint, add AI‑focused database capabilities, and create a marketplace route for Microsoft and Oracle partners to resell the service — a concrete signal that both vendors expect mainstream enterprise consumption rather than pilot‑only adoption.

What changed: regions, services, partners​

Global reach expanded — 28 regions now, more coming​

Oracle says Database@Azure is now available in 28 Microsoft Azure regions across North America, EMEA, Asia‑Pacific, Australia, and the Middle East, and that five additional regions will be added within the next 12 months. The live regions list published by Oracle includes major locations such as Australia East and Southeast, Brazil South, Canada Central and East, Central India, East US and East US 2, France Central, Germany North and Germany West Central, Italy North, Japan East and West, North Europe, Southeast Asia, Spain Central, Sweden Central, Switzerland North, UAE Central and North, UK South and West, and multiple West US variants. Oracle also named Brazil Southeast, France South, North Central US, South India, and West Europe as planned expansions.
This reach matters because in‑region availability affects latency, sovereignty, and compliance choices — especially for regulated industries where in‑country hosting or disaster‑recovery locality is required. Several independent regional announcements during 2024–2025 hinted at this trajectory; the October update simply consolidated those into a larger global footprint.

New database services (generally available)​

Oracle promoted three headline services as generally available on Database@Azure:
  • Oracle Base Database Service — a lifecycle‑managed, pay‑as‑you‑go option with automated provisioning, patching, Oracle APEX low‑code support, and independently scalable compute and block storage.
  • Oracle Autonomous AI Lakehouse — a lakehouse that combines Oracle Autonomous AI Database and Oracle AI Database 26ai with the open Apache Iceberg table format and Exadata performance optimizations, designed to let organizations run analytics and AI across platform boundaries (integration points to Microsoft Fabric and Power BI were highlighted).
  • OCI GoldenGate (GA) — Oracle’s established real‑time replication/change‑data‑capture technology, now available to synchronize data across systems inside the Oracle‑in‑Azure fabric.
Oracle frames the Autonomous AI Lakehouse as an “open” path for AI analytics that keeps the database as the authoritative data plane while enabling model application and visualization in Azure tooling. Independent reporting confirms these services are being pushed to customers now rather than remaining in preview.

Partner resale program​

For the first time Oracle says Microsoft and Oracle partners will be able to resell Oracle Database@Azure through the Microsoft Marketplace via private offers, provided they meet joint partner requirements (membership in the Microsoft AI Cloud Partner Program and Oracle PartnerNetwork, per the announcement). That commercial change is an important operational lever: it makes marketplace procurement and managed services by integrators a single‑stop option for customers already standardizing procurement on Azure.

Customer spotlight: what Activision Blizzard’s endorsement reveals​

Activision Blizzard was named as a prominent customer using Oracle Database@Azure to accelerate “agentic AI” workflows. The company’s finance engineering lead described being able to move Oracle workloads to a managed Exadata class environment inside Azure without compromise — getting native, real‑time access to Oracle data from Azure and integrating that data with Microsoft Fabric, Power BI, and Copilot Studio to accelerate insight delivery and agentic workflow creation. That language underscores a recurring pattern: keep Oracle as the authoritative OLTP/OLAP store, and use Azure for model hosting, orchestration, visualization, and developer surfaces.
This is not a trivial testimonial. Gaming platforms and digital media businesses typically have high throughput, low latency requirements and complex transactional systems — an environment that stresses database consistency and availability. Activision Blizzard’s public endorsement suggests the setup can meet demanding production workloads, but it should be treated as a reference case rather than universal proof. Prospective customers will still need proof‑of‑value testing using their own workloads.

Why this matters for AI adoption​

AI‑near‑data: lower latency, fewer copies​

One of the strongest technical rationales for Oracle Database@Azure is AI‑near‑data. Putting an Exadata‑class Oracle database inside an Azure datacenter reduces the network hops between operational data and Azure model hosting or inference services. That shortens retrieval‑augmented generation (RAG) loops, reduces egress and duplication costs, and can materially lower inference latency for production AI use cases. Oracle explicitly markets the database with AI capabilities embedded (vector processing, RAG primitives) so customers can run more of those operations inside the managed database without a separate, external vector store.

Exadata X11M and vector search acceleration​

Oracle’s Exadata X11M platform — the hardware underpinning many Oracle claims about database performance — introduced multiple AI optimizations earlier in 2025. Oracle’s published numbers indicate up to 55% faster persistent vector index (IVF) searches and 43% faster in‑memory vector index (HNSW) queries compared with the prior generation, thanks to intelligent storage‑side offload, Exadata RDMA (XRMEM), and flash optimizations. Independent trade press coverage and analyst commentary have validated the engineering approach (offload of intensive vector filtering to storage plus RDMA reduces CPU and I/O bottlenecks), but as always, vendor numbers are best validated with customer‑representative tests.

Technical deep dive — what to expect when you test or migrate​

Key architecture characteristics​

  • Oracle operates the Exadata hardware and database stack running on OCI infrastructure physically present in Azure datacenters. Azure manages the application, identity, and AI surfaces.
  • Networking between the Azure tenant and Oracle’s in‑region Exadata uses private, low‑latency paths to minimize hops and provide predictable performance; procurement and billing can be done via the Azure Marketplace (pay‑as‑you‑go, BYOL, private offers).
  • Identity and governance integrations tie Oracle roles to Azure Entra ID and allow key custody with Azure Key Vault for customer‑managed keys. Integration with Microsoft Purview for governance was also documented.

Migration pathways and data movement​

  • GoldenGate provides near‑real‑time replication for migrations and active‑active setups. Oracle also promotes Zero Downtime Migration (ZDM) tooling for lift‑and‑shift with minimal service interruption.
  • For analytics and AI pipelines, Oracle’s Autonomous AI Lakehouse supports Apache Iceberg tables to ease cross‑platform access and reduce vendor lock‑in of analytical datasets. Microsoft Fabric integration (and an Open Mirroring preview in some docations) provides a route to keep analytic surfaces synchronized without wholesale copies.

Operational split and runbooks​

The shared model increases the need for precise runbooks and RACI assignments:
  • Define who owns backup, patching, and DR within the Oracle‑operated Exadata environment vs. the Azure app/AI stack.
  • Validate recovery drills and test backup portability; insist on contractual SLAs that map incident ownership to responsive escalation processes.
  • Verify monitoring visibility and where telemetry lives — Azure Monitor vs. Oracle console — and prepare cross‑vendor alerting playbooks.

Commercial and licensing realities​

Oracle markets pay‑as‑you‑go options and BYOL support through the Azure Marketplace, enabling customers to leverage existing Azure Consumption Commitments (MACC) and Azure procurement flows. The new partner resale program lets qualified integrators and managed service providers package and sell Database@Azure via private offers — a convenience that can speed procurement, but it also creates a multi‑vendor billing and support chain that requires tighter contractual governance. Prospective customers must model TCO including managed service premiums, interconnect costs, potential egress, and any differences between Marketplace pricing and direct OCI or on‑prem licensing.

Strengths: what’s compelling​

  • Preservation of enterprise Oracle features. Real Application Clusters (RAC), Data Guard, Exadata offload and other enterprise primitives are kept intact, removing a major migration blocker for applications that cannot be refactored easily.
  • Low‑latency AI/analytics. Co‑location with Azure compute and AI services reduces latency and duplicate data movement for RAG and real‑time inference workloads.
  • Commercial convenience. Marketplace procurement, BYOL options, and private offers lower procurement friction for Azure‑centric enterprises.
  • Open lakehouse support. The Autonomous AI Lakehouse’s use of Apache Iceberg helps create interoperable analytics layers across clouds, a useful design for teams that want to avoid single‑vendor lock‑in at the lakehouse level.

Risks and caveats — what IT teams must validate​

  • Vendor‑reported performance needs validation. Oracle’s performance figures for Exadata X11M and Oracle AI Database features are vendor‑reported and impressive, but customers should validate gains against representative production workloads and not assume parity across all query patterns. Independent technical press and analysts have corroborated the architectural benefits, but real workload testing is essential.
  • Operational complexity and “vendor ping‑pong.” The split operational model increases the potential for delayed incident resolution unless runbooks, SLAs, and escalation matrices are explicit. Document “who does what” for monitoring, backups, and DR.
  • Potential cost and lock‑in considerations. While BYOL and marketplace buys reduce short‑term friction, large Oracle footprints can be costly to shift away from managed Exadata environments; contract exit clauses and data portability testing are vital.
  • Compliance nuance. Region availability helps residency requirements, but in‑region hosting does not automatically equal regulatory compliance. Data classification, control mapping, and audit readiness must be reviewed against local rules.

Practical checklist for Windows admins, DBAs and cloud architects​

  • Inventory: Catalog database versions, use of RAC/Data Guard/GoldenGate, and schema dependencies.
  • Compatibility check: Confirm supported Oracle Database versions (19c, 23c/23ai/26ai paths) and Exadata compatibility for your workloads.
  • Networking and latency testing: Design Azure VNet topologies and private connectivity (ExpressRoute/OCI FastConnect style patterns); measure round‑trip latency with realistic concurrency.
  • Pilot: Run a proof‑of‑value using GoldenGate or ZDM to replicate representative production slices. Validate RAG and vector search latencies against the chosen Azure AI stack.
  • Security & key custody: Map identity between Azure Entra and Oracle roles; validate key rotation and audit trails for keys stored in Azure Key Vault.
  • Backup & DR exercises: Configure Zero Data Loss Recovery Service where needed and run recovery drills; ensure immutable backup retention is contractually defined.
  • Commercial model: Confirm BYOL rules, Marketplace SKUs, MACC applicability, and partner resale terms for any managed services you plan to buy.
  • Exit strategy: Test restore to a non‑Oracle‑managed environment (export, GoldenGate sync to an alternate store) to validate portability.

Governance and AI safety: an operational priority​

“Agentic” AI workflows — systems that autonomously pursue multi‑step tasks — are among the use cases Oracle and customers like Activision Blizzard have highlighted. These workloads amplify governance needs: model provenance, data lineage, human‑in‑the‑loop controls, immutable audit trails, and per‑agent permissioning all become operational requirements. Organizations should ensure every agentic workflow has explicit guardrails, traceable data provenance, and supervised escalation paths before moving beyond low‑risk pilots. Vendor toolchains (Copilot Studio, Microsoft Fabric governance features, Azure AI Foundry) provide primitives, but enterprise policy and runbooks must be adapted to the new threat surface.

Final assessment and recommendations​

Oracle Database@Azure is a meaningful evolution of multicloud pragmatism: it lets enterprises retain a full Oracle feature set and Exadata‑grade performance while accessing Azure’s AI and developer ecosystem. That architectural approach aligns tightly with real enterprise constraints — large Oracle estates, strict compliance regimes, and the need to move quickly on AI.
For organizations considering Database@Azure:
  • Treat vendor claims as starting points; require representative proof‑of‑value tests that include network, RAG and inference latency, and cost modeling for at least 12–36 months of expected usage.
  • Map operational responsibilities and SLAs upfront; don’t assume issues “will be handled” by default.
  • Use the partner resale program wisely — third‑party integrators can shorten time‑to‑value, but confirm contractual responsibilities for billing, support, and incident escalation.
  • Prioritise governance for AI workflows; agentic or autonomous agents are powerful but require robust evidence of traceability, control, and human oversight.
Oracle’s announcement is both strategic and practical: it removes important migration blockers and stitches Oracle’s database strengths into Azure’s AI fabric. The result is a convincing option for enterprises that want to push advanced analytics and generative AI against trusted, mission‑critical data without wholesale rearchitecture — provided they validate performance, control the commercial terms, and operationalize governance.
In short: Oracle Database@Azure makes the promise of multicloud AI more tangible — but the real benefit will be earned one proof‑of‑value at a time, with careful attention to runbooks, licensing, and governance.
Source: IT Brief Australia Oracle Database@Azure expands global reach, drives AI adoption
 

Oracle Database@Azure’s latest wave of updates is a clear pivot from proof‑of‑concept to full production‑grade multicloud — adding AI‑ready data pathways, broader regional coverage, deeper security and governance controls, and partner‑centric migration programs that together make it a practical option for enterprises that must keep Oracle database capabilities while moving toward Azure‑centric AI and analytics.

A blue cloud icon receives data from stacked cylinders into a glowing pool labeled OneLake.Background / Overview​

Oracle Database@Azure is the collaboration that places Oracle‑managed Oracle Database services (Exadata, Autonomous Database, and now Base Database Service) on Oracle Cloud Infrastructure (OCI) hardware physically located in Microsoft Azure datacenters. The model keeps Oracle operating the database tier while Azure provides the application, AI, identity, and developer surfaces, and enables customers to purchase and manage those Oracle services through Azure billing and marketplace flows. This approach was first announced in 2023 and has steadily evolved with added services, regional rollouts, and integration points.
Why this matters: enterprises with large Oracle estates gain a migration path that preserves enterprise primitives — RAC, Data Guard, GoldenGate, Exadata I/O/RDMA optimizations and Oracle MAA levels — while gaining low‑latency access to Azure analytics and AI services for retrieval‑augmented generation (RAG), vector search, and agentic workflows. That architectural separation — Oracle as the authoritative data plane, Azure as the AI/application plane — is the feature set many large organizations asked for when they wanted to modernize without rewriting mission‑critical applications.

What’s new (high‑level summary)​

  • AI‑ready data integration: Zero‑ETL mirroring of Oracle databases into Microsoft Fabric’s OneLake (public preview) and a native Oracle GoldenGate integration for managed, low‑latency replication.
  • Expanded service portfolio: Oracle Base Database Service (VM‑based) is now available on Oracle Database@Azure, supporting Oracle Database 19c and 23ai, alongside Exadata Database Service on Dedicated and Exascale infrastructures and Autonomous Database options.
  • Broader regional footprint: Microsoft and Oracle report continued region expansion — Microsoft’s announcement states availability in 33 Azure regions (with on‑going additions announced at events such as Oracle AI World). Note: Oracle’s prior press releases tracked an earlier count (for example, 14 or 28 regions at prior milestones), so region counts have increased over successive rollouts and customers must verify exact SKUs and capabilities in their target region.
  • Security & governance integrations: Microsoft Defender for unified threat detection and Microsoft Sentinel SIEM paths, plus Azure Entra ID and Azure Key Vault support for key custody and identity. Azure Arc support extends Azure management and policy to Oracle Database@Azure infrastructure.
  • Commercial & partner programs: Azure Accelerate benefits to support migrations (technical assistance, Azure credits, Cloud Accelerate Factory) and a reseller model that lets qualifying Microsoft AI Cloud Partners and Oracle PartnerNetwork members resell Oracle Database@Azure via the Microsoft Marketplace.

Deep dive: AI‑ready data paths (OneLake mirroring + GoldenGate)​

Mirroring Oracle into Microsoft Fabric / OneLake​

One of the most operationally impactful features is database mirroring into Microsoft Fabric’s OneLake. Mirroring enables near‑real‑time replication (database mirroring and open mirroring approaches) so analytical and AI workloads in Fabric can query up‑to‑date Oracle data with zero‑ETL or minimal copy patterns. Fabric’s documentation and product announcements show Oracle mirroring in public preview, support for mirrored Data Agents (for LLM‑enabled insights), and Direct Lake/Delta formats to power Power BI and Fabric analytics workloads directly on mirrored data. This reduces the typical data movement and synchronization complexity that plagues RAG and vector‑driven AI pipelines.
Benefits:
  • Lower latency for inference and RAG pipelines by keeping analytics close to operational data.
  • Reduced data duplication and governance friction — mirrored data is cataloged and governed inside OneLake.
  • Enables Fabric Data Agents to run LLM‑powered queries against mirrored Oracle data.
Caveats:
  • Mirroring is subject to Fabric capacity and quotas; initial preview features may have limits and behaviour differences across database engines. Test for transactional consistency under your load patterns.

Oracle GoldenGate integration​

For customers who prefer Oracle’s mature CDC/replication stack, native Oracle GoldenGate integration is now available as a managed replication path into Fabric and Azure analytics surfaces. GoldenGate remains the enterprise option for high‑throughput, low‑latency, transactional replication and zero‑downtime migration scenarios, and Microsoft notes that GoldenGate purchases can be aligned to Azure billing constructs such as Microsoft Azure Consumption Commitment (MACC). GoldenGate gives a proven route for staged migrations and continuous operational synchronization between Oracle production systems and Azure analytics/AI workloads.

Services & versions: choice and compatibility​

Oracle Base Database Service (VM‑based) — what this gives you​

Oracle Base Database Service on Oracle Database@Azure is offered as a pay‑as‑you‑go, lifecycle‑managed VM‑based offering supporting Oracle Database 19c and 23ai. It targets customers who need a simpler entry point than Exadata while still retaining Oracle DB features and Oracle‑managed lifecycle operations (patching, lifecycle automation, APEX low‑code integration, separately scalable compute and storage). Oracle’s service documentation confirms Base Database Service support for 19c and 23ai and provides the expected enterprise editions and BYOL options.
Why it matters:
  • Lower operational cost for dev/test and smaller production workloads.
  • Familiar Oracle behavior (minimal refactor) for applications needing Standard or Enterprise features without immediate Exadata engineering.
  • Alternative to Exadata for organizations prioritizing price elasticity over absolute Exadata I/O performance.

Exadata Dedicated and Exascale support​

Exadata Database Service on both Dedicated Infrastructure and Exascale Infrastructure is supported, and newer Exadata generations (X11M) are part of the configuration options in many regions. Exascale provides hyper‑elastic Exadata capabilities for more flexible consumption and is positioned for larger analytics and AI workloads that benefit from Exadata offload and RDMA performance characteristics. Vendor performance claims around Exadata X11M acceleration for vector/AI workloads are compelling but should be validated with proof‑of‑value testing using representative workloads.

Regional coverage: the practical reality​

Region counts have been increasing rapidly as Oracle and Microsoft add joint footprints. Microsoft’s recent blog post highlights a 33‑region availability target for Oracle Database@Azure, reflecting rapid rollouts announced at events such as Oracle AI World; earlier Oracle press releases and Microsoft community updates show the program’s steady growth from an initial set of regions (14, then 28, then higher). Customers must verify the exact SKUs and Exadata/Autonomous/MAA tiers available in their specific Azure region because not all regions offer the same Exadata configurations or disaster‑recovery pairings.
Operational checklist for regions:
  • Confirm which Oracle Database service SKUs (Exadata Dedicated, Exascale, Autonomous, Base) are live in your target Azure region.
  • Verify whether multi‑zone (MAA Silver/Gold/Platinum) options are supported in that region.
  • Measure application‑to‑database latency in the paired Azure/OCI environment as part of your pilot.

Security, governance, and operations​

Microsoft Defender, Sentinel, and Entra ID​

Oracle Database@Azure integrates with Microsoft Defender for cloud workloads and with Microsoft Sentinel for SIEM/real‑time monitoring; it also supports Entra ID for unified identity controls. These integrations allow unified threat detection, automated compliance, and identity governance across both the Oracle‑operated database tier and Azure‑hosted application/AI surfaces. That said, integration requires careful design of Azure Arc connectors and identity mappings so that logging, auditing, and key custody meet your regulatory and forensic requirements.

Azure Arc for unified management​

Azure Arc is used to extend Azure management and governance to Oracle Database@Azure infrastructure; identity connectors register Exadata/VM clusters as Arc‑enabled machines so policy, monitoring, and Key Vault access can be centrally managed from Azure. Microsoft’s cloud adoption guidance includes runbooks for Arc connectivity and emphasizes that Autonomous Database services may behave differently (fully managed by Oracle) than Exadata VM clusters that can be Arc‑enabled. Design choices here affect operational responsibilities, runbook complexity, and incident escalations.

Commercial changes: Azure Accelerate & partner resale​

Azure Accelerate for Oracle​

Microsoft’s Azure Accelerate program provides funded migration assistance, Cloud Accelerate Factory engagement (zero‑cost deployment assistance), Azure credits, and partner funding to reduce migration risk and cost. Azure Accelerate is explicitly called out as a pathway to speed Oracle migrations, with assessment-to‑pilot support and investments available to qualified customers. If you’re planning an Oracle migration, Azure Accelerate is a practical way to get funded technical engagement and a sandboxed pilot environment.

Partner reseller model and marketplace procurement​

For the first time, Microsoft AI Cloud Partners and Oracle Partner Network members who meet joint eligibility can resell Oracle Database@Azure via the Microsoft Marketplace. This simplifies procurement for customers who prefer partner‑driven engagements and consolidates billing through Azure Marketplace private offers. It also creates a broader channel opportunity for MSPs and SIs to bundle migration + managed DBA services with marketplace billing.

Customer momentum and proof points​

High‑profile customers (for example, Activision Blizzard, Conduent, Vodafone, Craneware, and others) are public references for Oracle Database@Azure migrations; those case studies emphasize low‑latency access to Oracle data from Azure tools (Power BI, Microsoft Fabric, Copilot Studio) and the ability to preserve Exadata‑grade performance in cloud migrations. Such testimonials are useful validation points but should be viewed as reference cases; performance and TCO must be verified with your actual workload profile.

Risk and limitations — what to watch closely​

  • Licensing and commercial complexity. BYOL, MACC alignment, private offers, and Oracle support rewards can reduce cost, but negotiations are often non‑trivial. Model 1: BYOL + Azure billing may save money; Model 2: Marketplace pay‑as‑you‑go may be simpler but costlier. Build a 1–3 year TCO that includes interconnect, egress, licensing, and managed‑service premiums.
  • Feature parity and SKU variability by region. Not every region will have identical Exadata SKUs, Exascale capacity, or MAA tiers at launch. Validate the exact configuration you need (RAC nodes, Exadata X11M availability, DR zones) in your target region.
  • Vendor‑reported performance claims. Exadata X11M and database embedded vector search numbers are vendor‑reported and promising; treat them as directional until validated by representative POC tests that replicate your concurrency, query patterns, and vector workloads.
  • Operational responsibility split (Oracle vs. Microsoft). Joint support models reduce finger‑pointing risk if runbooks and SLAs are explicit. Negotiate both the technical runbook and contractual escalation/ownership to avoid operational confusion during incidents.
  • Security & audit trails. Centralized security benefits come with increased interdependence. Ensure your KMS, audit logging, and backup retention meet regulatory controls and that logs are available to support investigations across the Oracle and Azure planes. Validate where backups and forensic artifacts are stored and who has access.

Practical playbook: how to evaluate and migrate (step‑by‑step)​

  • Inventory and prioritize
  • Catalog databases (version, RAC, Data Guard, GoldenGate usage), schemas, and application dependencies.
  • Rank workloads: low‑risk pilots, migration candidates, and “do‑not‑touch” mission‑critical apps for later phases.
  • Validate region and SKU
  • Confirm the specific Oracle Database@Azure SKUs available in your target Azure region and whether multi‑zone MAA options exist.
  • Request private offers and confirm MACC/BYOL applicability.
  • Proof‑of‑Value (POV)
  • Run a representative workload pilot on Exadata or Base Database Service with mirrored analytics to Microsoft Fabric.
  • Test RAG and vector reproducibility end‑to‑end using Fabric + OneLake mirroring or GoldenGate pipelines.
  • Security & compliance verification
  • Set up Azure Arc identity connectors, configure Azure Key Vault integration, and validate Entra ID role mappings.
  • Run compliance checks (encryption at rest, key custody, audit log exportability).
  • Migration & cutover planning
  • Use GoldenGate or Oracle Zero‑Downtime Migration for phased cutovers to minimize downtime.
  • Prepare runbooks for failover, DR tests, and vendor escalation maps.
  • Optimize and modernize
  • After migration, evaluate modernization opportunities: push analytic queries into Fabric, instrument vector indexes, and measure cost/performance tradeoffs between Exadata and Base service for each workload.

Strategic takeaways for WindowsForum readers​

  • Oracle Database@Azure is now a mature, enterprise‑oriented multicloud option that preserves Oracle’s enterprise features while enabling Azure‑native AI and analytics adoption. The new OneLake mirroring + GoldenGate paths and the availability of Base Database Service widen choices for cost, scale, and migration patterns.
  • Organizations that value low‑latency AI near their Oracle data will find the OneLake + Fabric integration compelling — especially for RAG and vector‑driven applications where every millisecond of latency matters. Test inference and retrieval loops in a pilot before full rollout.
  • The commercial programs (Azure Accelerate, partner resale) materially reduce migration friction for organizations that lack internal cloud DBA or migration resources; factor these into your engagement model to lower upfront cost and risk.
  • Treat vendor performance claims as hypotheses to validate. Independent POCs, real workload testing, and careful TCO modeling (including hidden interconnect/egress costs and managed service premiums) remain essential.

Final assessment — strengths, risks, and who should consider it​

Strengths:
  • Enterprise fidelity: Preserves Exadata, RAC, Data Guard, GoldenGate and MAA, easing lift‑and‑shift migrations.
  • AI enablement: Direct, near‑real‑time paths into Microsoft Fabric, Copilot Studio, and Azure AI reduce friction for production AI.
  • Procurement & partner flexibility: Marketplace buying, MACC alignment, and reseller options simplify procurement and partner delivery models.
Risks:
  • Complex commercial and technical choices — licensing, region SKUs, and support models are nuanced.
  • Operational split — runbook clarity is required to avoid incident ownership gaps.
  • Performance validation required — vendor benchmarks are promising but not a substitute for your workload testing.
Who should evaluate Oracle Database@Azure now:
  • Enterprises with large, business‑critical Oracle estates seeking a path to cloud without application rewrites.
  • Organizations building production RAG/vector AI pipelines that need low latency between data and Azure AI services.
  • Customers seeking partner‑led migration engagements and funded assessments (Azure Accelerate) to reduce migration risk.
Oracle Database@Azure has moved from “interesting multicloud experiment” to a production‑grade option with concrete features to accelerate AI and analytics adoption. The blend of Oracle database fidelity and Azure AI/analytics integration presents a pragmatic route for organizations that must keep their Oracle investments but accelerate AI‑driven business outcomes — provided migrations are planned carefully, validated with realistic POCs, and governed with explicit runbooks and contractual SLAs.
Source: Microsoft Azure What’s new with Oracle Database@Azure
 

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