NEW YORK - Aug. 10, 2025 - PRLog -- Why has AI become a central topic inside the Oracle Database?
A: Oracle Database 23ai brought AI into the database itself. The new AI Vector Search feature enables similarity search on text, images, and other unstructured data alongside traditional corporate data — without ETL to a separate vector DB. The result is lower latency, reduced risk, and native governance.
Does this change the architecture for RAG applications?
A: With 23ai you can index embeddings directly in Oracle and run RAG without moving sensitive data. On OCI, GenAI Agents can orchestrate RAG calls and similarity searches directly on 23ai — adding automation that accelerates time-to-value.
Where does the Autonomous Database fit in?
A: The Autonomous Database already runs 23ai on OCI and in multicloud environments like Database@Azure, simplifying operations and generative development (GenDev). For smaller teams, Oracle Database 23ai Free has become a great sandbox for prototyping Vector Search and JSON Relational Duality without license cost.
What recent multicloud moves matter for enterprise AI?
A: The big highlight is Oracle Database@Google Cloud. The partnership evolved from GA in 2024 to a joint program in 2025, now with Exadata X11M support and regional expansion. This enables scenarios where your transactional data stays in Oracle, and you integrate natively with Google's AI tools (Vertex AI, Gemini), while maintaining corporate-grade support and governance.
What about foundation models on OCI?
A: OCI Generative AI is expanding quickly (new regions like Phoenix launched in July 2025). It's gained security guardrails, tool-use integration for external services, and support for model families like Llama 3.1 (including quantized versions), plus playgrounds and samples for LangChain/LlamaIndex. This shortens the path from POC to production in regulated environments.
Are companies already seeing results?
A: Yes. One public example is Sphere, which built and launched AI apps on OCI in 45 days using Autonomous Database on 23ai. It's not an isolated case; we're seeing much shorter delivery cycles when the data never leaves the database perimeter.
What is the current market context?
A: The appetite for AI capability continues to drive cloud growth and partnerships. Oracle has been in announcements related to model training with NVIDIA and cross-cloud integrations (Azure/Google). This explains optimistic guidance and investor interest — and reinforces that enterprise AI is a game of data + compute + governance.
In one sentence, what's the thesis?
A: Bring AI to where the data lives. When vectors, queries, and policies live in the same place, you move faster, reduce risk, and lower TCO — without sacrificing RAG/GenAI performance.
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Source: Google
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