Organisations today depend on fast, secure access to data to support mission-critical operations and evolving AI workloads. With Oracle AI Database services available on AWS, your team can streamline data integration and deliver high-impact solutions for semantic search, fraud detection, quality control, and much more.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Oracle AI Database services running on AWS. Vector + relational together. Use cases: semantic search, fraud detection, quality control.
Why it matters
Mature vector + RDBMS reduces stack complexity. One database instead of two reduces ops burden, sync issues, consistency problems.
The hard parts
Performance trade-offs of vector operations in row-oriented RDBMS. Generic benchmarks don't tell you what your specific workload will do.
Playbook moves
(1) Benchmark vector + relational hybrid queries against your real workload patterns. (2) Compare against the alternative (separate vector DB + RDBMS) honestly. (3) Factor in operational overhead, not just query performance.
The surprise
Putting vectors next to transactional data unlocks real-time RAG over fresh data. Your warehouse can't do that — there's always replication lag. If your AI use case requires acting on fresh transactional data (fraud detection, real-time personalisation), the consolidated DB option becomes more compelling than its raw benchmarks suggest.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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