RAG on AWS Bedrock vs Oracle Cloud: Architecture, Trade-offs, and When to Choose Each

Series: Enterprise GenAI & RAG Architecture — Part 4 of 5 

Two Very Different Approaches to Enterprise RAG

In Part 3, we covered Azure — the natural choice for Microsoft-heavy enterprises. Today we explore two more platforms: AWS Bedrock and Oracle Cloud Infrastructure (OCI). Each has a distinct architectural philosophy and a clear ‘best fit’ use case.

AWS Bedrock: Maximum flexibility. The broadest model catalog in the industry. Built for teams that want to experiment with multiple LLMs and use managed services to minimize operational overhead.

OCI Vector Search: Maximum efficiency for Oracle customers. Native vector support directly in Oracle Database 23ai — no separate vector database needed. SQL-native vector queries from day one.

Choosing a cloud for RAG is not about which is ‘best’. It is about which fits your existing infrastructure, team skills, and cost model.

AWS Implementation — Amazon Bedrock + OpenSearch

AWS Services

AWS Architecture Flow

• Documents in S3 trigger a Lambda function via S3 Event Notification
• AWS Step Functions orchestrates the pipeline: Extract → Chunk → Embed → Store
• Chunking Lambda uses LangChain RecursiveCharacterTextSplitter (512 tokens, 50-token overlap)
• Amazon Bedrock Titan Embeddings V2 generates a 1024-dim vector per chunk
• OpenSearch k-NN index stores chunk text + vector + metadata using HNSW algorithm
• RAG app (Lambda + API Gateway): embed query → OpenSearch k-NN → build prompt → call Bedrock Claude → return answer
• CloudWatch captures all logs, latency metrics, and fires alerts on anomalies

AWS Bedrock — The Multi-Model Advantage

The biggest differentiator of AWS Bedrock is its model catalog. No other platform offers this breadth through a single unified API:

Bedrock lets you A/B test different LLMs with zero infrastructure changes. Switch Claude to Llama by changing one line — the API contract stays identical.

Bedrock Knowledge Bases — Managed RAG

For teams that want to move fast, AWS Bedrock Knowledge Bases is the fastest path to production RAG:

• Point it at an S3 bucket — AWS handles chunking, embedding, and OpenSearch automatically
• No infrastructure to manage — fully serverless, auto-scaling
• Integrated with all Bedrock LLMs — one API call returns a grounded answer with citations
• Trade-off: less control over chunking strategy, embedding model, and retrieval tuning

Use the managed option for prototypes and internal tools. Use the custom Lambda + OpenSearch path for production systems where retrieval quality tuning is critical.

AWS CodePipeline Testing

OCI Implementation — Oracle Database 23ai as Your Vector DB

Oracle Cloud Infrastructure takes a fundamentally different approach. Instead of adding a new vector database service alongside your existing infrastructure, OCI embeds vector capabilities directly into Oracle Database 23ai.

If your enterprise already runs Oracle ERP, Oracle databases, or Oracle Fusion middleware — you already have a production-grade vector database. You just need to enable it.

OCI Services

OCI Architecture Flow

• Documents uploaded to OCI Object Storage trigger an OCI Function via OCI Events Service
• Function extracts text and sends to chunking logic (Python-based splitter)
• OCI Generative AI Embed Models generate 1024-dim vector per chunk
• Chunk text + vector stored directly in Oracle DB 23ai using the native VECTOR column type
• RAG app: embed query → SQL VECTOR_DISTANCE() search → build prompt → call OCI GenAI → return answer

OCI’s Killer Feature — SQL-Native Vector Search

This is what makes OCI genuinely unique. Vector search in Oracle DB 23ai is just a SQL query:

SELECT chunk_text,

       VECTOR_DISTANCE(embedding, :query_vector, COSINE) AS similarity_score

FROM   documents_chunks

WHERE  category = 'HR'

ORDER  BY similarity_score

FETCH  FIRST 5 ROWS ONLY;

Notice the WHERE clause — you can combine traditional SQL filters with vector similarity in a single query. This enables hybrid filtering that other vector databases require complex workarounds to achieve.

AWS vs OCI — Side-by-Side Comparison

➡  In Part 5 — the final post — we cover the full testing strategy for RAG systems: the 4-layer testing pyramid, RAGAS metrics, hallucination detection, and the complete GenAI CI/CD pipeline.