Bedrock Knowledge Bases vs. Custom SageMaker RAG: Which Architecture Actually Scales in 2026?
Building production-grade RAG on AWS? We pit managed Amazon Bedrock Knowledge Bases against a fully custom SageMaker + OpenSearch Serverless pipeline. Includes dynamic reranking code, an architectural flow diagram, a latency/cost comparison table, and key production insights.
Architecting Retrieval-Augmented Generation (RAG) on AWS usually leads to a critical decision point: Do you go 100% managed with Amazon Bedrock Knowledge Bases, or build a fully tailored custom pipeline using Amazon SageMaker?
Many developers start with Bedrock for its fast setup, only to hit limits on chunking heuristics or domain-specific vector reranking. Conversely, building a fully custom pipeline on SageMaker provides precision control but introduces infrastructure overhead, container management, and autoscaling complexity.
This post evaluates both approaches across latency, cost, retrieval quality, and maintenance, featuring an architecture for high-accuracy Hybrid RAG that combines both services.
Code Snippet: Calling SageMaker Reranker to Elevate Bedrock Context
Here is how to implement a custom reranking step between OpenSearch vector retrieval and Amazon Bedrock model inference:
Code ->
import boto3
import json
import json
bedrock_runtime = boto3.client('bedrock-runtime', region_name='us-east-1')
sagemaker_runtime = boto3.client('sagemaker-runtime', region_name='us-east-1')
sagemaker_runtime = boto3.client('sagemaker-runtime', region_name='us-east-1')
def rerank_documents_with_sagemaker(query: str, retrieved_docs: list) -> list:
"""
Passes query and retrieved OpenSearch chunks to a SageMaker Cross-Encoder Endpoint
to calculate semantic relevance scores.
"""
payload = {
"inputs": [[query, doc["text"]] for doc in retrieved_docs]
}
"""
Passes query and retrieved OpenSearch chunks to a SageMaker Cross-Encoder Endpoint
to calculate semantic relevance scores.
"""
payload = {
"inputs": [[query, doc["text"]] for doc in retrieved_docs]
}
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response = sagemaker_runtime.invoke_endpoint(
EndpointName='cross-encoder-bge-reranker-v2',
ContentType='application/json',
Body=json.dumps(payload)
)
scores = json.loads(response['Body'].read().decode())
# Attach scores and sort docs by highest relevance score
for i, score in enumerate(scores):
retrieved_docs[i]['score'] = score
reranked_docs = sorted(retrieved_docs, key=lambda x: x['score'], reverse=True)
return reranked_docs[:3] # Return top 3 highly relevant contextsdef generate_rag_response(prompt: str, context_chunks: list):
"""
Invokes Amazon Bedrock using the Bedrock Converse API with refined context.
"""
context_str = "\n\n".join([doc['text'] for doc in context_chunks])
"""
Invokes Amazon Bedrock using the Bedrock Converse API with refined context.
"""
context_str = "\n\n".join([doc['text'] for doc in context_chunks])
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messages = [{
"role": "user",
"content": [{"text": f"Context:\n{context_str}\n\nQuestion: {prompt}"}]
}]
response = bedrock_runtime.converse(
modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
messages=messages,
inferenceConfig={"temperature": 0.2, "maxTokens": 1000}
)
return response['output']['message']['content'][0]['text']Enjoyed reading this content? Let the author know!
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