S3 Vectors: How to Build a RAG Without a Vector Database

You don't need a vector database anymore. S3 Vectors gives you native vector storage + similarity search, serverless, at up to 90% less cost

Jubin Abhishek Soni

CORE ·

May. 19, 26 · Analysis

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Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructure, and pray the costs don't spiral. For most internal AI apps, this is overkill.

Amazon S3 Vectors changes the equation. It's native vector storage built into S3 — no clusters, no provisioning, no idle compute. You store vectors like you store objects, query them with sub-100ms latency, and pay per use. It went GA in December 2025 and now supports 2 billion vectors per index across 31+ AWS regions.

This post walks through building a complete RAG pipeline using only S3 Vectors and Amazon Bedrock. No external vector database ~50 lines of Python.

Architecture

Three phases, two AWS services, zero infrastructure.

S3 Vectors vs. Traditional Vector Databases

	S3 Vectors	Managed Vector DB (e.g. OpenSearch, Pinecone)
Infrastructure	None — fully serverless	Clusters, shards, replicas
Scale	2B vectors/index, 10K indexes/bucket	Varies, often requires re-sharding
Query latency	~100ms (frequent), <1s (infrequent)	~10-50ms
Cost model	Pay per PUT + storage + query	Hourly/monthly compute + storage
Cost at scale	Up to 90% cheaper	Idle compute adds up fast
Metadata filtering	Up to 50 keys, filterable by default	Full query language
Best for	RAG, agent memory, semantic search	High-QPS production search, hybrid search

The tradeoff is clear: S3 Vectors trades single-digit-ms latency for zero ops and dramatically lower cost. For internal RAG apps, agent memory, and moderate-QPS workloads, it's the better choice.

Step 1: Set Up S3 Vectors

Create a vector bucket and index. You can do this in the console or via CLI:

    Shell
   
 

   # Create a vector bucket
aws s3vectors create-vector-bucket \
  --vector-bucket-name my-rag-bucket

# Create a vector index (1024 dims for Titan Embeddings V2)
aws s3vectors create-vector-index \
  --vector-bucket-name my-rag-bucket \
  --index-name my-rag-index \
  --dimension 1024 \
  --distance-metric cosine
  

That's your "database" — done in two commands.

Step 2: Ingest Documents

Here's the ingestion pipeline. We chunk text, embed each chunk with Titan Embeddings V2, and store vectors with metadata:

    Python
   
 

   import boto3
import json
import uuid

bedrock = boto3.client("bedrock-runtime", region_name="us-west-2")
s3vectors = boto3.client("s3vectors", region_name="us-west-2")

BUCKET = "my-rag-bucket"
INDEX = "my-rag-index"


def embed(text: str) -> list[float]:
    """Generate embeddings using Titan Text Embeddings V2."""
    response = bedrock.invoke_model(
        modelId="amazon.titan-embed-text-v2:0",
        body=json.dumps({"inputText": text}),
    )
    return json.loads(response["body"].read())["embedding"]


def chunk_text(text: str, chunk_size: int = 500) -> list[str]:
    """Split text into overlapping chunks."""
    words = text.split()
    chunks = []
    for i in range(0, len(words), chunk_size - 50):
        chunk = " ".join(words[i : i + chunk_size])
        if chunk:
            chunks.append(chunk)
    return chunks


def ingest(doc_text: str, source: str):
    """Chunk, embed, and store a document."""
    chunks = chunk_text(doc_text)
    vectors = []

    for i, chunk in enumerate(chunks):
        vectors.append({
            "key": f"{source}::chunk-{i}",
            "data": {"float32": embed(chunk)},
            "metadata": {
                "source": source,
                "chunk_index": i,
                "text": chunk,  # store original text for retrieval
            },
        })

    # PutVectors supports batches
    s3vectors.put_vectors(
        vectorBucketName=BUCKET,
        indexName=INDEX,
        vectors=vectors,
    )
    print(f"Ingested {len(vectors)} chunks from {source}")
  

Usage:

    Python
   
   with open("internal-docs.txt") as f:
    ingest(f.read(), source="internal-docs.txt")

Step 3: Query + Generate

Now the RAG loop — embed the question, find similar chunks, and feed them to Claude:

    Python
   
 

   def rag_query(question: str, top_k: int = 5) -> str:
    """Full RAG pipeline: retrieve + generate."""

    # 1. Embed the question
    query_vector = embed(question)

    # 2. Find similar chunks
    results = s3vectors.query_vectors(
        vectorBucketName=BUCKET,
        indexName=INDEX,
        topK=top_k,
        queryVector={"float32": query_vector},
        returnMetadata=True,
        returnDistance=True,
    )

    # 3. Build context from retrieved chunks
    context_parts = []
    for v in results["vectors"]:
        text = v["metadata"]["text"]
        source = v["metadata"]["source"]
        dist = round(v["distance"], 4)
        context_parts.append(
            f"[Source: {source}, Distance: {dist}]\n{text}"
        )
    context = "\n\n---\n\n".join(context_parts)

    # 4. Generate answer with Claude
    prompt = f"""Answer the question based on the provided context. 
If the context doesn't contain enough information, say so.

## Context
{context}

## Question
{question}

## Answer"""

    response = bedrock.invoke_model(
        modelId="us.anthropic.claude-sonnet-4-20250514",
        body=json.dumps({
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 1024,
            "messages": [{"role": "user", "content": prompt}],
        }),
    )

    body = json.loads(response["body"].read())
    return body["content"][0]["text"]
  

Usage:

    Python
   
   answer = rag_query("What is our refund policy for enterprise customers?")
print(answer)

That's the entire RAG pipeline — ~50 lines of actual logic, no infrastructure.

Step 4: Metadata Filtering

S3 Vectors supports filtering by metadata during queries. This is powerful for multi-tenant or multi-source RAG:

    Python
   
 

   # Only search chunks from a specific document
results = s3vectors.query_vectors(
    vectorBucketName=BUCKET,
    indexName=INDEX,
    topK=5,
    queryVector={"float32": query_vector},
    returnMetadata=True,
    filter={"source": {"eq": "refund-policy.pdf"}},
)
  

Filter operators include eq, ne, gt, gte, lt, lte, in, beginsWith, and logical and/or combinators.

Data Flow

Here's how a query flows through the system end-to-end:

When to Use S3 Vectors (and When Not To)

Use S3 Vectors when:

You're building internal RAG apps, agent memory, or semantic search
Query volume is moderate (not thousands of QPS)
You want zero infrastructure management
Cost matters more than single-digit-ms latency

Use a dedicated vector DB when:

You need <10ms query latency consistently
You need hybrid search (keyword + semantic)
Your QPS is in the hundreds or thousands
You need advanced features like aggregations or faceted search

Use both (tiered): S3 Vectors as cheap, durable storage + OpenSearch for hot queries. AWS supports this integration natively.

Integrating With Bedrock Knowledge Bases

If you don't want to write the chunking and embedding code yourself, Bedrock Knowledge Bases can use S3 Vectors as its vector store directly:

Just select "S3 Vectors" as the vector store when creating your knowledge base. Bedrock handles chunking, embedding, and storage automatically.

Cleanup

     Shell
    
 

    # Delete the vector index
aws s3vectors delete-vector-index \
  --vector-bucket-name my-rag-bucket \
  --index-name my-rag-index

# Delete the vector bucket
aws s3vectors delete-vector-bucket \
  --vector-bucket-name my-rag-bucket
   

Resources

Database vector database RAG

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