What are Amazon S3 Vectors and How To use it?

Imagine you're running a company with millions of documents, images, or videos. Your customers want to find similar content instantly,  like Netflix recommending movies you'll love, or a medical team searching through thousands of X-rays to find similar cases. Traditional search only works with exact matches, but what if you could search by meaning instead?

This is where vector search comes in, but there's a catch: storing and searching through millions of vectors has been incredibly expensive. Many companies either limit their AI capabilities or face massive bills that make advanced search features financially unfeasible.

What are Amazon S3 Vectors?

Amazon S3 Vectors is a new type of cloud storage that goes beyond simply storing files, it understands them. Built by AWS, it’s the first storage solution with native support for vectors, meaning you can search for content based on meaning, not just keywords.

Think of it as giving your storage superpowers, so instead of exact matches, you can find similar documents, images, or videos based on what they actually contain. Whether you're powering recommendations, semantic search, or intelligent media retrieval, S3 Vectors makes it possible.

What Makes Amazon S3 Vectors Special?

Amazon S3 Vectors brings vector search capabilities to the storage layer, eliminating the need for separate vector databases or complex infrastructure. That alone is a game-changer.

• Amazon S3 Vectors reduces the cost of storing and searching vectors by up to 90%. For businesses, this means you can finally afford to implement AI-powered search across your entire data collection.
• You can store billions of vectors and get search results in sub-second performance. Whether you're dealing with a startup's growing dataset or an enterprise's petabyte-scale archives, it scales effortlessly.
• Unlike traditional vector databases that require you to manage complex infrastructure, S3 Vectors provides dedicated APIs without any provisioning. It's as simple as using regular S3 storage.

In short, Amazon S3 Vectors makes vector search cheaper, faster, and radically simpler, removing the infrastructure and financial hurdles that have limited AI adoption until now.

AI Applications You Can Build with Amazon S3 Vectors

1. Smart Document Search

Traditional keyword-based search often misses the mark, especially when users don’t know the exact terms to use. With S3 Vectors, you can implement semantic document search across contracts, support tickets, research papers, or legal documents.

For example, an employee could type: “Show me contracts similar to the Microsoft deal”, and instantly receive documents with similar structure, intent, or terminology, even if the keyword “Microsoft” isn’t mentioned. This saves hours of manual digging and makes enterprise knowledge more accessible.

2. Medical Breakthroughs

In healthcare, time and accuracy are everything. By embedding medical images (like X-rays, MRIs, or pathology slides) as vectors, doctors and researchers can quickly retrieve visually or structurally similar cases from massive datasets.

For instance, a radiologist could upload a new chest X-ray and immediately surface similar past cases, complete with diagnoses and treatment notes, enabling faster, AI-assisted decision-making and better patient outcomes.

3. Video Content Discovery

Media companies often deal with petabytes of unstructured video. With S3 Vectors, they can tag scenes using embeddings and index them for similarity search.

Want to find all sunset beach scenes across years of archived footage? With vector search, it’s as easy as querying by an example frame or description. This opens up smarter editing workflows, scene-based content tagging, and recommendation engines for viewers.

4. Personalized Recommendations

E-commerce and retail businesses can go beyond “people also bought” logic. With vector search, you can recommend items based on visual similarity, behavioral embeddings, or text descriptions.

Imagine a shopper uploads a picture of a handbag, and the system instantly suggests visually similar products, or matches items based on how others with similar preferences behaved, leading to more relevant and personalized shopping experiences.

5. Multilingual or Context-Aware Chatbots (Bonus Use Case)

Pairing S3 Vectors with Amazon Bedrock or other LLMs lets you build intelligent, memory-aware chatbots that retrieve vector-matched documents as context. This enables bots to answer nuanced customer questions with grounded, semantically relevant data, across multiple languages and domains.

Suggested Reads-  How to Analyse Documents Using AWS Services

How Amazon S3 Vectors Work? 

Amazon S3 Vectors combines familiar S3 storage with built-in vector indexing to power semantic search at scale. Here’s a quick breakdown of how it works and why it matters:

The Three Core Components

1. Vector Buckets

Think of these as specialized storage containers designed specifically for AI data. Unlike regular S3 buckets, these understand the mathematical relationships between your data.

2. Vector Indexes

Inside each vector bucket, you can create up to 10,000 searchable indexes. Each index can hold tens of millions of vectors, enabling fast and scalable retrieval based on similarity.

3. Smart Metadata

Every vector can carry custom metadata like timestamps, categories, or user IDs. This lets you apply fine-grained filters to your similarity searches, for example, limiting results to a specific date range or user group.

Why Amazon S3 Vectors Matter for Your Business?

1. From Expensive to Affordable

Traditional vector databases often cost thousands per month for large datasets. With S3 Vectors' pay-as-you-go pricing, you only pay for what you use, making advanced AI accessible to businesses of all sizes.

2. Integration That Just Works

S3 Vectors integrates seamlessly with Amazon Bedrock Knowledge Bases for building intelligent chatbots and Amazon OpenSearch for hybrid search strategies. You can build sophisticated AI applications without becoming a machine learning expert.

3. Enterprise-Ready Security

You get the same trusted security as the rest of AWS: encryption at rest and in transit, fine-grained IAM access controls, and compliance with regulations like GDPR and HIPAA. It’s AI infrastructure you can trust for even the most sensitive data.

How to Build Your First Vector Application with Amazon S3 Vectors?

1. Set up billing so you don’t get an exorbitant amount out of the blue.

2. Search for S3 in console:

3. Select Vector buckets (not available in all the regions, eg India, so use us-east-1)

4. Click on create vector bucket

5. Give a name to your bucket and tada, bucket is ready:

6. After creating the bucket, create a vector index for it.

While creating vector index, keep the dimensionality in mind, you can find it from your embedding model:

And our vector index is ready for vectors, semantic and similarity.

7. Go to IAM and get your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. 

8. Use this basic code to create embedding vectors via openai and then store and query them in AWS S3:

import os, uuid, time, boto3, openai
from dotenv import load_dotenv

# Load config
load_dotenv(override=True)
openai.api_key = os.getenv("OPENAI_API_KEY")
VECTOR_DIM = 3072
EMBED_MODEL = "text-embedding-3-large"

# AWS clients
s3v = boto3.client("s3vectors",
    region_name=os.getenv("AWS_REGION", "us-east-1"),
    aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
    aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"))

def embed(texts):  # Generate OpenAI embeddings
    res = openai.embeddings.create(input=texts, model=EMBED_MODEL)
    return [e.embedding for e in res.data]

def insert(bucket, index, vectors, metadatas):
    vecs = [{
        "key": str(uuid.uuid4()),
        "data": {"float32": vec},
        "metadata": meta
    } for vec, meta in zip(vectors, metadatas)]
    return s3v.put_vectors(vectorBucketName=bucket, indexName=index, vectors=vecs)

def query(bucket, index, vector, top_k=3):
    res = s3v.query_vectors(
        vectorBucketName=bucket, indexName=index,
        queryVector={"float32": vector},
        topK=top_k, returnDistance=True, returnMetadata=True)
    for r in res.get("vectors", []):
        print(f"→ {r['metadata'].get('original_text')} (dist: {r['distance']:.4f})")

# --- Demo ---
bucket = os.getenv("S3_VECTOR_BUCKET_NAME")
index = os.getenv("S3_VECTOR_INDEX_NAME")

texts = ["The quick brown fox...", "Early bird catches the worm"]
vecs = embed(texts)
insert(bucket, index, vecs, [{"original_text": t} for t in texts])
time.sleep(10)  # wait for indexing
query_vec = embed(["Who wakes up early?"])[0]
query(bucket, index, query_vec)

Output:

The complete implementation is available in the AWS S3 Vectors POC repository, which includes:

• Infrastructure setup with error handling
• OpenAI integration for generating embeddings
• Robust querying with metadata filtering
• Production-ready examples for real applications

Why Now Is the Right Time to Start Using Amazon S3 Vectors

Amazon S3 Vectors is currently in preview, giving early adopters a chance to build competitive advantages. The service delivers:

• S3-level durability and scale you already trust
• Sub-second query performance for real-time applications
• 90% cost reduction compared to traditional solutions
• Native integration with AWS's AI ecosystem

Conclusion

We're at the beginning of a transformation where every application can understand context and meaning. Amazon S3 Vectors removes the cost and complexity barriers that have kept advanced AI features limited to tech giants.

Whether you're building the next generation of customer support, creating smarter content discovery, or developing breakthrough medical applications, S3 Vectors provides the foundation to turn ambitious AI ideas into an affordable reality.

The question isn't whether your business needs intelligent search - it's whether you'll be among the first to implement it cost-effectively. With Amazon S3 Vectors, that opportunity is here today.

Ready to transform your applications with intelligent search? Check out the complete implementation guide and start building your first vector-powered application with Amazon S3 Vectors.