For years, Amazon S3 has been depicted as "just storage" in most architectural diagrams.

We put everything there:

• Raw events
• PDFs and contracts
• Images and videos
• Data lake zones

Then we built more systems around it:

• Vector databases for semantic search
• Indexing services
• ETL pipelines to sync embeddings

Every new AI workload meant one more moving piece.

With Amazon S3 Vectors, AWS is quietly asking us:

💡 What if your object store could also be your vector store?

And that’s a big shift for anyone building AI, RAG, agents, or semantic search on AWS.

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Why S3 Vectors Matter (In One Sentence)

Amazon S3 Vectors lets you store and query vector embeddings directly in S3, with native similarity search no separate vector database needed.

If you’re an AWS builder, architect, or data practitioner, this changes how you think about:

• Where do you keep embeddings?
• How many systems do you operate?
• How do you design RAG and AI search workloads?

This is not "just another feature".

This is S3 stepping into the AI runtime 🚀.

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First, What Are "Vectors" in This Story?

Short version:

• Take text, image, audio, or a document
• Run it through an embedding model (Bedrock, SageMaker, open-source, etc.)
• You get a list of numbers like: [0.12, -0.83, 0.07, …]

That list of numbers is a vector embedding, a mathematical representation of meaning.

Two items with similar meaning → similar vectors.

This unlocks:

• Semantic search ("find things like this")
• Recommendations ("suggest similar items")
• RAG ("retrieve the right context for my LLM query")

And now, S3 understands this type of data natively ✨

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What Did AWS Actually Add?

Amazon S3 Vectors introduces three core building blocks:

1. Vector bucket

• A special type of S3 bucket designed for storing and querying vectors
• Same durability and elasticity guarantees as S3

2. Vector index

• Lives inside a vector bucket
• Groups vectors logically (for example: docs, products, support tickets)
• This is where similarity search happens

3. Vectors

• The embeddings you write into a vector index
• Can include metadata (doc_id, type, tenant, created_at, etc.)

You interact with this using dedicated APIs and console support, not just PutObject / GetObject.

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Why This Is a Big Deal for AI Builders

Let’s be honest: vector databases solved real problems.

But at scale, many teams ended up with:

• S3 for raw objects
• A vector DB for embeddings
• Glue/Spark/ETL to sync between them
• Extra monitoring, security, and cost overhead

With S3 Vectors:

• Storage and vectors live together
• No extra vector infrastructure to deploy and babysit
• You pay based on S3 style storage + query usage, not "always-on" clusters

This is especially attractive if you:

• Already treat S3 as your "source of truth."
• Are cost-sensitive with AI workloads
• Want fewer systems in your architecture diagram

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How S3 Vectors Fit Into a RAG / AI Architecture

Here’s a simple mental flow for a RAG-style app on AWS:

Content lands in S3

• PDFs, docs, wiki exports, tickets, transcripts, etc.

Embedding generation

• Use Amazon Bedrock (for example, a Titan embedding model) or another model to generate embeddings for these items.

Store embeddings in S3 Vectors

• One vector bucket, one or more vector indexes (for example: kb-docs, faqs, tickets)

Query at runtime

• Given a user query → generate a query embedding → run similarity search on S3 Vectors → get the top-K closest vectors

Feed top-K results into your LLM

• Use Bedrock, SageMaker, or any LLM endpoint

Return final answer to user

• Combine retrieved context + LLM response

No external vector DB.

No "sync job" to keep storage and vectors aligned.

If your data lake is in S3, your AI retrieval layer can now live there too.

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Where S3 Vectors Shine (Concrete Use Cases)

1️⃣ Semantic Search Over Documents

Perfect for:

• Internal knowledge bases
• Policy/compliance documents
• Customer contracts
• Product manuals

Store both the original files in S3 and the embeddings in S3 Vectors.

Search by meaning, not just exact keyword match.

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2️⃣ RAG for Enterprise Assistants

LLM-powered assistants need:

• Relevant context
• Low latency retrieval
• Cost-effective storage

S3 Vectors can power the retrieval layer for:

• Support chatbots 💬
• Internal Q&A over Confluence / SharePoint exports
• Developer assistants over code snippets & docs

With Bedrock Knowledge Bases and other integrations, you can plug S3 Vectors into a managed RAG pipeline.

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3️⃣ Recommendations & Similarity

Examples:

• "Show me products similar to this one."
• "Find images visually similar to this photo."
• "Recommend articles similar to what I just read."

Store behavior or content embeddings in S3 Vectors and query for nearest neighbors based on vector distance.

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4️⃣ Multi-Tenant AI Platforms

If you’re building:

• A SaaS AI product on AWS
• A multi-tenant knowledge platform

You can:

• Use metadata like tenant_id, project, visibility.
• Filter queries per customer/user
• Keep it all in one storage + vector layer

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S3 Vectors vs Dedicated Vector Databases

So does this kill vector databases?

Not necessarily.

It changes the question from:

"Which vector DB should I use?"

to:

"Do I actually need a separate vector DB for this workload?"

✅ When S3 Vectors Are a Great Fit

• Your primary data is already in S3
• You need durable, cost-optimized, large-scale vector storage
• You don’t want to operate another distributed system
• You’re building RAG, semantic search, and recommendations on top of existing S3 data

When a Separate Vector DB Might Still Make Sense

• Ultra-low-latency, very high-QPS workloads with complex query patterns
• Tight coupling with existing non-AWS ecosystems
• Highly customized index tuning or retrieval logic

For a huge percentage of GenAI and AI-search workloads on AWS, S3 Vectors will be the default starting point.

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🛠️ Getting Started (Builder Mindset)

If I were starting a PoC this week, here’s how I’d approach it:

1. Pick one narrow use case Example:

"Semantic search over our User Group session notes and slide decks."

2. Create a vector bucket + index

Follow the "Getting started with S3 Vectors" section in the AWS docs.

3. Generate embeddings with Bedrock or another model

• Start with a Bedrock embedding model (for example, Amazon Titan)
• One embedding per document, page, or chunk

4. Write embeddings to S3 Vectors

Include metadata like title, speaker, date, tags, url.

5. Build a tiny API or CLI

• Input: natural language query
• Output: top-K sessions/docs that match
• Return relevant links/summaries

📌 In my next post, I’ll walk you through a hands-on PoC using Amazon S3 Vector Engine — so you can see this in action.

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🧭 Architect’s Lens: Designing for the Next 12–18 Months

As AI workloads mature, we’ll care more about:

• Unified data governance (one place to manage access)
• Cost curves, not just PoCs
• Operational simplicity – fewer systems, fewer outages

S3 Vectors aligns nicely with all three:

• Uses IAM + S3 policies for control 🔐
• Priced like storage + queries, not another cluster
• Integrates with Bedrock, OpenSearch, analytics tools, and more

As builders, our job is not to collect more tools.

It’s to design systems that are boring to operate and exciting to use.

S3 Vectors is one of those quiet features that moves us in that direction.

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🧠 TL;DR – In Simple Words

Amazon S3 Vectors lets you store AI embeddings in S3 and search them by meaning, without needing a separate vector database. It makes semantic search easier, scalable, and integrated with the AWS ecosystem you already use.

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🔗 References

• Amazon S3 Vectors – Feature overview
• Working with S3 Vectors and vector buckets – User guide
• Getting started with S3 Vectors

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What are you planning to build with S3 Vectors? Drop your thoughts in the comments!

Happy Building! 🚀

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About the Author

Sujitha Rasamsetty is an AWS Community Builder in AI Engineering and a Data Scientist at Relanto, with a strong passion for emerging technologies in cloud computing and artificial intelligence.

In her role, she works hands-on with data, cloud architectures, and AI-driven solutions, focusing on building scalable, secure, and production-ready systems. Her interests span across machine learning, generative AI, cloud-native architectures, and data platforms, where she enjoys bridging the gap between advanced analytics and real-world cloud implementations.

Sujitha actively shares her learning and experiences with the community through blogs, discussions, and technical knowledge sharing, with a strong belief in learning in public and growing together as a community.