If you've been working with AI or machine learning recently, you've probably heard about vector databases like Pinecone, Qdrant, Milvus, and pgvector. Now, AWS has added its own option: Amazon S3 Vectors.
So, is it just another AWS service, or something different? The honest answer: it's different. Neither strictly better nor worse, but unique in its own way. Let's explore what it is, when to use it, and when other solutions might be better.
What Exactly are Amazon S3 Vectors?
Traditional Amazon S3 is like a giant digital filing cabinet - it's cheap, reliable, and able to hold a vast amount of data. But it's built for storage, not for fast searching.
Amazon S3 Vectors adds vector search to this storage. Instead of just storing files, you can store AI embeddings (numerical representations of text, images, etc.) and search them directly.
The key here is that S3 Vectors brings S3's low storage cost together with built-in search capability. No separate database needed.
Why Should You Care?
Imagine you're building an AI-powered customer support system. You have millions of old tickets and want to find similar ones fast.
Using traditional vector databases like Pinecone means paying …
- Storage fees for embeddings,
- Query costs,
- Infrastructure upkeep.
With S3 Vectors, you pay mostly for S3 storage (which is very cheap) and only pay query costs when you search.
For many teams, this means saving 60–90% over a year.
Benefits of Using S3 Vectors
- Dramatic Cost Savings For example, storing 10 million vectors and running 500,000 queries a month costs about:
$159/month with S3 Vectors,
450/month with Pinecone,
Over $2,000/month if self-hosted.
- Zero Infrastructure to Manage No servers, no scaling headaches. AWS handles all that for you.
- Seamless AWS Integration Works smoothly with other AWS tools like: Amazon Bedrock for AI models, Lambda for serverless processing, OpenSearch for keyword search.
- Easy to Start Create an index, upload vectors, and search - no complex tuning or setups.
Trade-Offs to Consider
Speed: S3 Vectors query latency is about 100–800 ms, compared to Pinecone's 5–80 ms. For real-time user-facing applications, dedicated databases are better.
Throughput: It's made for hundreds of queries per second, not thousands.
Advanced Features: It doesn't natively support hybrid search (vector + keyword) or complex pre-filtering.
Metadata Filtering: Filtering occurs after search results are returned, which can be slower and less precise.
When to Use S3 Vectors: Examples
Internal Document Search
Low to moderate queries per month,
Search latency of 200–300 ms is fine,
Cost-effective storage for hundreds of thousands of documents.Batch Product Categorization
Run heavy searches overnight,
Speed isn't critical,
Huge cost savings.Retrieval-Augmented Generation (RAG) Systems
Low query volumes,
Integrates well with Amazon Bedrock,
Good for chatbots with sub-second response needs.Data Archival Search
Occasional searches over cold or warm data,
Low cost, no need for instant results.
When Not to Use S3 Vectors
Need millisecond latency for real-time apps,
Massive query volumes,
Native hybrid search required.
Fast real-time data updates,
Complex metadata filtering,
Running outside the AWS ecosystem.
Real Performance Numbers at a Glance
How to Get Started
Create an S3 Vector index with your dimensions.
Upload your embeddings and metadata.
Query your vectors and process results.
It’s that simple.
Final Thoughts
Amazon S3 Vectors is not a vector database killer. It’s a cost-effective, easy-to-use solution for low-to-medium query volumes where sub-second latency is acceptable.
For real-time, high-performance apps, dedicated databases shine. But for many others, this practical service is exactly what the market needed.
Many teams actually combine S3 Vectors with other vector databases, using it for bulk or archival data and faster services for hot data.
If you want to save costs and you’re already on AWS, definitely give S3 Vectors a try — you might find it fits your needs perfectly.
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