Revolutionizing AI Storage: Meet Amazon S3 Vectors — S3’s First Native Vector Support
Amazon just launched S3 Vectors (Preview) — a specialized S3 bucket type with built-in APIs for storing and querying massive vector embeddings. Designed to cut vector storage and query costs by up to 90%, it provides elastic, durable, and sub-second vector search performance — without requiring dedicated vector database infrastructure.
Vector search is an emerging technique used in generative AI applications to find similar data points to given data by comparing their vector representations using distance or similarity metrics. Vectors are numerical representation of unstructured data created from embedding models. You use embedding models to generate vector embeddings of your data and store them in S3 Vectors to perform semantic searches.
What’s the Problem?
- When AI systems — like chatbots or smart search tools — learn from documents, images, or videos, they convert that material into “vectors”: numerical summaries that capture meaning.
- Storing and running searches on these vectors used to require expensive, specialized databases.
This setup was often:
Costly — You have to keep servers running all the time and pay for storage and compute.
Complex — Requires managing infrastructure and scaling it as data grows.
Hard to scale — As you add more AI data, costs and management overhead can explode.
What Does S3 Vectors Do?
- Introduces a new type of S3 bucket designed specifically to hold vectors.
- Lets you create up to 10,000 “indexes” (organized collections) in each bucket.
- Stores billions of vectors with associated tags (like dates, categories, etc.) — so you can easily search within specific groups.
- Offers sub-second search responses, meaning most searches return results in under one second.
- Offers dedicated, infrastructure-free APIs (CLI, SDK, Rest API) for storing, accessing, and querying vectors.
Why Is This Better (and Cheaper)?
- Massive cost savings — It can cut the cost of vector storage and queries by up to 90% compared to traditional vector databases.
- No infrastructure hassle — No need to set up servers; AWS handles everything behind the scenes .
- Scales effortlessly — Can grow from a few vectors to billions with no manual work.
- Supports smart filtering — Each vector can have tags (metadata) for refined searches.
- Flexible performance tiers — Keep less-used vectors in S3 (cheap), and move frequently used vectors into OpenSearch for faster access.
How It Works with Other AWS Tools
- Amazon Bedrock & SageMaker Knowledge Bases: Automatically embed your unstructured data (like documents, images, and video) and store it in S3 Vectors — it becomes part of the RAG (Retrieval‑Augmented Generation) pipeline.
- Amazon OpenSearch Service: Ideal for “hot” data — frequently searched vectors get moved here for quicker responses; “cold” data stays in S3 Vectors.
What You Should Know
- Slightly slower: Searches are fast (~100 ms), but not as instantaneous as in-memory databases.
- Not fully automatic tiering: You need to decide and act on moving “hot” data vectors to OpenSearch i.e. some manual work is required.
- Use-case fit: Great for tasks like document search, RAG, video similarity, and AI agent memory, where ultra-low latency isn’t critical.
- Cost model depends on usage patterns: storage + query volume.
Amazon S3 Vectors solves key pain points in AI:
- Challenge: High cost & complexity of storing and searching vector data.
2. Solution: A simple, scalable, low‑cost vector store built into S3.
3. Benefits: Save up to 90% on costs
- No servers to manage
- Handle growth easily
- Integrates with Bedrock, SageMaker, and OpenSearch
- Mix hot & cold data for performance and savings
It’s a game-changer if you’re building AI apps that need to store, search, and manage large volumes of embedded data without breaking the bank.
