Amazon S3 Vectors: The End of Expensive Vector Databases for RAG?

Unlocking Scalable, Cost-Effective RAG with Amazon S3 Vectors

At AWS Summit New York in July 2025, Amazon Web Services (AWS) introduced a game-changing new capability: Amazon S3 Vectors. Currently in preview, this service brings native vector storage and search capabilities directly into Amazon Simple Storage Service (Amazon S3) - one of the world’s most durable, scalable, and cost-efficient storage solutions. AWS has designed Amazon S3 Vectors to support use cases that involve retrieval-augmented generation (RAG), AI agent memory, and long-term document retrieval by enabling semantic search over vast volumes of vector embeddings stored in S3 buckets.

What makes this especially significant is AWS’s claim of up to 90% cost savings on vector ingestion, storage, and querying compared to traditional vector databases. For organisations experimenting with or scaling out RAG architectures using Amazon Bedrock, this could be a turning point in how they manage and query unstructured knowledge at scale.

Let’s explore what Amazon S3 Vectors brings to the table, how it compares with incumbent solutions like Pinecone and Amazon OpenSearch Service, and what its arrival means for architects and developers building next-generation generative AI applications.

Amazon S3 Vectors: What’s New?

Amazon S3 Vectors enables developers to create vector indexes directly in Amazon S3 and populate them with high-dimensional embeddings—commonly generated from text, images, or structured data using foundation models such as Amazon Titan, Cohere, or open-source alternatives. Each vector can be enriched with metadata, which can then be used for filtering during retrieval.

These vector indexes can be queried via a dedicated semantic search API that returns the most similar items based on vector similarity, typically using approximate nearest neighbour (ANN) search algorithms. This is tightly integrated with S3’s serverless design - offering durability, elasticity, and simple API-based access - while maintaining sub-second latency for retrieval operations. Importantly, S3 Vectors is also designed to work seamlessly with Amazon Bedrock Knowledge Bases, allowing users to rapidly implement and deploy retrieval-augmented generation workflows.

This architecture is aimed not at low-latency transactional workloads but at large-scale, cost-sensitive vector storage and retrieval. For example, if your RAG application needs to draw insights from a large corpus of legal documents, scientific papers, or historical customer records, S3 Vectors offers a scalable and efficient solution that won't break your cloud budget.

Pinecone vs. S3 Vectors: Complementary, Not Competitive

Pinecone is widely regarded as a best-in-class vector database. It is fully managed, serverless, and optimised for ultra-low-latency similarity search over billions of vectors. It supports namespace isolation, dynamic metadata filtering, and high throughput, making it a strong fit for real-time applications such as personalised recommendations, chatbots, and search experiences where latency must stay well below 100ms.

In contrast, S3 Vectors is not engineered to deliver that level of performance. Its design trade-offs favour cost efficiency and scalability over latency, making it more suitable for cold or batch-style retrieval scenarios. These include RAG pipelines that don’t require instant response times, such as generating reports, summarising archived records, or supporting memory for autonomous agents. The flexibility of integrating S3 Vectors with Bedrock means developers can experiment without heavy upfront infrastructure commitments.

It’s worth noting that Pinecone’s leadership publicly welcomed the arrival of S3 Vectors, viewing it as complementary rather than competitive. In essence, where Pinecone serves hot-path inference, S3 Vectors is better suited for cold-path storage and archival retrieval. This dual-layered approach mirrors the evolution of traditional data lakes—where hot OLAP queries hit a data warehouse like Redshift or Snowflake, while colder data lives more cheaply in S3.

OpenSearch: An Evolving RAG Companion

Amazon OpenSearch Service also supports vector search and has already been integrated into Bedrock’s knowledge base feature. It offers hybrid search capabilities that combine keyword search with semantic matching, and is a natural choice for teams already using OpenSearch for observability, log analysis, or full-text search.

However, OpenSearch is not without limitations. It is more operationally complex and expensive than Amazon S3 Vectors. For organisations that don't have strong search engineering expertise, managing OpenSearch clusters - especially for vector-heavy workloads - can become a burden. This is especially true when dealing with terabytes or petabytes of embeddings, where operational overhead and storage costs scale rapidly.

S3 Vectors introduces a tiered vector architecture to address this. Developers can store most of their embeddings in cost-efficient S3 vector indexes, and selectively export “hot” or frequently accessed data to OpenSearch Serverless when low-latency retrieval is required. This hybrid approach gives teams the best of both worlds: S3 for cost-effective durability, and OpenSearch for high-speed queries.

Using S3 Vectors with Amazon Bedrock Knowledge Bases

One of the most powerful use cases for Amazon S3 Vectors lies in its tight integration with Amazon Bedrock Knowledge Bases, a fully managed framework for implementing retrieval-augmented generation pipelines.

With just a few steps, developers can ingest documents into Bedrock, which handles chunking, embedding generation (via Amazon Titan or Cohere), and automatic storage in an S3 vector index. Once stored, these vectors can be queried through Bedrock’s Retrieve or RetrieveAndGenerate APIs, allowing LLMs to ground their responses in enterprise data.

The end-to-end pipeline becomes seamless: ingest PDFs, Word documents, images or web pages into a knowledge base, and leverage S3 Vectors to power semantic search at scale. This dramatically reduces operational burden and eliminates the need to stitch together custom pipelines for chunking, embedding, storing, and querying. Developers benefit from full IAM integration, metadata filtering, and secure storage without managing database servers or scaling concerns.

Additionally, the economics are compelling. Many teams currently using OpenSearch or third-party vector databases such as Pinecone have expressed concerns over monthly bills that reach into the thousands—even for moderate usage. S3 Vectors offers a pathway to scale vector storage by orders of magnitude without sacrificing budget control.

Design Considerations & Best Practices

When implementing Amazon S3 Vectors, it’s important to align your architectural decisions with your latency and retrieval requirements. For instance, in scenarios where real-time inference is not essential - such as legal discovery, compliance audits, or academic research - Amazon S3 Vectors may be the ideal storage layer. On the other hand, if your use case demands responses in under 100 milliseconds, consider routing queries through OpenSearch or Pinecone.

Another key consideration is chunking strategy. Bedrock supports automated chunking during ingestion, but developers should pay attention to the size and content of those chunks to optimise for retrieval accuracy and metadata usage. AWS recommends chunk sizes of 200–500 tokens, which strike a good balance between granularity and context.

From a governance perspective, the combination of S3’s familiar IAM model, encryption features, and auditability makes it easier to enforce policies, especially in regulated industries. Vector indexes can be versioned, tagged, and archived just like other S3 objects.

To maximise flexibility, teams can even build automated workflows that migrate frequently accessed vectors into OpenSearch Serverless based on usage patterns—mirroring the idea of lifecycle policies used in S3 for object tiering.

Greener AI: How to Cut Your Carbon Footprint with Amazon S3 Vectors

Using Amazon S3 Vectors for your AI applications, specifically for RAG, is a powerful way for your organisation to reduce its carbon footprint. It offers a much greener alternative to traditional methods by building vector search capabilities directly into their highly efficient S3 storage service. This avoids the need to run separate, energy-hungry database systems.

The Future of RAG Storage Architectures

Amazon S3 Vectors represents a natural evolution in the AWS stack, enabling serverless vector storage to become a first-class citizen in AI architectures. By reducing the cost and complexity of storing high-dimensional embeddings at scale, AWS has paved the way for broader adoption of retrieval-augmented generation - not just among AI specialists, but across enterprise development teams.

In the coming months, we can expect enhancements to Amazon S3 Vectors, including more fine-grained access policies, support for hybrid queries, tighter integrations with other AWS AI services, and more efficient batch operations. It is also likely that analytics capabilities will expand to support vector-based anomaly detection, similarity graphs, and cross-modal search.

Final Thoughts

As an AWS Ambassador and senior solutions architect working with generative AI use cases, I believe Amazon S3 Vectors fills a long-standing gap in the ecosystem. It enables teams to implement scalable, low-cost, and operationally simple RAG workflows with native support in Amazon Bedrock.

It doesn’t seek to replace fast, dedicated vector databases like Pinecone or advanced search platforms like OpenSearch, but instead offers a compelling new layer in the AI storage pyramid. If you're building AI agents, content retrieval systems, or long-form RAG applications and cost or scale is a concern, now is the time to experiment with S3 Vectors.

About the Author

As an experienced AWS Ambassador and Technical Practice Lead, I have a proven track record of delivering innovative cloud solutions and driving technical excellence within dynamic organisations.

With deep expertise in both Amazon Web Services (AWS) and Microsoft Azure, I lead the successful design and deployment of robust cloud solutions. My extensive knowledge covers the cloud, internet and security technologies, and heterogeneous systems like Windows and Unix, alongside virtualisation, application and systems management, networking, and automation.

I am passionate about championing innovative technology, sustainability, best practices, streamlined operational processes, and high-quality documentation.

Nasstar | Colibri Digital

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Note: These views are those of the author and do not necessarily reflect the official policy or position of any other agency, organisation, employer or company mentioned within the article.