Choosing the Right Vector Database on AWS for AI and ML Workloads

Introduction

As AI/ML systems and large language models (LLMs) become essential to modern applications, we increasingly deal with vector embeddings, numerical representations of text, images, or other data in high-dimensional space. These embeddings are the backbone of intelligent search, recommendation systems, and Retrieval-Augmented Generation (RAG) setups.

The problem is that traditional databases weren’t built to handle similarity searches in this high-dimensional space. That’s where vector databases come in, purpose-built systems designed to store, index, and query vectors quickly and efficiently.

AWS now offers several options in this space, either through native services or via partner solutions.

In this post, we will walk through some of the most popular vector databases available on AWS, explain how they work behind the scenes, and discuss when each makes sense depending on your use case.

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Vector Databases on AWS

Amazon OpenSearch Service (with k-NN plugin)

How It Works:
Amazon OpenSearch, originally based on Elasticsearch, is typically used for full-text search and log analytics. Adding the k-NN (k-nearest neighbor) plugin extends this capability to support vector similarity search.

Under the hood, it uses the HNSW (Hierarchical Navigable Small World) algorithm, a graph-based structure where each data point (vector) connects to its nearest neighbors. This makes it possible to search for similar vectors much faster than brute-force comparisons, even when dealing with millions of records.

When It’s a Good Fit:

When you need both text-based and semantic (vector) search in the same system.
For AI-driven search experiences in enterprise applications.
As part of RAG pipelines built natively within the AWS ecosystem.

Operational Flow:
You’d typically create an index with a vector field type, store embeddings and related document metadata, and perform similarity searches combined with traditional filters or keyword queries.

Amazon Aurora PostgreSQL (with pgvector extension)

How It Works:
Aurora is AWS’s managed relational database, and with the pgvector extension, you can introduce vector similarity search into your relational data world.

Vectors get stored directly as native vector data types within PostgreSQL tables. For efficient querying, it uses an indexing technique called ivfflat (Inverted File Flat), partitioning the vector space into clusters, so searches do not have to scan the entire dataset.

When It’s a Good Fit:

If you already have structured data stored in Amazon Aurora and want to add semantic search to existing applications.
For SaaS platforms needing AI-powered search or recommendations alongside business data.
When you prefer SQL-based query patterns with vector similarity filters built-in.

Operational Flow:
You would install the pgvector extension, define vector columns in your tables, build an approximate nearest neighbor index, and run similarity searches optionally via SQL.

Pinecone (AWS Partner Service)

How It Works:
Pinecone is a fully managed, dedicated vector database service. It handles indexing, storage, scaling, and failover for you. It uses an optimized version of the HNSW algorithm under the hood and offers additional features like metadata filtering, namespaces for logical data separation, and real-time updates.

It’s designed for large-scale AI and Gen AI applications where query latency and system availability are non-negotiable.

When It’s a Good Fit:

When your AI applications deal with millions of vectors and need sub-100ms similarity search latency.
This is for RAG systems, where new data gets added or updated frequently.
If you want a managed solution that abstracts away infrastructure complexity.

Operational Flow:
You would provision a vector index via Pinecone’s console or API, ingest your vectors and metadata, and perform similarity queries through API endpoints. The system automatically handles scaling and high availability behind the scenes.

Weaviate (Deployed via AWS Marketplace or EKS)

How It Works:
Weaviate is an open-source vector database that pairs vector search with a flexible schema and metadata filtering. One of its standout features is automatically vectorizing data using external AI models like OpenAI, Cohere, or Hugging Face through built-in modules.

It supports both FAISS and HNSW for approximate nearest neighbor searches. Additionally, it offers hybrid search, meaning you can combine keyword (text-based) search with semantic (vector) similarity search in the same query.

When It’s a Good Fit:

When you need to combine AI-based semantic search with traditional keyword queries.
If you’re building open-source infrastructure and prefer self-managed deployments.
When you want native integration with your preferred AI model providers.

Operational Flow:
Weaviate can be deployed on AWS through the Marketplace or Amazon EKS. You’d load your data and vector embeddings, and then query through REST or GraphQL APIs, combining text and vector filters as needed.

Milvus (Self-Managed on AWS)

How It Works:
Milvus is a popular open-source, high-performance vector database optimized for massive-scale AI workloads. It supports several indexing algorithms, including IVF_FLAT, HNSW, and ANNOY, allowing you to tune your performance vs. accuracy trade-offs.

Milvus can run in standalone mode for smaller workloads or distributed mode with services like etcd, Pulsar, and MinIO for large, production-grade deployments.

When It’s a Good Fit:

When you need full control over how vectors are indexed and queried.
If your workloads involve tens or hundreds of millions of vectors.
For cost-sensitive, high-scale AI search systems on EC2 or EKS.

Operational Flow:
Milvus is typically deployed via Docker Compose, Kubernetes, or manually on Amazon EC2. You would manage your storage, scaling, and high availability, but gain the ability to customize every aspect of the system. Queries are made via REST or gRPC interfaces.

Quick Comparison

vector db

Conclusion

AWS and its partner ecosystem provide a rich set of options for vector databases, each designed to meet specific needs. The key is to choose based on your data volume, performance requirements, integration needs, and whether you want a managed or self-managed setup. Understanding how each system stores, indexes, and queries vectors ensures you will build AI systems that are both efficient and scalable.

Drop a query if you have any questions regarding Vector Databases and we will get back to you quickly.

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FAQs

1. What is a vector database?

ANS: – A vector database stores and searches high-dimensional vector embeddings to find similar items based on meaning, not exact text matches.

2. Why can’t I use a normal SQL or NoSQL database for vectors?

ANS: – Traditional databases aren’t optimized for similarity search in high-dimensional space, they’re built for exact value lookups.

WRITTEN BY Bineet Singh Kushwah

Bineet Singh Kushwah works as Associate Architect at CloudThat. His work revolves around data engineering, analytics, and machine learning projects. He is passionate about providing analytical solutions for business problems and deriving insights to enhance productivity. In a quest to learn and work with recent technologies, he spends the most time on upcoming data science trends and services in cloud platforms and keeps up with the advancements.