Vector Databases on AWS: A Practical Guide to Embedding-Based Search

In today’s data-driven landscape, organizations deal with a mix of text, images, code, and structured data. Storing raw data is only half the battle; extracting meaning from it often requires converting content into high-dimensional vectors (embeddings) and performing fast similarity searches. A vector database on AWS provides the storage, indexing, and query capabilities needed to support semantic search, recommendations, multimodal retrieval, and more. This article breaks down what a vector database is, how AWS offers native support, and how to design practical architectures that balance latency, cost, and accuracy.

What is a vector database and why use it on AWS?

A vector database is a specialized storage and query system that holds high-dimensional numerical representations of data and returns items based on vector similarity. Unlike traditional databases that rely on exact keys or relational queries, a vector database excels at nearest-neighbor search, cosine similarity, dot product, and other distance metrics. When paired with embeddings produced by machine learning models, it enables powerful capabilities such as:

• Semantic search that understands intent beyond exact keywords
• Recommendation and personalization driven by content similarity
• Multimodal retrieval across text, images, and other data types
• Code or document search that matches concept-level meaning

On AWS, you can combine managed services to build a robust vector data stack without managing the underlying infrastructure. Key advantages include integration with security, monitoring, and scalable storage, as well as access to a variety of model and data tools that fit different parts of the workflow.

Core AWS options for vector data

OpenSearch Service with kNN vector search

A primary option for many teams is OpenSearch Service, the managed service based on OpenSearch. OpenSearch includes a k-Nearest Neighbors (kNN) capability that supports storing embeddings as vector fields and running approximate nearest neighbor (ANN) queries. The typical pattern is to index embeddings alongside metadata for each document or item, then issue vector queries to retrieve the most similar items.

• Vector indexing uses algorithms such as HNSW (Hierarchical Navigable Small World) to balance accuracy and latency.
• Hybrid search allows combining vector similarity with traditional keyword or attribute filtering to refine results.
• Managed security features (IAM, encryption, VPC endpoints) simplify compliance and access control.
• Scalability is achieved through shards and chosen instance types, with options for auto-scaling in some configurations.

OpenSearch Service is well-suited for document search, knowledge bases, and enterprise content repositories where you need fast semantic retrieval alongside classic text search.

Aurora PostgreSQL with vector search

Aurora PostgreSQL with vector search brings vector storage closer to relational data. With a vector data type (often used via extensions like pgvector), you can store embedding vectors in PostgreSQL tables and index them for fast similarity queries using SQL. This approach helps organizations keep their structured data and vector representations in a unified environment, enabling hybrid queries that combine vector similarity with relational filters, aggregates, and joins.

• SQL-based vector queries can be combined with traditional SQL to support complex business logic.
• Native integration with existing PostgreSQL tooling and schemas can simplify adoption.
• Good fits include catalog search, customer analytics, and use cases where relational constraints matter alongside vector similarity.

Kendra for semantic search across documents

AWS Kendra is a specialized service designed for enterprise search and knowledge discovery. While Kendra handles indexing, relevance, and access control out of the box, it also supports embedding-based semantic retrieval scenarios. You can feed documents and embeddings into Kendra’s index, use natural language queries, and rank results with machine-learned relevance. This is particularly useful for internal search portals, help desks, and knowledge bases where precise alignment with user intent is important.

SageMaker and Feature Store for embeddings

Amazon SageMaker covers the model lifecycle—training, hosting, and inference—and can generate embeddings from various models. SageMaker Feature Store helps you persist and serve feature data, including vector features, in a governed, scalable manner. A common pattern is to generate embeddings with a SageMaker endpoint, store them with the corresponding metadata in Feature Store, and then push the vectors to a vector-enabled store (OpenSearch or Aurora) for fast retrieval. This workflow supports batch and real-time pipelines and keeps feature definitions discoverable for downstream applications.

Architectural patterns for vector data on AWS

• Data ingestion and embedding generation: Source content (documents, images, code) is processed to produce embeddings using SageMaker or external models. Each item is tagged with metadata (author, date, category) to enable filtering and scoring.
• Vector storage and indexing: Embeddings are stored in a vector-aware store (OpenSearch Service, Aurora with vector support). Index settings optimize latency and recall for your typical query size and dimension.
• Hybrid retrieval: Combine a vector similarity query with traditional filters (per metadata, facets, or text search) to achieve precise results. This approach often yields better relevance than either method alone.
• Monitoring and governance: Track latency, hit rate, and cost. Use IAM policies, encryption, and network controls to meet security and compliance requirements. Version embeddings and model metadata to support reproducibility and rollback.

Typical use cases on AWS

• Semantic product search and recommendation: users find items that are conceptually similar to their queries or past interactions.
• Document and code search: developers and knowledge workers locate relevant content based on meaning, not just keywords.
• Image or multimedia similarity: identify visually similar assets for cataloging or moderation workflows.
• Multimodal retrieval: blend text, image, and structured data to deliver rich search results.

Getting started: a practical workflow

1. Define the goal: Semantic search, recommendation, or multimodal retrieval? Decide which AWS services best align with the goal.
2. Choose the storage layer: OpenSearch Service for vector search with hybrid capabilities, or Aurora PostgreSQL for tight relational integration with vector data. Consider Kendra if enterprise search needs are primary.
3. Set up embedding generation: Use SageMaker to train or host a model that produces high-quality embeddings for your data.
4. Ingest and index: Produce embeddings for your dataset and load them into the chosen vector store, along with metadata for filtering and scoring.
5. Design queries: Build vector similarity queries and hybrid filters. Start with a simple nearest-neighbor search and gradually add text filters and ranking rules.
6. Test and iterate: Measure latency, precision, recall, and user satisfaction. Tune index parameters (e.g., number of neighbors, search depth) and model choices accordingly.

Cost and performance considerations

Costs for vector workloads on AWS depend on data volume, embedding dimensions, query latency targets, and the chosen storage service. A few practical guidelines include:

• Dimensionality vs. latency: Higher-dimensional embeddings offer richer representations but can increase indexing and query time. Balance accuracy with latency requirements.
• Hybrid search tuning: When combining vector queries with text filters, you may reduce the amount of data processed per query, improving latency and cost-efficiency.
• Index maintenance: Vector indexes may require periodic optimization or retraining when embeddings are updated or new data arrives. Plan for incremental updates rather than full rebuilds when possible.
• Security and governance: Enable encryption at rest and in transit, use VPC endpoints, and manage access with IAM roles. Security is a cost driver but essential for enterprise workloads.

Best practices and design tips

• Model selection matters: Choose embedding models that align with your domain (e.g., legal docs, customer support, code). Benchmark on a representative subset before full rollout.
• Dimension and indexing strategy: Pick a dimension that matches the model and an index configuration (e.g., HNSW graph parameters) that yields the best trade-off between accuracy and speed.
• Hybrid architectures offer resilience: Keep metadata in relational stores or a data warehouse to enable rich filtering, while using the vector store for fast similarity retrieval.
• Observability: Use CloudWatch, OpenSearch dashboards, or SageMaker monitoring to track query performance, cache hit rates, and model drift that could impact embedding quality.

Future directions and considerations

Vector databases on AWS are evolving, with deeper integration between embedding generation, feature management, and vector search performance. As models improve and data grows, expect more streamlined workflows for end-to-end semantic search—from data ingestion to user-facing results. The trend toward multimodal and context-aware retrieval will push architectures toward unified storage of vectors across text, image, and other data types, with stronger guarantees around latency and consistency.

Final thoughts

Building a robust vector data solution on AWS involves choosing the right combination of services, from OpenSearch Service for scalable vector search to Aurora PostgreSQL for relational workloads with vector capabilities, and SageMaker to generate and manage embeddings. A well-designed architectural pattern emphasizes hybrid retrieval, governance, and observability, ensuring your semantic search and recommendation workloads scale with business needs. By starting with a clear use case, validating embedding quality, and iterating on index configurations, teams can unlock powerful retrieval capabilities that were difficult to achieve with traditional databases.