How NoSQL Databases and Architecting Vector Search on AWS Are Powering the Next Generation of AI and Machine Learning

by Justin Cook

If you’re building intelligent, scalable applications on AWS, it's time guys to look into NoSQL databases. These non-relational systems—like Amazon DynamoDB and Couchbase—are no longer just flexible, high-performance storage layers. They’ve rapidly evolved into critical enablers for real-time AI and machine learning use cases.

Whether you’re building recommendation engines, fraud detection systems, or responsive chatbots, the combination of NoSQL databases and AI services on AWS can provide sub-second performance, adaptive intelligence, and incredible scalability. I’ve personally experimented with many of these patterns, and the results are exciting.

We will now look how NoSQL integrates with services like Amazon SageMaker, AWS Lambda, Amazon OpenSearch Service, and AWS Kinesis to deliver ML-powered applications that are fast, personalized, and production-ready.

At their core, NoSQL databases like DynamoDB and Couchbase are built for scale, flexibility, and speed. They excel at storing semi-structured and unstructured data such as user sessions, metadata, clickstreams, and product catalogs—exactly the kind of rich context that modern ML models thrive on. Recent advancements across AWS have made it easier than ever to tightly couple these databases with machine learning workflows. Innovations like real-time AI inference, SageMaker integration, event-driven triggers with Lambda, and vector storage support have made this ecosystem increasingly powerful.

One of the most transformative use cases is real-time AI inference. Imagine a user browsing a product on your ecommerce platform. Their session is stored in DynamoDB or Couchbase, which can instantly trigger a model hosted in SageMaker. This interaction returns personalized recommendations within milliseconds. Typically, the flow works like this: the customer views a product and that event is logged in DynamoDB. This update triggers a DynamoDB Stream, which invokes an AWS Lambda function. Lambda then calls a SageMaker endpoint with relevant user data, which responds with top product recommendations. These are then written back to DynamoDB and immediately served to the user interface. This loop results in an incredibly responsive and scalable experience.

Couchbase, which we know is one of my favorites, fits into these architectures seamlessly as well. It can store user profile data, session metadata, and catalog information while acting as both a source of truth and a high-performance serving layer. Couchbase Eventing can be used to trigger AWS Lambda functions, perform in-place data transformations, or even invoke external ML endpoints. For edge and mobile scenarios, Couchbase Mobile and Capella offer offline-first experiences, enabling smart apps that sync data back to the cloud. In my experience, using Couchbase Capella with Kafka streams and SageMaker creates a smooth flow from data collection to real-time model inference.

Semantic search and vector similarity have become key pillars of modern AI workloads. NoSQL databases now support storing vector embeddings—dense numeric representations of complex data like text, images, or user behavior. With support for vector storage in DynamoDB and Couchbase, and tight integration with Amazon OpenSearch Service and its K-Nearest Neighbor (KNN) plugin, teams can build semantic search experiences on top of their NoSQL data. For example, imagine storing product embeddings in DynamoDB. When a user searches for an item, the query is converted into an embedding using a large language model. OpenSearch then performs a similarity search and returns contextually relevant results. This approach unlocks a new level of personalized, AI-powered discovery that goes far beyond traditional keyword matching.

Natural language querying is also transforming how we interact with data. Historically, querying NoSQL data—especially JSON documents or nested structures—has required deep technical knowledge. But now, large language models can translate natural language questions into database queries. Users can ask questions like, “Which users abandoned their carts in the past week?” or “What are the most popular items in New York right now?” These questions are parsed by models hosted on SageMaker or accessed via Amazon Bedrock, and translated into DynamoDB filters or Couchbase N1QL queries. This natural interface brings the power of NoSQL to a much broader audience, from product managers to analysts.

Streaming and event-based architectures are critical for enabling AI at scale. AWS offers a robust set of tools to link NoSQL and ML pipelines. Amazon Kinesis can capture clickstream or IoT data in real time, which is then written into DynamoDB. Changes in DynamoDB can trigger Lambda functions, which process data and optionally pass it to SageMaker for inference or retraining. This architecture is highly flexible and ideal for real-time applications like fraud detection, predictive maintenance, and user personalization. I’ve seen great success using Couchbase Eventing for similar flows—directly invoking Lambda or updating downstream services without needing an external orchestrator.

Preparing high-quality training data is one of the most time-consuming steps in any machine learning lifecycle. NoSQL databases excel at storing raw and semi-structured data, making them ideal sources for ML features. With services like AWS Glue, teams can ETL data from DynamoDB or Couchbase into Amazon S3 for long-term storage or use with SageMaker. Lambda functions can be used for real-time data transformations, while SageMaker Data Wrangler or EMR clusters provide deeper analytics at scale. A popular pattern I see more often now is the creation of feature stores on top of NoSQL. These stores cache the most relevant model features with low latency, ensuring that inference requests are both fast and consistent.

Edge computing adds another dimension to this story. With Couchbase Mobile and AWS Greengrass, developers can build AI-enabled apps that work offline or in constrained environments. These systems can run inference locally and sync results to the cloud later, making them ideal for retail, healthcare, or industrial use cases. For example, a smart retail shelf could use computer vision models to detect stock levels, make local decisions, and sync insights to Couchbase Capella in the cloud. This hybrid architecture of edge intelligence and cloud ML provides both responsiveness and centralized learning.

Taken together, the convergence of NoSQL and AI on AWS is creating smarter, more responsive, and far more scalable applications. NoSQL is no longer just a backend for scalable key-value storage. It’s becoming a core component of the AI stack—enabling fast feature access, vector similarity search, semantic interfaces, and real-time model orchestration. Whether you’re using DynamoDB to serve low-latency recommendations or Couchbase to capture and stream behavioral data into SageMaker pipelines, this model is extremely powerful.

In a modern application architecture, you might use DynamoDB or Couchbase to store session and product data. AWS Lambda orchestrates event-driven model calls, while SageMaker or Bedrock powers inference. Amazon OpenSearch Service adds semantic or vector search capabilities. Kinesis and EventBridge keep everything connected in real time. This is an AI-native stack built for scale—and AWS makes it relatively simple to implement.

Start to play with the new tech by building a recommendation engine using DynamoDB, Lambda, and SageMaker. Explore vector search with OpenSearch and Couchbase Capella. Or experiment with natural language querying over NoSQL data using Bedrock models. As AI continues to redefine how applications interact with users, the underlying database matters more than ever. NoSQL and AI together form a stack that’s intelligent, fast, and built for what’s next.

Architecting Vector Search and Retrieval Systems on AWS with NoSQL and AI

As AI continues to evolve, so does the need for smarter, more contextual data retrieval in AWS. Traditional search methods like keyword or filter-based queries often fall short when trying to match on meaning, intent, or similarity. That’s where vector search is CRITICAL, a paradigm shift enabling applications to find results based on semantic relevance rather than exact matches.

AWS is rapidly becoming a fertile ground for building vector-powered applications, especially when paired with high-performance NoSQL databases like Amazon DynamoDB and Couchbase, and integrated with services like Amazon OpenSearch Service, Amazon Bedrock, and SageMaker. Let's start with vector embeddings, NoSQL databases, and AWS-native tooling come together to build scalable, real-time, AI-enhanced search and recommendation systems.

What is Vector Search?

Vector search is transforming how we retrieve and interact with data. Instead of relying on exact keyword matches, vector search converts data—whether it’s text, images, audio, or behavioral signals—into high-dimensional numerical representations known as embeddings. These vectors encapsulate the semantic meaning, context, and similarity of the data. Once stored, you can run nearest-neighbor searches across them to find the most semantically relevant results, even if the query doesn’t contain matching keywords.

For example, instead of a traditional search that looks for the word “headphones,” vector search can understand a query like “best noise cancelling for travel” and return relevant product matches that don’t explicitly include the term “headphones,” but still match the user’s intent. This semantic capability is now foundational across many AI-driven applications. Common use cases include product recommendation engines, natural language search, chatbot memory retrieval, fraud pattern matching, and image similarity detection. AWS provides all the necessary tools to build these systems—but to do so effectively, you need the right data architecture underneath.

Why NoSQL Matters in Vector Architectures on AWS

To support vector-powered applications, the underlying database system must be flexible, scalable, and optimized for performance. This is where NoSQL databases like Amazon DynamoDB and Couchbase excel. Unlike traditional relational databases, which require rigid schemas and can become performance bottlenecks at scale, NoSQL databases offer the flexibility and throughput needed for real-time vector use cases.

NoSQL databases support a dynamic schema model, allowing you to store embeddings of varying sizes as part of JSON documents or attributes without needing to conform to a fixed structure. This flexibility is essential when working with different models and embedding formats. Additionally, NoSQL databases offer high write throughput, which is crucial when updating user embeddings frequently—such as after every user interaction, clickstream event, or product view. They also deliver low-latency read performance, a key requirement when serving recommendations or results to users in real-time.

On top of this, advanced querying capabilities—like Couchbase's N1QL (SQL for JSON) and OpenSearch’s vector retrieval—allow developers to combine semantic search with filtering, scoring, and complex joins. In short, NoSQL forms the backbone of a responsive, AI-native data layer.

Real-Time Vector Search on AWS

An effective vector-enabled architecture on AWS typically combines several services working in tandem. NoSQL databases such as Amazon DynamoDB or Couchbase serve as the real-time, low-latency store for user sessions, metadata, and embeddings. Amazon OpenSearch Service—with its k-Nearest Neighbor (KNN) plugin—functions as the core vector similarity engine, powering approximate nearest neighbor lookups at scale.

Foundation model services like Amazon Bedrock or Amazon SageMaker are used to generate and manage embeddings. These services can process queries, documents, and behavioral logs to output high-dimensional vectors that represent semantic meaning. AWS Lambda serves as the orchestration layer, triggering embedding generation or vector queries as needed. For long-term storage or training data preparation, Amazon S3 plays a role in persisting historical data and snapshots.

In practice, a user might search for something like “great beach vacation camera.” That query is routed to a Bedrock-hosted model—such as Cohere or Amazon Titan—which returns a vector embedding of the phrase. This vector is then passed to OpenSearch to perform a nearest-neighbor query across a product catalog. The resulting product IDs are joined with rich metadata stored in DynamoDB and returned to the user as personalized results—all within a few hundred milliseconds. This architecture enables intelligent, real-time interactions built entirely on AWS-native components.

Let's Look Into Generating and Storing Embeddings

Embeddings can be generated in a variety of ways depending on the model and use case. Amazon Bedrock offers easy, serverless access to foundation models from providers like Cohere, Anthropic, and Amazon itself. You can generate text or image embeddings using their managed APIs. If you require more control, Amazon SageMaker is ideal for training or fine-tuning embedding models using Hugging Face Transformers or PyTorch-based architectures. For lightweight, cost-effective applications, you can even run pretrained models in AWS Lambda to generate embeddings on-demand in real time.

Once generated, embeddings are stored directly alongside application metadata in DynamoDB or Couchbase. For instance, in DynamoDB, you might store a document that contains a user ID, the original query, a timestamp, and an array of floating-point values representing the embedding. Couchbase offers similar flexibility, allowing you to embed this vector data directly into your JSON documents and enrich it with tags, categories, and other attributes. With this setup, you can perform traditional queries—like filtering by category—as well as semantic similarity lookups based on the vector data.

Searching Vectors with OpenSearch KNN

Amazon OpenSearch Service provides robust vector search capabilities via its KNN plugin. This feature allows you to store and index high-dimensional vectors and run similarity queries based on distance metrics such as cosine similarity, L2 norm (Euclidean), or dot product. To enable this, you first create a vector index specifying the dimension and distance metric. You can then ingest embeddings either via AWS Lambda or through a data pipeline, and store them alongside identifiers or metadata.

At query time, OpenSearch allows you to submit a vector and perform a k-nearest-neighbor search. The response includes the closest matching vectors along with associated document IDs. You can then retrieve additional context for these results from DynamoDB or Couchbase. This pattern enables lightning-fast, semantically relevant retrieval of documents, products, or user profiles.

Hybrid Search: Combining Vector and Keyword

While vector search is powerful, the most effective architectures often combine it with traditional keyword-based filtering or boosting—a technique known as hybrid search. OpenSearch supports combining query and knn clauses in a single request. This allows you to prioritize documents that match certain keywords or categories while still scoring them based on vector similarity.

For example, if a user searches for “budget mirrorless camera,” OpenSearch can boost documents tagged with “mirrorless” or “budget” while also considering the semantic vector embedding of the full query. This ensures that the results are both contextually relevant and aligned with user-specified keywords.

Vector Indexing Strategies

Designing a scalable vector architecture requires careful planning. In DynamoDB, embeddings are typically stored as lists of numbers. You can use Global Secondary Indexes (GSIs) to filter data by user segment, region, or category before passing the narrowed-down results to OpenSearch for vector ranking. Since DynamoDB doesn’t support native vector indexing, it’s best used for metadata storage and lookup, with the actual vector search delegated to OpenSearch or SageMaker Feature Store.

Couchbase, on the other hand, has launched vector indexing features (currently in beta in Couchbase Capella) that allow you to run nearest-neighbor searches directly on vector fields. Using N1QL queries, you can combine structured filtering and vector lookup in a single query. Additionally, Couchbase Eventing can be used to trigger vector generation or downstream ML pipelines when documents are created or updated, ensuring that your vector index stays fresh and relevant.

To maintain performance and accuracy, you should regularly refresh user embeddings based on recent interactions, periodically re-embed product catalogs, and purge stale or unused vectors from your indexes.

Best Practices and Cost Considerations

When designing your vector-based system, there are a few best practices to follow. Use batch embedding for static content like articles, products, or catalogs, and real-time embedding for user sessions or ad-hoc queries. Store raw content and preprocessed embeddings in Amazon S3 for retraining or disaster recovery purposes.

Not every document needs to be vectorized. Index only the data that will benefit from semantic search. For example, product descriptions, support tickets, or knowledge base entries are good candidates. Choose the right distance metric based on your modality: cosine similarity works well for text, while L2 norm is more suitable for spatial or image data.

Lastly, security is critical. Use IAM roles to control access between Bedrock, Lambda, OpenSearch, and your NoSQL databases. Encrypt your data in transit and at rest using AWS KMS and follow the principle of least privilege across all components.

What I Have Built

There are a wide variety of production-grade use cases for vector search on AWS. You can build personalized search engines that embed both queries and documents to return tailored results. For recommender systems, vector similarity enables “customers also viewed” or “you might also like” experiences that go beyond collaborative filtering.

Conversational AI benefits from storing past interactions as embeddings and using them for retrieval-augmented generation (RAG), improving the relevance and grounding of LLM responses. Anomaly detection systems can compare behavioral vectors to a baseline of normal activity and flag anything that falls outside the expected range. You can also build multimodal search systems that combine visual and textual embeddings for rich discovery experiences in ecommerce, media, and education.

Vector search represents one of the most exciting frontiers in AI infrastructure. By combining it with NoSQL databases like DynamoDB and Couchbase, you can deliver intelligent, low-latency applications that scale to millions of users. AWS provides a powerful set of building blocks—Amazon Bedrock and SageMaker for embedding generation, OpenSearch for similarity search, and NoSQL for fast storage and metadata enrichment.

NoSQL databases like Amazon DynamoDB and Couchbase are powering a new generation of AI-native applications on AWS by supporting flexible, real-time data architectures. When combined with services like SageMaker, Bedrock, OpenSearch, and Lambda, they enable scalable solutions such as recommendation engines, semantic search, and fraud detection. Vector embeddings and hybrid search unlock personalized, context-aware experiences far beyond traditional keyword matching. Event-driven pipelines and streaming tools like Kinesis and Couchbase Eventing help integrate AI workflows at low latency and global scale. Together, NoSQL and AI form the foundation of intelligent, production-ready systems built for speed, context, and scale.

Justin Cook is an AWS Ambassador, AWS Gold Jacket, AWS Community, AWS SME Exam Creator, and leads Cloud Evangelism across the industry