Vector databases are used to store and search high-dimensional embeddings, making them a good fit for applications involving semantic search, recommendation systems, personalization, image or video similarity, and anomaly detection. On AWS infrastructure, these use cases are enhanced by the availability of scalable compute, GPU acceleration (if needed), and deep integration with services like S3, Lambda, and SageMaker.
For example, a developer building a product recommendation engine can use a vector database to store user and item embeddings generated from behavioral data. When a new user action comes in, the system can run a similarity search to find the most relevant products. With a service like Zilliz Cloud on AWS, this can happen in milliseconds, even when searching through millions of vectors. Another example is semantic search for support documents: embeddings generated from customer questions and knowledge base articles can be stored in a vector database, and queries can be matched based on meaning rather than keywords.
AWS provides the building blocks to scale these use cases easily. You can connect your vector database to data pipelines using services like Kinesis or Kafka, run preprocessing with AWS Lambda or Glue, and deploy AI models on SageMaker to generate embeddings. Since AWS handles networking, security, and compliance, you can also meet enterprise-grade requirements for availability and data protection. Altogether, these capabilities make AWS a robust platform for running production-ready vector search applications.