How to Choose a Vector Database for AWS

Vector databases are critical components of generative AI applications and offer high-dimensional data storage and support for queries across vector data. They convert data into numerical embeddings, employ vector search algorithms, and return a larger volume of data with a broader scope than traditional keyword searches. Vector databases are well-suited to use cases such as building recommendation engines and retrieval-augmented generation (RAG). However, they also have the downside of being yet another element of your architecture that can become a point of failure. AWS provides various options for hosting vector databases, including managed services like Amazon RDS for PostgreSQL and Aurora, serverless services like Amazon Aurora Serverless, and self-hosted options such as Apache Cassandra and DataStax's Astra DB. Each option has its pros and cons, and the choice depends on staffing levels, support availability, and budget. Managed services simplify deployment and maintenance but require someone to monitor and fine-tune them for performance, while serverless services offer zero infrastructure management but are generally more expensive. Self-hosted options provide full control but require significant expertise and resources. AWS offers multiple options for hosting vector databases, allowing developers to choose the best fit for their use case and requirements.