DataStax Astra DB

DataStax Astra DB is a serverless vector-and-document database built on Apache Cassandra, purpose-built for retrieval-augmented generation (RAG) and other AI-native workloads that need low-latency similarity search at scale. Now part of IBM following the 2025 acquisition, it exposes a MongoDB-style Data API alongside CQL, so developers can store JSON documents, structured rows, and dense vectors in a single database and query them with familiar find/insert/update semantics plus vector similarity operators. Its integrated vector search is backed by DiskANN-style indexing and supports hybrid retrieval that combines dense vector similarity, lexical (BM25-like) matching, and metadata filtering in one query — the kind of blended retrieval most production RAG pipelines need but most pure vector stores force you to bolt together. Astra DB also ships with vectorize, a server-side embedding feature that lets you write raw text and have the database call OpenAI, Cohere, Hugging Face, Mistral, NVIDIA NIM, or a bring-your-own endpoint to generate the vector, eliminating a whole class of client-side embedding plumbing. First-class integrations exist for LangChain, LlamaIndex, Haystack, LangFlow (also DataStax-owned), Vercel AI SDK, Apache Airflow, and Kafka. It runs as a fully managed service on AWS, GCP, and Azure with multi-region replication, and the underlying Cassandra core gives you linear horizontal scalability, tunable consistency, and mature operational maturity that pure-play vector databases typically lack. Common workflows: RAG chatbots grounded on enterprise docs, agent memory stores, semantic product search, log/event similarity, and mixed operational+vector workloads where you do not want to run two databases.