MongoDB announced the general availability of MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes in the latest in a series of announcements to enable developers to more easily build generative AI applications.
The general availability of MongoDB Atlas Vector Search and Atlas Search Nodes follows up from a June launch where the company set out to enable AI and large language model (LLM) workloads.
Customers using MongoDB Atlas Vector Search and Atlas Search Nodes include AT&T Cybersecurity, Pathfinder Labs and UKG.
Since that launch MongoDB has had a steady cadence of launches. For instance, at AWS re:Invent, the company said it would integrate MongoDB Atlas Vector Search with Amazon Bedrock and announced it would optimize Amazon CodeWhisperer suggestions for MongoDB developers.
MongoDB steps up generative AI rollout across platform
Those horizontal services are also being complemented by MongoDB's focus on industry specific use cases for healthcare, public sector, manufacturing and automotive as Atlas ramps.
MongoDB, along with companies such as Databricks and Snowflake, are aiming to help developers and enterprises leverage real-time data to build generative AI applications. The argument is that AI applications can't be built efficiently without strong data strategies. Atlas Vector Search and Atlas Search Nodes are designed to give enterprise the capability to search real-time data efficiently.
Key points about MongoDB's two additions:
MongoDB Atlas Vector Search, which is an integrated vector database for MongoDB. Developers can use one API to build generative AI applications across AWS, Microsoft Azure and Google Cloud without duplicating and synchronizing data. MongoDB Atlas Vector Search also enables enterprises to use retrieval-augmented generation (RAG) with pre-trained foundation models.
Atlas Vector Search integrates with models from LangChain, Cohere, OpenAI, LlamaIndex, Hugging Face and Nomic.
MongoDB Atlas Search Nodes provide dedicated infrastructure to manage workloads using MongoDB Atlas Vector Search and Atlas Search independent of operational nodes of the database. This workload isolation performs better at scale and optimizes costs.