Atlas Vector Search가 다시 한 번 가장 사랑받는 벡터 데이터베이스로 선정되었습니다.

MongoDB Atlas Vector Search has been selected as the most popular vector database for two consecutive years, according to the 2024 Retool AI Trends Report. The report also reveals that Atlas Vector Search received the highest NPS score among users who recommend solutions to their peers. The survey is a global roundup of opinions from developers, tech leaders, and IT decision-makers on topics such as vector databases, RAG (retrieval augmentation generation), AI adoption, and AI innovation challenges. MongoDB Atlas Vector Search has seen rapid growth since its launch in 2023, with a 21.1% increase in NPS score this year, closely trailing pgvector's 21.3%. The survey also highlights the growing preference for RAG over large language models (LLMs) to generate more accurate responses by fine-tuning data. Many companies are using RAG to access real-time stock prices and internal business intelligence like customer and transaction records. Atlas Vector Search can significantly enhance the performance of generative AI applications by allowing users to easily leverage data stored in MongoDB at both training and evaluation stages. The usage rate of vector databases among Retool survey respondents has increased dramatically from 20% in 2023 to 63.6% in 2024, with performance benchmarking (40%), community feedback (39.3%), and concept-proof-of-experimentation (38%) being the key factors influencing their selection. The report emphasizes the challenges faced by developers when selecting AI technologies, with over half of respondents expressing dissatisfaction or difficulty in making a choice. To address this issue, integrated solution product lines can be used to streamline the onboarding process and eliminate the need for multiple vendors. Since vector search is a core feature of MongoDB's developer data platform Atlas, users do not need to adopt standalone solutions. By simply adding vector data to their existing MongoDB Atlas deployment, developers can create AI-based environments. For those interested in building generative AI applications using Atlas Vector Search, several resources are available, including tutorials on integrating Google Gemini's advanced natural language processing capabilities with Vertex AI extension features for enhanced database accessibility and usability.