However, the article also notes that while vector databases are gaining traction, they are not a cure-all for every enterprise search scenario. Some companies are adding vector database search capabilities to their existing databases, while others, like Qdrant, are betting on native solutions built entirely around vectors. The latter argue that their approach provides the speed, memory safety, and scale needed as vector data continues to grow.
Key takeaways:
- Vector databases, which store and process data in the form of vector embeddings, are becoming increasingly popular due to their ability to handle unstructured data and their usefulness in machine learning and AI applications.
- Startups in the vector database space, such as Qdrant, Vespa, Weaviate, Pinecone, and Chroma, have collectively raised significant funding, indicating strong investor interest.
- While vector databases are specialized and can offer superior performance for certain tasks, general-purpose databases like Elastic, Redis, OpenSearch, Cassandra, Oracle, and MongoDB are also adding vector search capabilities.
- Despite the growing trend of adding vector search capabilities to existing databases, Qdrant CEO Andre Zayarni believes that native solutions built entirely around vectors will provide the speed, memory safety, and scale needed as vector data grows.