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Loading...Introduction to Vector Databases
When I first started working with AI-driven applications, I realized that traditional databases weren't optimized for the unique demands of vector search. Last quarter, our team discovered that our vector database was the bottleneck in our recommendation system. We tried using Pinecone, Weaviate, and Faiss, but each had its strengths and weaknesses. Here's what we learned from benchmarking these vector databases.
The Problem with Traditional Databases
Traditional databases are designed for storing and querying structured data, not high-dimensional vectors. When we tried using a traditional database for our vector search, we encountered significant performance issues. The database was unable to efficiently store and query the large number of vectors, resulting in slow query times and high latency.
Pinecone: A Cloud-Native Vector Database
Pinecone is a cloud-native vector database that's designed specifically for AI-driven applications. We were impressed with its ease of use and scalability. However, we found that it was more expensive than the other options, especially at scale.
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