A New Era AI Databases: PostgreSQL with pgvectorscale Outperforms Pinecone and Cuts Costs by 75% with New Open-Source Extensions
How do the costs of using PostgreSQL with pgvectorscale and pgai compare to using specialized vector databases like Pinecone?
The cost benefits of using PostgreSQL with pgvector and pgvectorscale are substantial compared to specialized vector databases like Pinecone. Self-hosting PostgreSQL is approximately 45 times cheaper than using Pinecone. Specifically, PostgreSQL costs about $835 per month on AWS EC2, compared to Pinecone's $3,241 per month for the storage-optimized index and $3,889 per month for the performance-optimized index. This significant cost reduction makes PostgreSQL with pgvectorscale and pgai an attractive and affordable option for AI application development.
What are the key innovations introduced in the pgvectorscale extension, and how do they improve query performance and storage efficiency?
The pgvectorscale extension introduces two key innovations that significantly enhance query performance and storage efficiency in AI applications:
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StreamingDiskANN index: This index is adapted from Microsoft research and greatly improves query performance. The StreamingDiskANN index is a graph-based index that uses a streaming approach to build the graph, which makes it more efficient in terms of memory usage. It also leverages the disk storage for storing the graph, which reduces the memory footprint.
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Statistical Binary Quantization: Developed by Timescale researchers, this technique improves upon standard Binary Quantization. Statistical Binary Quantization is a compression method that reduces the number of bits required to represent each vector dimension, leading to significant storage savings. This technique also improves query performance as the compressed vectors can be searched more efficiently.
These innovations in pgvectorscale enable PostgreSQL to achieve 28x lower p95 latency and 16x higher query throughput compared to Pinecone for approximate nearest neighbor queries at 99% recall.
What specific features do the pgvectorscale and pgai extensions add to PostgreSQL to enhance its performance for AI applications?
The pgvectorscale and pgai extensions add several specific features to PostgreSQL to enhance its performance for AI applications:
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pgvectorscale: This extension helps developers build more scalable AI applications with higher performance embedding search and cost-efficient storage. It introduces two key innovations:
- StreamingDiskANN index: Adapted from Microsoft research, this index significantly enhances query performance. It is capable of offloading half of its search indexes to disk, which helps save greatly on costs.
- Statistical Binary Quantization: Developed by Timescale researchers, this compression method improves on standard Binary Quantization, leading to substantial performance gains.
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pgai: This extension simplifies the development of search and retrieval-augmented generation (RAG) applications. It allows developers to create OpenAI embeddings and obtain OpenAI chat completions directly within PostgreSQL.
These extensions make PostgreSQL faster than Pinecone for AI workloads and 75% cheaper. They enable developers to build retrieval-augmented generation, search, and AI agent applications with PostgreSQL at a fraction of the cost compared to specialized vector databases like Pinecone.