Real ChatGPT answer · index seedCV0DC6UYbnccompleteB2B software · data and analytics platformsinformational
How does the query performance of Pinecone compare to Milvus when running dense vector similarity searches over a billion embeddings with high concurrency?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How does the query performance of Pinecone compare to Milvus when running dense vector similarity searches over a billion embeddings with high concurrency?
3 real searches · 1 real-search pass
Pinecone vs Milvus billion vectors high concurrency benchmark dense vector search QPS latencyGoogle ↗
OBSERVEDReason
Pinecone architecture serverless dedicated read nodes high concurrency performance billion vectorsGoogle ↗
OBSERVEDComparison
Milvus billion scale vector search performance benchmark concurrency HNSW IVF DiskANNGoogle ↗
OBSERVEDComparison
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (3)
Classes come from the query-classifier (a local Laya model). 3 of 3 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT3 real searches · gpt-6
Pinecone vs Milvus billion vectors high concurrency benchmark dense vector search QPS latency Google ↗
Pinecone architecture serverless dedicated read nodes high concurrency performance billion vectors Google ↗
Milvus billion scale vector search performance benchmark concurrency HNSW IVF DiskANN Google ↗
- Pinecone Dedicated Read Nodes are now in Public Preview · pinecone.io
- Pinecone Dedicated Read Nodes · pinecone.io
- How Pinecone Works: Architecture and Engineering Deep Dive · pinecone.io
- Milvus 2.2 Benchmark Test Report · milvus.io
- ranging from 17.5× to 39.5× on Cohere 1M and from 15.8× to 31.0× on OpenAI 500K with 16 threads as using a single thread. However, when dataset sizes scale by 10× (to Cohere 10M and OpenAI 5M), the throughput scaling behavior varies across databases. Specifically, the throughput of Milvus-IVF and Milvus-DiskANN plateaus after four concurrent threads, showing 6.0× and 1.6× throughput as one thread on the two datasets with Milvus-IVF, respectively, and 2.1× bandwidth for both datasets with Milvus- · atlarge-research.com
- DiskANN Explained: On-Disk Vector Index Visualized · milvus.io
- VectorDBBench leaderboard · zilliz.com
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Pinecone vs Milvus billion vectors high concurrency benchmark dense vector search QPS latency OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
Pinecone architecture serverless dedicated read nodes high concurrency performance billion vectors OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
Milvus billion scale vector search performance benchmark concurrency HNSW IVF DiskANN OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |