WaveflowDB vs. Pinecone: Real-World Retrieval Benchmarking
WaveflowDB's Global DeepSearch Vector Lake Technology delivers dramatically higher precision, stronger ranking accuracy, and 65% fewer documents retrieved—with consistently better relevance across all domains.
Precision
0.3773 vs 0.0688
WaveflowDB delivers over one-third relevant documents, compared to Pinecone's 7% relevance rate.
F1 Score
0.4626 vs 0.1251
Superior balanced performance across precision and recall metrics.
Documents Retrieved
3.53 vs 10
Eliminates noise and significantly reduces LLM token costs.
Query Performance
47/67 queries
WaveflowDB consistently outperforms across diverse query types.
Recall Performance
0.808 vs 0.688
Finds more relevant documents despite retrieving 65% fewer total documents.
Retrieval Quality Metrics
| Metric | Pinecone | WaveflowDB | Difference (Δ) |
|---|---|---|---|
| Retrieved Document Count | 10.00 | 3.53 | -6.47 |
| Precision | 0.0688 | 0.3773 | +0.3085 |
| Recall | 0.688 | 0.808 | +0.120 |
| F1 Score | 0.1251 | 0.4626 | +0.3375 |
| MRR (Mean Reciprocal Rank) | 0.5370 | 0.6869 | +0.1499 |
| NDCG@10 | 0.5741 | 0.7178 | +0.1437 |
| Win Rate | 20 (30%) | 47 (70%) | +27 |
WaveflowDB demonstrates superior performance across all retrieval quality metrics.
Retrieval Quality Metrics Comparison
WaveflowDB demonstrates significantly higher performance across all metrics, particularly in precision and F1 score.
Average Documents Retrieved
WaveflowDB returns only 35% of the document volume compared to Pinecone.
Relevant Documents Found
Despite retrieving fewer documents, WaveflowDB finds 17.4% more relevant documents.
Win Rate Comparison
WaveflowDB outperformed Pinecone in 70% of queries, demonstrating consistent superiority.
Latency Performance
| Metric | Pinecone (ms) | WaveflowDB (ms) | Difference (ms) |
|---|---|---|---|
| Average | 331.60 | 447.41 | +115.81 |
| Median | 311.18 | 397.23 | +90.24 |
Modest latency increase is offset by substantial improvements in retrieval quality and intelligent filtering.
Retrieval Efficiency
WaveflowDB retrieves fewer but more relevant documents (3.53 vs 10). This eliminates noise and lowers token cost by 65%.
Precision & Relevance
Precision jumps from 6.9% → 37.7%, enabling dramatically cleaner context for LLMs and more accurate responses.
Ranking Quality
Higher MRR (0.6869) and NDCG@10 (0.7178) ensure important documents appear earlier in results.
Cross-Domain Excellence
Stronger retrieval across healthcare, banking, research, literature, and organizational documents.
Latency Trade-off
Slightly higher latency (+115ms) is justified by higher-quality filtering and superior relevance ranking.
Dataset Summary
Multi-domain, full-document, real-world dataset for enterprise-grade evaluation.
Architecture Advantage Summary
WaveflowDB's zero-embedding architecture eliminates 20–40% latency overhead seen in traditional vector databases. Its hybrid filtering requires no metadata setup, improving results even under complex constraints.
This architecture ensures stable, predictable performance at enterprise scale.
Read the Full Technical Benchmark
Download the complete evaluation with methodology, domain-level scores, and raw metrics.