Applications
390M+ embeddings. 100K+ namespaces. Sustained P50 latency of ~60ms at ~40 QPS. 📈 @ZoomInfo used our Dedicated Read Nodes, now in GA, to byp…
390M+ embeddings. 100K+ namespaces. Sustained P50 latency of ~60ms at ~40 QPS. 📈 @ZoomInfo used our Dedicated Read Nodes, now in GA, to bypass the 'infrastructure wall' and ship real-time AI recommend
390M+ embeddings. 100K+ namespaces. Sustained P50 latency of ~60ms at ~40 QPS. 📈 @ZoomInfo used our Dedicated Read Nodes, now in GA, to bypass the "infrastructure wall" and ship real-time AI recommendations at massive scale. 🚀 The Production Gap: ❌ Bolted-on vector search hits a wall. ❌ DIY clusters = massive overhead. ❌ Latency spikes kill engagement. With Pinecone: ✅ Slab Architecture: Consistent performance, zero fragmentation. ✅ Dedicated Read Nodes: Resource isolation + guaranteed warm data. The Results: 📈 50% increase in engagement. 🎯 2x better relevancy. ⏱️ Hours of research turned into minutes. ZoomInfo case study: https://www.pinecone.io/customers/zoominfo/?utm_source=twitterx&utm_medium=organic-social&utm_campaign=zoominfo DRN GA blog: http://pinecone.io/blog/dedicated-read-nodes-ga?utm_source=twitterx&utm_medium=organic-social&utm_campaign=drn-ga
Related
- Dedicated Read Nodes are now generally available. Predictable performance at scale. Up to 97% lower costs on real production workloads. Fixe…
- NOMAD: Generating Embeddings for Massive Distributed Graphs
- RegD: Hierarchical Embeddings via Dissimilarity between Arbitrary Euclidean Regions
- 20M+ Indian legal documents with citation graphs and vector embeddings – potential uses for legal NLP? [D]
Source: Pinecone (X) | 2026-04-15