ICYMI: Dedicated Read Nodes are now in Public Preview Need predictable performance for high-throughput vector workloads? Pinecone's newest feature gives you reserved capacity with: ✓ Hourly per-node pricing for cost predictability ✓ Warm data paths (memory + SSD) for consistent low latency ✓ Linear scaling: add replicas for throughput, shards for storage Perfect for billion-vector semantic search, recommendation systems, and mission-critical AI services with strict SLOs. Choose what fits your workload: → On-Demand for bursty, elastic traffic → DRN for always-on, high-QPS applications Learn more about Dedicated Read Nodes: https://lnkd.in/g8b5WCTm
Dedicated read nodes solve throughput, but the real friction shows up when teams underestimate how violent QPS spikes can be once agents start chaining vector calls. At SAP we learned the hard way that read isolation only works if your indexing cadence doesn’t fall behind your traffic shape. How are you seeing teams balance freshness vs deterministic latency as their vector workloads mature?
This is big. You guys basically saying, “Here, have enterprise-grade performance without the random slowdowns.” Dedicated Read Nodes are a cheat code if you’re running high-QPS apps. No cold starts, no surprise bottlenecks, just consistent speed. If you’re building anything vector-heavy, this is the kind of infrastructure upgrade that quietly saves you a ton of headache (and money).