Pinecone is announcing the general availability of its Dedicated Read Nodes (DRN), designed for workloads that need predictable performance, high throughput, and cost-efficient scaling under sustained load.
If you run search, recommendations, or agents with sustained, high-volume traffic, DRN gives users:
- Lower, more predictable cost with fixed hourly per-node pricing that is significantly more cost-effective than per-request pricing for high-QPS workloads and easier to forecast
- Predictable low-latency and high throughput through dedicated, provisioned read nodes with a warm data path (memory + local SSD) that keeps your vectors always hot, no cold start latency regressions
- Scaling that matches production traffic, via replicas for QPS and shards for storage, no rate limits constraining your throughput
- A single API call migration path from On-Demand with no reindexing, no downtime, and no code changes
According to the company, DRN's core value stays the same: dedicated resources, always-hot data, and fixed-cost scaling. And with GA, DRN adds four new production capabilities for deeper control and observability for day-2 operations: configurable performance vs. recall per query, metrics exporting for observability, a web console experience, and multi-namespace support (in early access).
Dedicated Read Nodes gives your index a dedicated serving layer for reads while keeping everything else the same.
Users keep:
- The same Pinecone APIs and SDKs
- The same write pipeline
- The same operational model for your index lifecycle
Users add:
- Dedicated, provisioned read capacity per index
- A warm data path, with data always kept in memory and on local SSD
- No read rate limits, dedicated resources mean you control your throughput ceiling
Users can scale DRN in two dimensions:
Replicas scale throughput and availability—add replicas to increase QPS near-linearly.
Shards scale storage—add shards to grow capacity in fixed increments.
DRN lets users keep Pinecone's simple developer experience while making read performance and costs predictable at production scale, the company said.
For more information about this news, visit www.pinecone.io.