Pinecone blossoms on cost-performance for AI workloads
Because vector workloads aren’t one-size-fits, many applications, such as RAG systems, agents, prototypes and scheduled jobs, have “bursty workloads” meaning that they maintain low, steady traffic most of the time but experience sudden spikes in query volume.
Pinecone’s on-demand vector database service is thirsty for bursty and promises to offer simplicity and elasticity.
The company says that where applications require constant high throughput, operate at high scale and are latency-sensitive (such as billion-vector-scale semantic search jobs, real-time recommendation feeds, or user-facing assistants with tight SLOs) they need critical performance, but also need the cost to be predictable and efficient at scale.
Pinecone Dedicated Read Nodes (DRN) is built for these workloads at this level to give teams reserved capacity for queries with predictable performance and cost.
From RAG to recommendation
The combination of DRN and on-demand services enables Pinecone to support a range of use cases with varying requirements in production with enterprise-grade performance. From RAG to search to recommendation systems and more, you can now choose the service that optimises price-performance for each index.
“With DRN, users get lower, more predictable cost: hourly per-node pricing is significantly more cost-effective than per-request pricing for sustained, high-QPS workloads and makes spend easier to forecast,” detailed the company, in a technical statement. “Predictable low-latency and high throughput: Isolated read nodes and a warm data path (memory + local SSD) deliver consistent performance under heavy load.”
Users can scale for the largest workloads because the technology is built for billion-vector semantic search and high-QPS recommendation systems.
What are Dedicated Read Nodes?
Dedicated Read Nodes are a deployment option where queries run on isolated, provisioned read nodes (no noisy neighbors, no shared queues, no limits). Data stays warm in memory and on local SSD, avoiding cold fetches from object storage and keeping latency low as a system scales.
From a developer’s standpoint, it’s just another Pinecone index: same APIs, same SDKs, same code. Pricing is hourly per-node for cost predictability and strong price-performance for heavy, always-on workloads.
How it works
Dedicated Read Nodes scale along two dimensions: replicas and shards.
- Replicas add throughput and availability. Add replicas to handle more QPS and improve resilience, scaling QPS near-linearly.
- Shards expand storage capacity. Add shards to support more data as your index grows.
- No migrations required. Pinecone moves data and scales read capacity behind the scenes.
- Write behaviour and limits remain the same as on-demand.
One customer uses DRN to power metadata-filtered, real-time media search in their design platform. Across 135M vectors, they sustain 600 QPS with a P50 latency of 45ms and a P99 of 96ms in production. That same customer ran a load test by scaling their DRN nodes and reached 2200 QPS with a P50 latency of 60ms and a P99 of 99ms.
“As workloads grow, most vector databases hit limits. With Dedicated Read Nodes, you control the limits. You get isolated read nodes and a warm data path for predictable low-latency, replicas to scale throughput, shards to grow storage and hourly per-node pricing so costs stay predictable as you grow,” said the Pinecone team.
Pinecone says teams should choose Dedicated Read Nodes when they need performance isolation, predictable low-latency under heavy load, linear scaling as data and QPS grow and cost predictability at scale.
