Pinecone recently announced the public preview of Dedicated Read Nodes (DRN), a new capacity mode for its vector database designed to deliver predictable performance and cost at scale for high-throughput applications such as billion-vector semantic search, recommendation systems, and mission-critical AI services. This capability builds on Pinecone’s existing serverless on-demand model, offering enterprises provisioned hardware for steady high query volumes without the variability inherent in usage-based pricing.
For those unfamiliar, Pinecone is a fully managed vector database designed to store, index, and search high-dimensional embeddings at scale with low latency and predictable performance. It is commonly used to power semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications in production AI systems.
Dedicated Read Nodes allocate exclusive compute and memory resources for query operations, ensuring data stays warm in memory and on local SSD storage to avoid latency spikes from cold data fetches and shared queues. With hourly per-node pricing rather than per-request billing, DRN aims to make costs more predictable for workloads with sustained traffic, while delivering consistent low-latency performance even under heavy load. Developers interact with DRN using the same Pinecone APIs and SDKs as they would in on-demand mode, preserving existing code and workflows.
The architecture scales along two dimensions: replicas to increase query throughput and availability, and shards to expand storage capacity as datasets grow. Pinecone handles data movement and capacity adjustments behind the scenes, eliminating manual migrations and allowing organizations to grow with minimal operational overhead. DRN is particularly suited for applications with strict service-level objectives and consistent demand patterns, such as user-facing assistants requiring sub-100-millisecond latency across millions of vectors or high-QPS recommendation engines driving personalized feeds.
Performance benchmarks shared in the announcement illustrate DRN’s capabilities: one design platform sustained ~600 QPS with median latency around 45 ms on 135 million vectors, scaling up to ~2,200 QPS under load, while an e-commerce marketplace handling ~1.4 billion vectors recorded 5,700 QPS with median latencies in the tens of milliseconds.
Cost predictability is a central benefit claim of DRN. With fixed hourly pricing tied to node count, teams can better forecast spend and optimize price-performance without fluctuating charges tied to individual query volumes. On-demand indexes remain suitable for bursty or variable workloads where autoscaling and usage-based billing offer cost advantages. However, for predictable, heavy usage, DRN provides a compelling alternative when the need for that costing model proves effective./p>
Because DRN indexes are built on Pinecone’s platform but provision dedicated hardware for read operations, they eliminate rate limits present in the on-demand mode and offer linear scaling when adding replicas. This flexibility allows enterprises to fine-tune throughput capacity and grow seamlessly as data volumes and query demands increase.
To get started, users can create a Dedicated Read Nodes index via the Pinecone console or API, selecting node type, number of shards, replicas, and cloud region, typically reaching full read capacity within about 30 minutes. For those already using on-demand indexes, Pinecone provides API support for migrating an existing index to DRN without downtime./p>
There are many different players in the vector database ecosystem, and there are several alternatives to Pinecone’s solution that reflect common architectural patterns outside of Pinecone’s dedicated node model.
Milvus is built for massive scalability and high performance across very large datasets, often reaching billions of vectors. It supports diverse indexing structures such as IVF, HNSW, and GPU acceleration, allowing optimized search for different workload patterns. Milvus typically achieves high throughput, with independent benchmarks showing it can sustain thousands of queries per second when properly configured. Importantly, Milvus separates storage and compute, enabling distributed deployments that scale horizontally to meet large workload demands; this is conceptually similar to dedicated capacity but requires more hands-on infrastructure management. Unlike Pinecone’s managed DRN offering, Milvus can be self-hosted or consumed via managed services such as Zilliz Cloud, giving teams full control over resource allocation.
Qdrant focuses on high-performance similarity search with a cloud-native, horizontally scalable design. Written in Rust, it emphasizes low latency and strong payload filtering, making it suitable for workloads requiring fast nearest-neighbor results with rich metadata constraints. In throughput and latency benchmarks, Qdrant is competitive with managed services like Pinecone for moderate-scale workloads, and can be scaled by adding nodes to distributed clusters.
Where Pinecone’s DRN mode offers predictable performance via reserved hardware, Qdrant’s model typically requires operators to manage and scale clusters themselves. Horizontal scaling can improve throughput and resilience, but creating a predictable cost/performance profile is more dependent on infrastructure choices (VM types, cluster size) than with Pinecone’s bundled node pricing.
Weaviate stands out for combining semantic vector search with structured metadata models and hybrid query capabilities. It supports hybrid retrieval (vector + keyword) and is often chosen for applications needing more expressiveness than pure similarity search. Weaviate scales by distributing shards across nodes, handling high throughput as clusters grow, and its modular architecture allows linking embedding modules directly within the database.
For teams already invested in relational databases, pgvector extends PostgreSQL to support approximate nearest neighbor search using algorithms like HNSW and DiskANN. While it brings vector search into the familiar SQL ecosystem, pgvector is generally best suited to smaller or hybrid workloads and lacks the raw distributed throughput of purpose-built databases. Its performance and scaling depend heavily on PostgreSQL’s configuration and the underlying hardware, making it less ideal for very high QPS environments without additional custom sharding or replication strategies.
