When dealing with large vector indexes, the choice between using many cheaper nodes versus fewer powerful ones depends on scalability needs, budget, and workload characteristics. More nodes allow horizontal scaling, distributing the index across machines to handle higher query throughput or larger datasets. This approach improves fault tolerance—if one node fails, the system can reroute traffic. However, coordinating across nodes introduces network latency and management overhead (e.g., sharding, replication). For example, a distributed system like Elasticsearch with a vector plugin might scale well with cheaper nodes but requires robust orchestration. Fewer powerful nodes simplify architecture and reduce inter-node communication, which is useful for latency-sensitive applications. Vertical scaling works if the dataset fits within a single machine’s memory or storage, but it risks bottlenecks if resources are exhausted. Cost isn’t linear: ten $1k nodes might offer more aggregate RAM than one $10k server but add complexity.
Storage performance is critical. NVMe SSDs provide faster read/write speeds (higher IOPS, lower latency) compared to SATA SSDs or HDDs, which matters for vector indexes that require rapid random access. For instance, searching a billion vectors using an approximate nearest neighbor (ANN) algorithm like FAISS benefits from NVMe’s ability to quickly load vector chunks into memory. If the index exceeds RAM capacity and relies on disk-based caching, NVMe reduces query latency. However, NVMe costs more per GB than HDDs, so balancing capacity and speed is key. A hybrid approach might use NVMe for hot data (frequently accessed vectors) and cheaper storage for colder data. In distributed setups, pairing cheaper nodes with NVMe can offset their lower CPU/RAM by accelerating disk-bound operations.
Other hardware factors include RAM size, CPU capabilities, and network bandwidth. Vector indexes often require significant memory—especially for in-memory ANN libraries like FAISS—so nodes need enough RAM to hold working sets. CPUs handle distance calculations (e.g., cosine similarity), so multi-core processors improve parallel query processing. GPUs can accelerate certain operations but add cost. Network bandwidth affects distributed systems: nodes exchanging shard data or results need high throughput to avoid bottlenecks. For example, a cluster with 100 nodes but a 1 Gbps network might struggle under heavy query loads. Balancing these factors—such as opting for nodes with moderate CPU/RAM but high-speed networking—depends on whether the workload is compute-, memory-, or I/O-bound.