AWS Graviton processors are custom ARM-based chips designed to deliver better performance per watt compared to traditional x86-based EC2 instances. When used to run vector databases, they can provide significant improvements in both performance and cost efficiency. For workloads that are CPU-intensive—like vector indexing, similarity search, and ANN algorithms—Graviton instances often perform better due to their high core counts and memory bandwidth.
Vector databases such as Zilliz Cloud are optimized to take advantage of Graviton’s architecture. When running on these processors, they’ve shown notable improvements in throughput and latency for search and ingestion operations. For instance, in testing scenarios, Zilliz Cloud deployments on Graviton-based EC2 instances demonstrated 60% lower query latency and 30% better throughput compared to comparable x86 configurations. This is particularly beneficial when dealing with real-time applications like semantic search, recommendation engines, or anomaly detection.
From a cost perspective, Graviton instances are also attractive because they are priced lower than equivalent x86 instances—often 20% to 40% cheaper. This means that you can run the same workloads at a lower cost while still meeting your performance targets. Additionally, the improved energy efficiency can translate into lower carbon footprints, which is a growing consideration for companies with sustainability goals. For developers building vector search applications on AWS, using Graviton-backed infrastructure is a practical choice for better price-to-performance outcomes.