AWS Graviton 3 outperforms Graviton 4 in vector similarity search scenarios, delivering better performance and cost efficiency.Graviton4 has a smaller SVE SIMD register size of 128 bits compared to Graviton3's 256 bits, affecting vector search performance.The difference in SIMD register width impacts the efficiency of distance calculations in vector similarity search.Execution throughput comparisons between Graviton3 and Graviton4 show advantages for Graviton3 in loading data efficiently.Latencies in the CPUs are similar, but Graviton4 incurs a higher latency cost due to the smaller register width.Benchmarks reveal that Graviton3 performs better in various vector search scenarios, including IVF and HNSW indexes.Raw distance calculations demonstrate Graviton4's superiority with NEON kernels but show Graviton3's advantage with SVE kernels.The regression in SVE register width for Graviton4 raises questions about the trade-offs in microarchitecture design choice.Choosing the right microarchitecture for vector search use cases can significantly impact performance and cost efficiency.The decision to use Graviton 3 over Graviton 4 for vector search tasks with SVE support is recommended for better performance.In conclusion, careful evaluation based on benchmarks and specific use case requirements is crucial for selecting the optimal microarchitecture.