Vector search engines built on cloud object storage have drawn growing attention from both academia and industry. These systems typically adopt a disaggregated storage-compute architecture, persisting the primary vector payload in object stores (e.g., Amazon S3) while retaining compact index metadata on compute nodes. However, conventional vector indexing structures are not natively tailored to the pronounced network latency and per-request access costs of cloud object stores, resulting in network I/O amplification, degraded query throughput, and higher API expenses. To resolve these challenges, we present RemoteANN, a vector search engine explicitly co-designed for cloud object storage across indexing and querying. To mitigate the dominant network I/O bottleneck, RemoteANN couples an Inverted File (IVF) index with RaBitQ quantization, further optimized by inter-cluster and intra-cluster data reordering for improved data locality. Building on this tailored data layout, RemoteANN's query engine combines cluster pruning, fine-grained Range-GET requests, request merging, and local NVMe SSD caching, dramatically reducing discrete storage requests and network transfer volume without sacrificing accuracy. To address the index construction bottleneck, we further develop QGKMeans, which substantially reduces construction latency without compromising clustering quality. Extensive evaluations on real-world datasets demonstrate that, compared with representative baselines, RemoteANN achieves up to a $5.9\times$ throughput speedup while issuing up to $16.1\times$ fewer object storage requests at 97% target recall.

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