The rapid expansion of large-scale genomic variant datasets necessitates efficient database architectures for querying and programmatic access. This study systematically compares six database systems—four NoSQL document stores (MongoDB, Elasticsearch, RavenDB, CouchDB) and two PostgreSQL relational schemas—evaluating performance on typical variant queries using whole genome sequencing data. Through twelve representative scenarios including point lookups, range queries, and complex filters, MongoDB achieved the fastest median response times (~2.9 ms), significantly outperforming relational approaches by two orders of magnitude. Elasticsearch showed strength in queries searching nested annotation arrays representing variant identifiers (rsIDs), leveraging its inverted indexing for efficient lookup while CouchDB exhibited performance bottlenecks for nested array operations. This work highlights critical considerations in schema design, query optimization, and API implementation for scalable variant data management, offering practical insights for genomic data infrastructure development and precision medicine applications.