Which of the following solutions would help a company reduce costs while maintaining data visibility across older records in Amazon Redshift?

Using Amazon Redshift Spectrum to query data directly from S3 is an effective way to reduce costs while maintaining data visibility across older records. This solution allows the company to store infrequently accessed historical data in Amazon S3, which is typically less expensive than keeping all data in Redshift.

By leveraging Redshift Spectrum, you can perform SQL queries that include both data residing in your Redshift cluster and data stored in S3 without the need to move all the data into Redshift. This means that you can keep your Redshift cluster smaller, which reduces costs related to instance hours and storage. Importantly, this approach provides flexible access to archival data without sacrificing performance, thus maintaining visibility over older records.

Other options, while they may have benefits, don't align as directly with the goal of cost reduction and maintaining visibility in the same way. Automatic data lifecycle management policies in Redshift mainly focus on data retention and deletion rather than cost efficiency paired with visibility. Migrating all analytical queries to AWS Glue could lead to increased complexity and potentially higher costs, especially if Glue's serverless model does not match the current query patterns and data usage. Incorporating caching layers might improve performance but doesn't inherently drive cost savings and may introduce additional operational overhead.