dlt and Sling are two data ingestion tools compared for their performance, cost, and functionality, with dlt emerging as the superior option in several key areas. dlt, an open-source Python library, offers a code-first approach with extensive connector ecosystems and supports incremental loading, schema evolution, and state management, allowing for more flexibility and control. It excels in performance with lower CPU and memory usage and is significantly more cost-effective, as evidenced by its cheaper per-job execution compared to Sling. In contrast, Sling is a low-code ETL platform that emphasizes ease of use with YAML-based configurations and visual interfaces but is less flexible and incurs higher costs, particularly in its commercial version, Sling Pro. dlt's open-source nature and strong community support further enhance its appeal, making it ideal for Python-centric data teams, while Sling remains a viable option for those seeking a straightforward, no-code solution.