This study evaluates the capability of Vision-Language Models (VLMs) in image data annotation by comparing their performance on the CelebA dataset in terms of quality and cost-effectiveness against manual annotation. Annotations from the state-of-the-art LLaVA-NeXT model on 1000 CelebA images are in 79.5\% agreement with the original human annotations. Incorporating re-annotations of disagreed cases into a majority vote boosts AI annotation consistency to 89.1\% and even higher for more objective labels. Cost assessments demonstrate that AI annotation significantly reduces expenditures compared to traditional manual methods—representing less than 1\% of the costs for manual annotation in the CelebA dataset. These findings support the potential of VLMs as a viable, cost-effective alternative for specific annotation tasks, reducing both financial burden and ethical concerns associated with large-scale manual data annotation. The AI annotations and re-annotations utilized in this study are available on \href{https://github.com/evev2024/EVEV2024_CelebA}{GitHub}.