With the rapid growth of information technology, users are exposed to a massive amount of data online, including image, music, and video. This has led to strong needs to provide effective corresponsive search services such as image, music, and video search services. Most of them are operated based on keywords, namely using keywords to find related image, music, and video. Additionally, there are image-to-image search services that enable users to find similar images using one input image. Given that videos are essentially composed of image frames, then similar videos can be searched by one input image or screenshot. We want to target this scenario and provide an efficient method and implementation in this paper. We present Shotit, a cloud-native image-to-video search engine that tailors this search scenario in a compute-efficient approach. One main limitation faced in this scenario is the scale of its dataset. A typical image-to-image search engine only handles one-to-one relationships, colloquially, one image corresponds to another single image. But image-to-video proliferates. Take a 24-min length video as an example, it will generate roughly 20,000 image frames. As the number of videos grows, the scale of the dataset explodes exponentially. In this case, a compute-efficient approach ought to be considered, and the system design should cater to the cloud-native trend. Choosing an emerging technology - vector database as its backbone, Shotit fits these two metrics performantly. Experiments for two different datasets, a 50 thousand-scale Blender Open Movie dataset, and a 50 million-scale proprietary TV genre dataset at a 4 Core 32GB RAM Intel Xeon Gold 6271C cloud machine with object storage reveal the effectiveness of Shotit. A demo regarding the Blender Open Movie dataset is illustrated within this paper.