Phytoplankton is a key biological group used to assess the ecological status of lakes. The classical monitoring approach relies on microscopic identification and counting of phytoplankton species, which is time-consuming and requires high taxonomic expertise. High -throughput sequencing, combined with metabarcoding, has recently demonstrated its potential as an alternative approach for plankton surveys. Several studies have confirmed the relevance of the diatom metabarcoding approach to calculate biotic indices based on species ecology. However, phytoplankton communities have not yet benefited from such validation. Here, by comparing the results obtained with the two methods (molecular and microscopic counting), we evaluated the relevance of metabarcoding approach for phytoplankton monitoring by considering different metrics: alpha diversity, taxonomic composition, community structure and a phytoplankton biotic index used to assess the trophic level of lakes. For this purpose, 55 samples were collected in four large alpine lakes (Aiguebelette, Annecy, Bourget, Geneva) during the year 2021. For each sample, a metabarcoding analysis based on two genetic markers (16S and 23S rRNA) was performed, in addition to the microscopic count. Regarding the trophic level of lakes, significant differences were found between index values obtained with the two approaches. The main hypothesis to explain these differences comes from the incompleteness, particularly at the species level, of the barcode reference library for the two genetic markers. It is therefore necessary to complete reference libraries for using such species -based biotic indices with metabarcoding data. Besides this, species richness and diversity were higher in the molecular inventories than in the microscopic ones. Moreover, despite differences in taxonomic composition of the floristic lists obtained by the two approaches, their community structures were similar. These results support the possibility of using metabarcoding for phytoplankton monitoring but in a different way. We suggest exploring alternative approaches to index development, such as a taxonomy -free approach.