In the last 5 years, the rapid pace of innovations and improvements in sequencing technologies has completely changed the landscape of metagenomic and metagenetic experiments. Therefore, it is critical to benchmark the various methodologies for interrogating the composition of microbial communities, so that we can assess their strengths and limitations. The most common phylogenetic marker for microbial community diversity studies is the 16S ribosomal RNA gene and in the last 10 years the field has moved from sequencing a small number of amplicons and samples to more complex studies where thousands of samples and multiple different gene regions are interrogated.We assembled 2 synthetic communities with an even (EM) and uneven (UM) distribution of archaeal and bacterial strains and species, as metagenomic control material, to assess performance of different experimental strategies. The 2 synthetic communities were used in this study, to highlight the limitations and the advantages of the leading sequencing platforms: MiSeq (Illumina), The Pacific Biosciences RSII, 454 GS-FLX/+ (Roche), and IonTorrent (Life Technologies). We describe an extensive survey based on synthetic communities using 3 experimental designs (fusion primers, universal tailed tag, ligated adaptors) across the 9 hypervariable 16S rDNA regions. We demonstrate that library preparation methodology can affect data interpretation due to different error and chimera rates generated during the procedure. The observed community composition was always biased, to a degree that depended on the platform, sequenced region and primer choice. However, crucially, our analysis suggests that 16S rRNA sequencing is still quantitative, in that relative changes in abundance of taxa between samples can be recovered, despite these biases.We have assessed a range of experimental conditions across several next generation sequencing platforms using the most up-to-date configurations. We propose that the choice of sequencing platform and experimental design needs to be taken into consideration in the early stage of a project by running a small trial consisting of several hypervariable regions to quantify the discriminatory power of each region. We also suggest that the use of a synthetic community as a positive control would be beneficial to identify the potential biases and procedural drawbacks that may lead to data misinterpretation. The results of this study will serve as a guideline for making decisions on which experimental condition and sequencing platform to consider to achieve the best microbial profiling.