Metabolic cross-feeding is an important process that can broadly shape microbial communities. Comparative genomic analysis of >6000 sequenced bacteria from diverse environments provides evidence to suggesting that amino acid biosynthesis has been broadly optimized to reduce individual metabolic burden in favor of enhanced cross-feeding to support synergistic growth across the biosphere. Still, little is known about specific cross-feeding principles that drive the formation and maintenance of individuals within a mixed population. Here, we devised a series of synthetic syntrophic communities to probe the complex interactions underlying metabolic exchange of amino acids. We experimentally analyzed multi-member, multi-dimensional communities of Escherichia coli of increasing sophistication to assess the outcomes of synergistic cross-feeding. We find that biosynthetically costly amino acids including methionine, lysine, isoleucine, arginine and aromatics, tend to promote stronger cooperative interactions than amino acids that are cheaper to produce. Furthermore, cells that share common intermediates along branching pathways yielded more synergistic growth, but exhibited many instances of both positive and negative epistasis when these interactions scaled to higher-dimensions. This system enabled the identification of synergistic pairings and optimal expression levels of amino acid exporters of arginine, threonine and aromatics towards drastic improvements of ecosystem productivity. Tradeoffs identified in these mutualistic systems between secretion, relative abundance and absolute community productivity have implication in the evolution of cooperative behaviors. Long-term evolution of these synthetic communities highlight transporter over-expression, amino acid pool redistribution, and perturbations to nitrogen regulation as strategies to circumvent imposed metabolic dependencies. To address this potentially problematic genomic plasticity, a genetically reassigned organism is leveraged to investigate synthetic metabolic dependencies showing improved biocontainment and potential for microbial consortia control. These results improve our basic understanding of microbial syntrophy while also highlighting the utility and limitations of current approaches to modeling and controlling the dynamic complexities of microbial ecosystems. This work sets a foundation for future endeavors in microbial ecology and evolution, and presents a platform to develop better and more robust engineered synthetic communities for industrial biotechnology.