Cloud　Computing　has　become　a　reliable　solution　for　outsourcing　business　data　and　operation　with　its　cost-effective　and　resource-efficient　services.　A　key　part　of　the　success　of　the　cloud　is　the　multi-tenancy　architecture,　where　a　single　instance　of　a　service　can　be　shared　between　a　large　number　of　users,　also　known　as　tenants.　Service　selection　for　multiple　tenants　presents　a　real　challenge　that　has　not　been　properly　addressed　in　the　literature　so　far.　Most　of　the　existing　cloud　services　selection　approaches　are　designed　for　a　single-user,　and　hence　are　inefficient　when　applied　to　the　case　of　a　large　group　of　users　with　different,　and　often,　conflicting　requirements.　In　this　paper,　we　propose　a　multi-tenant　cloud　services　evaluation　framework,　whereby　service　selection　is　carried　out　per　group　of　tenants　that　can　belong　to　different　service　classes,　rather　than　per　a　single　user.　We　formulate　the　cloud　services　selection　for　multi-tenants　as　a　complex　multi-attribute　large-group　decision-making　(CMALGDM)　problem.　Skyline　method　is　initially　applied　to　reduce　the　search　space　by　eliminating　the　dominated　services　regardless　of　tenants＇　requirements.　Tenants　are　clustered　based　on　their　profiles　characterized　by　different　personal,　service,　and　environmental　features.　Each　tenant　is　assigned　a　weight　to　reflect　its　importance　in　the　decision-making.　The　weight　of　a　tenant　is　determined　locally　based　on　its　closeness　to　the　group　decision　and　globally　by　combining　its　local　weight　with　its　corresponding　cluster　weight　to　reflect　its　total　contribution　to　the　overall　decision-making.　The　final　ranking　of　the　alternatives　is　guided　by　a　dynamic　consensus　process　to　reach　a　final　solution　with　the　highest　level　of　agreement.　The　proposed　framework　supports　multiple　types　of　information,　including　deterministic　data,　interval　numbers,　and　fuzzy　numbers,　to　realistically　represent　the　heterogeneity　and　uncertainty　of　security　information.