Pyramis is a library for quantitative evaluation of hierarchical UML statecharts with non-Markovian stochastic timing and probabilistic choices. It implements an efficient numerical approach for transient analysis until absorption and steady-state analysis, separately evaluating the Semi-Markov Process (SMP) of each model component. As Pyramis facilitates code reusability, maintainability, and extensibility, it has been easily integrated with the FaultFlow library for dependability analysis of component-based systems, supporting efficient quantitative evaluation of stochastic static fault trees without repeated events. In this paper, we use Pyramis for quantitative evaluation of workflows where activities have non-Markovian stochastic duration and where precedence constraints define a Directed Acyclic Graph (DAG). Workflows have a Service Level Objective (SLO) on their end-to-end (E2E) response time distribution at low workloads of requests. Pyramis efficiently derives the workflow E2E response time distribution, yielding a stochastic upper bound for topologies with non-well-nested precedence DAGs. In our experiments, we consider a workflow with topology derived from a real benchmark and execution times obtained from a known dataset. We report results for workflow variants obtained by increasing the number of sequential, concurrent, or alternative activities of workflow patterns. Results are promising in terms of tradeoff between accuracy and complexity.