  In cloud event processing, data generated at the edge is processed in
real-time by cloud resources. Both distributed stream processing (DSP) and
Function-as-a-Service (FaaS) have been proposed to implement such event
processing applications. FaaS emphasizes fast development and easy operation,
while DSP emphasizes efficient handling of large data volumes. Despite their
architectural differences, both can be used to model and implement
loosely-coupled job graphs.
  In this paper, we consider the selection of FaaS and DSP from a cost
perspective. We implement stateless and stateful workflows from the Theodolite
benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we
show how application type, cloud service provider, and runtime environment can
influence the cost of application deployments and derive decision guidelines
for cloud engineers.
