Companiesincreasingly rely onstreamprocessingengines(SPEs)to quickly analyze data and monitor infrastructure. These systems enable continuous querying of data at high rates. Current productionlevel systems, such as Apache Flink and Spark, rely on clusters of servers to scale out processing capacity. Yet, these scale-out systems are resource inefficient and cannot fully utilize the hardware. As a solution, hardware-optimized, single-server, scale-up SPEs were developed. To get the best performance, they neglect essential features for industry adoption, such as larger-than-memory state and recovery. This requires users to choose between high performance or system availability. While some streaming workloads can afford to lose or reprocess large amounts of data, others cannot, forcing them to accept lower performance. Users also face a large performance drop once their workloads slightly exceed a single server and force them to use scale-out SPEs. To acknowledge that real-world stream processing setups have drastically varying performance and availability requirements, we propose scale-in processing. Scale-in processing is a new paradigm that adapts to various application demands by achieving high hardware utilization on a wide range of single- and multi-node hardware setups, reducing overall infrastructure requirements. In contrast to scaling-up or-out, it focuses on fully utilizing the given hardware instead of demanding more or ever-larger servers. We present Darwin, ourscale-in SPEprototype that tailors its execution towards arbitrary target environments through compiling stream processing queries while recoverable larger-than-memory state management. Early results show that Darwin achieves an order of magnitude speed-up over current scale-out systems and matches processing rates of scale-up systems.