Cognitive cloud services seek to provide end-users with functionalities that have historically required human intellect to complete. End-users expect these services to be both responsive and accurate, which pose conflicting requirements for service providers. Today’s cloud services deployment schemes follow a “one size fits all” scale-out strategy, where multiple instantiations of the same version of the service are used to scale-out and handle all end-users. Meanwhile, many cognitive services are of a statistical nature where deeper exploration yields more accurate results but also requires more processing time. Finding a single service configuration setting that satisfies the latency and accuracy requirements for the largest number of expected end-user requests can be a challenging task. As a result, cognitive cloud service providers are conservatively configured to maximize the number of enduser requests for which a satisfactory latency-accuracy tradeoff can be achieved. Using a production-grade Automatic Speech Recognition cloud service as a representative example to study, we demonstrate the inefficiencies of this single version approach and propose a new service node multi-versioning deployment scheme for cognitive services instead. We present an oracle-based limit study where we show that service node multi-versioning can provide a 2.5X reduction in execution time together with a 24% improvement in accuracy over a traditional single version deployment scheme. We also discuss several design considerations to address when implementing service node multi-versioning.