Serverless promises the potential to programmatically autoscale cloud resources for genuine pay-as-you go compute. However, in our experience at HomeAway, we found critical gaps with the offerings in what Cloud Providers provided in their core Function as a Service (FaaS) and operational necessities we required to effectively run at scale. These gaps include not being data-centric, a lack of operational support services, inability to employ heterogeneous compute, and a lack of support for a running a wider array of workloads. This lead us ask a simple question – given a function, how can a developer easily deploy to any cloud provider, in any region with scalability, observability, routing, and all the other support services expected by any good developer? More importantly, how can one reconcile FaaS with the existing trends in data-centric, custom hardware demands of Stream Analytics and Machine Learning workloads. This experience at HomeAway led us to build our own FaaS with Kafka as a central backbone. This solution provides us with a clear roadmap to leverage Streaming SQL (KSQL) as well as User Defined Scalar Functions (UDF) and User Defined Aggregate Function (UDAF). We would like to share MultiFaaS, a composable serverless platform for offering highly available, scalable compute to the widest possible audience of customers. It enables developers and non-developers to access compute and be close to data with zero-barrier to entry. MultiFaaS supports varying types of workloads – not only traditional Microservices, but also Streaming Analytics and Machine Learning. It does this while provides seamless integration for scaling, observability, communication, and data. It increases development velocity by removing overhead and allowing users to focus on solving problems.