Serverless computing relies on the composition of short-lived stateless functions that are executed following events such as HTTP requests. These functions are mostly deployed in containers called ”replicas”. Scaling a FaaS application, i.e. to maintain a consistent level of performance, consists in growing or shrinking the pool of function replicas following load peaks. When a function is requested while no replica exists on the cluster, it goes through a cold start that incurs an additional initialization delay to the function’s response time. Function images are stored in repositories on dedicated nodes and pulled by worker nodes where and when functions are deployed. Depending on image size, this can have detrimental consequences on request latency with deployments where cold starts dominate a function’s total response time. To mitigate these issues, we propose a distributed function cache that opportunistically takes advantage of available disk space and memory on worker nodes in the cluster. Such nodes are highly heterogeneous: we consider functions that can be executed on different platform architectures, ranging from CPUs, to GPUs and FPGAs. Furthermore, storage on these nodes exhibit various levels of performance and cost, ranging from HDDs, to SSDs and NVMs. By proactively caching functions from the same application using adequate storage on the same nodes, we seek to minimize cold starts and data movement to improve total response times. We evaluate our platform in a simulation environment using workload traces derived from Microsoft’s Azure Functions, enriched with measurements from a deepfake detection project at the B<>com Institute of Research and Technology.