Rembrand

Solution

DWS on GKE gave us reliable, cost-efficient access to the accelerators we needed, so consolidating our core AI workloads on Google Cloud made sense. In the early days, Rembrand relied on a costly patchwork of cloud providers and neocloud GPU platforms that slowed the team down. AWS supported much of their production layer, and GPU-heavy experiments bounced between Google Kubernetes Engine (GKE) and other… "For a long time, running across multiple clouds felt unavoidable. Access to high-end GPUs was unpredictable, and we had to take capacity wherever we could," said Saad. But when the team found Google Cloud's Dynamic Workload Scheduler (DWS), it immediately… Key components include Dynamic Workload Scheduler (DWS) orchestrates scheduled workflows and service-to-service jobs across the platform; Cloud Storage stores large files, lake data, or static assets that feed downstream analytics and applications; Google Kubernetes Engine (GKE) orchestrates the platform's containerized.

Data flow

Instead of reserving entire eight-GPU machines just to run a single experiment, DWS allocates the exact number of GPUs needed and queues jobs efficiently. For Rembrand, this immediately cut training costs by 45–55% while giving them consistent access to high-end accelerators. "Accessing high-end GPUs in single-unit slices meant we could experiment constantly without overprovisioning or waiting weeks for capacity," Khamis said.