In a recent engineering blog post, Graphsignal shared autodebug, an autonomous loop that deploys an inference service, benchmarks it, updates the deployment config, and redeploys it again. This case study looks at the team workflow behind that setup, and how dstack gives Graphsignal a common layer for GPU development, inference deployment, and benchmarking.
In a recent engineering blog post, Toffee shared how they use dstack to run large-language and image-generation models across multiple GPU clouds, while keeping their core backend on AWS. This case study summarizes key insights and highlights how dstack became the backbone of Toffee’s multi-cloud inference stack.
At NVIDIA GTC 2025, Electronic Arts shared how they’re scaling AI development and managing infrastructure across teams. They highlighted using tools like dstack to provision GPUs quickly, flexibly, and cost-efficiently. This case study summarizes key insights from their talk.
EA has over 100+ AI projects running, and the number keeps growing. There are many teams with AI needs—game dev, ML engineers, AI researchers, and platform teams—supported by a central tech team. Some need full MLOps support; others have in-house expertise but need flexible tooling and infrastructure.