Lambda's positioning: simpler and cheaper than AWS/GCP for GPU workloads, without the complexity of general-purpose cloud services. You get GPU instances with PyTorch, CUDA, and ML frameworks pre-installed — no need to configure networking, storage, or container orchestration unless you want to. This simplicity appeals to researchers and small teams who want to train models, not manage infrastructure.
Lambda competes with: hyperscalers (AWS, GCP, Azure — expensive but feature-rich), other GPU clouds (CoreWeave, RunPod, Vast.ai — various price points and availability), and on-premise options (buying NVIDIA DGX systems). The GPU cloud market is growing rapidly because AI training demand far exceeds supply, and most organizations can't justify the capital expense and operational complexity of owning GPU infrastructure.