Lambda Labs

Lambda Labs sells H100/H200/B200 capacity to AI labs at competitive prices. The right answer for teams doing real model training; not a serverless inference platform.

Lambda Labs sits in the "neocloud" category — companies built specifically to sell GPU capacity for AI workloads, distinct from AWS/GCP/Azure. Their value proposition is straightforward: get H100s or H200s on-demand or on reserved contracts at prices materially below the hyperscalers, with a stack (Lambda Stack: PyTorch, CUDA, drivers preinstalled) that's tuned for training rather than general compute.

The trade-off is operational maturity. Lambda doesn't give you the full breadth of services AWS does — no managed Kubernetes equivalents, fewer compliance certifications, less mature support. For training workloads where the team owns the infrastructure layer anyway, this doesn't matter much. For teams that wanted GPUs as part of a broader cloud stack, it matters more. Reserved 1-year contracts get you another 30-50% off but lock you in.

Buy Lambda Labs if you're training real models (multi-node H100 clusters, fine-tuning at scale) and have the GPU cluster ops experience to make use of raw capacity. Use 1-Click Clusters if you want managed multi-node training without standing up Slurm yourself. Skip for inference (use Fireworks/Together/Baseten), and skip if you need the breadth of AWS services bundled with your GPU compute.