Crusoe vs Wafer

Vertically integrated power sourcing across wind, solar, hydropower, geothermal, natural gas, and carbon capture

Machines are deployed quickly when they are launched

Persistent and shared disks, object storage, and a container registry for AI datasets

Provides instances with NVIDIA SXM GPU interconnects

99.5% uptime commitment backed by 24/7 enterprise-grade support

LLM inference with up to 9.9x faster time-to-first-token and 5x higher throughput versus vLLM

Pay-as-you-go API access to hosted open-source models including GLM, Kimi, Qwen, and DeepSeek with no infrastructure management

Custom-tuned inference deployments with performance guarantees, provisioned in under 24 hours

Agents profile inference bottlenecks and tune across serving engines (vLLM, SGLang, TensorRT-LLM), custom kernels (CUDA, HIP, Triton, NKI), quantization (FP8/FP4), and decode strategies

Workloads run on NVIDIA B200/B300, AMD MI350X/MI355X, and AWS Trainium depending on the model and traffic shape

OpenAI-compatible endpoint at pass.wafer.ai/v1 and Anthropic-compatible endpoint at pass.wafer.ai/v1/messages, both using Bearer token authentication

Cached input tokens are billed at reduced rates on supported models