Hyperstack vs Wafer

Automated software deployment, scaling and management

Optimized templates for GPU-accelerated workloads with custom flavor options

Purpose-built API designed specifically for GPU cloud operations

Network architecture optimized for maximum GPU efficiency

Multiple storage options including NVMe, HDD block, and HDD Shared storage

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