Wafer - 2026 Pricing, Features, Reviews & Alternatives

Wafer is a cloud-based LLM inference platform designed for enterprise AI product teams, AI infrastructure engineers, and GPU kernel developers. It deploys autonomous AI agents that run a continuous profiling loop across five stack layers — model, decode engine, GPU kernels, hardware, and production pipelines — to identify bottlenecks and generate concrete optimization candidates such as fused decode kernels, FP8 quantization passes, and sharding layouts. All candidates are validated for output correctness and measured for speed before shipping to production. Access is available via a serverless pay-as-you-go API (no card required to create an API key) and dedicated cloud endpoints for workloads requiring predictable uptime. Both endpoint types expose OpenAI-compatible and Anthropic-compatible Messages API interfaces, making Wafer compatible out of the box with major agent harnesses including Claude Code, Codex, Cline, Roo Code, Kilo Code, OpenHands, Conductor, OpenClaw, Hermes Agent, and Linzumi.

Wafer targets organizations building voice agents, intelligent copilots, coding agents, interactive AI applications, and large-scale batch or parallel LLM workloads. Hardware support spans NVIDIA and AMD GPUs including H100, B200, B300, MI300X, and MI355X, lowering the cost of evaluating and committing to alternative silicon. The platform includes custom kernel writing capabilities covering fused ops, attention paths, GEMM variants, and decode kernels tuned to specific model shapes and hardware targets. Serving engines are auto-tuned per model, traffic shape, memory pressure, and latency target. The stack re-optimizes continuously as traffic patterns shift, models update, or new hardware is introduced, with no manual re-tuning required. Developer tooling is available via CLI and VS Code IDE integration for kernel development, trace analysis, bottleneck diagnosis, and kernel optimization. WaferBench provides benchmarking for AI-generated GPU kernels.