RunInfra

RunInfra is a web-based tool that helps developers and MLOps teams optimize open-source AI models for production. Instead of manually testing different serving engines, GPU targets, and configuration settings, you describe your model and workload in plain English, and RunInfra benchmarks the options to find the best combination. It then gives you a transparent receipt with measured p95 latency, throughput, VRAM usage, and cost per million tokens — plus a deployable stack you can run or export.

The tool compares engines like vLLM, SGLang, and TensorRT-LLM, and automatically tunes settings such as quantization, speculative decoding, kernel generation, and continuous batching. You can deploy on NVIDIA GPUs (H100, H200, A100, L4, L40S, B200) and pay per token, or export the entire stack as Dockerfiles, Kubernetes configs, and launch scripts to self-host. RunInfra supports a wide range of open models — Llama, Qwen, Mistral, DeepSeek, Whisper, and many more — across LLM, vision, audio, embedding, and classification tasks.

This tool is built for engineers who want control over their AI infrastructure without the guesswork. It's especially useful for teams deploying open-source models at scale who need predictable performance, cost transparency, and the ability to move between cloud providers. RunInfra is backed by Y Combinator and focuses on making production AI deployment measurable, reproducible, and portable.