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RunInfra
RunInfra is an AI-powered GPU infrastructure and model deployment platform developed by RightNow AI. By describing desired inference workloads in plain text, the platform's AI agent selects compatible open-source models, benchmarks candidate serving engines like vLLM and SGLang on physical GPUs, and generates custom CUDA kernels using its Forge agent. The resulting OpenAI-compatible API endpoint can be deployed on managed serverless infrastructure with scale-to-zero pricing or exported to self-hosted clouds.
What it does
Generates custom CUDA and Triton kernels for Hugging Face models using agentic optimization loops. Benchmarks serving engines and GPU instances to establish verified latency and throughput metrics. Deploys OpenAI-compatible endpoints supporting streaming, tool use, embeddings, and vision-language tasks. Exports complete deployment kits including Dockerfiles and configuration files for self-hosted execution.
Why it works
Agentic kernel synthesis removes manual vLLM configuration and low-level CUDA tuning requirements. Pay-per-million token pricing with scale-to-zero capabilities prevents idle server compute expenses. Zero lock-in architecture allows exporting full deployment kits directly to private cloud accounts. Built-in benchmark receipts provide verified before-and-after performance validation on real hardware.
Category: AI & Automation
Team Size: 2-10
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As of 2026
AI & Automation
Revenue: 0-$100k ARR
Turns natural language prompts into production-optimized AI inference APIs.
Overview
RunInfra is an AI-powered GPU infrastructure and model deployment platform developed by RightNow AI. By describing desired inference workloads in plain text, the platform's AI agent selects compatible open-source models, benchmarks candidate serving engines like vLLM and SGLang on physical GPUs, and generates custom CUDA kernels using its Forge agent. The resulting OpenAI-compatible API endpoint can be deployed on managed serverless infrastructure with scale-to-zero pricing or exported to self-hosted clouds.
Funding status: Funded
Revenue source: Usage-based
Customer type: B2B
Pricing: Usage-based
Tech stack: Python, C++, CUDA, Triton, vLLM, SGLang, Docker, REST API
Platform: Web
Integrations: Hugging Face, vLLM, SGLang, TensorRT-LLM, RunPod, Modal, OpenAI API Spec
Founder story
RunInfra was built by Jaber Jaber and Osama Jaber under the RightNow AI research lab in 2026. Frustrated by the weeks of manual work required to select GPUs, benchmark serving engines like vLLM, and write CUDA kernels for open-source AI models, the founders developed an autonomous agent harness to automate low-level hardware optimization through plain English commands.
What it does
- Generates custom CUDA and Triton kernels for Hugging Face models using agentic optimization loops
- Benchmarks serving engines and GPU instances to establish verified latency and throughput metrics
- Deploys OpenAI-compatible endpoints supporting streaming, tool use, embeddings, and vision-language tasks
- Exports complete deployment kits including Dockerfiles and configuration files for self-hosted execution.
Who it's for
- AI Engineers & LLM Developers
- DevOps & Infrastructure Teams
- Enterprise AI Application Builders
- Voice Agent & RAG Solution Architects
Why it works
- Agentic kernel synthesis removes manual vLLM configuration and low-level CUDA tuning requirements
- Pay-per-million token pricing with scale-to-zero capabilities prevents idle server compute expenses
- Zero lock-in architecture allows exporting full deployment kits directly to private cloud accounts
- Built-in benchmark receipts provide verified before-and-after performance validation on real hardware.
Growth strategies
- Participating in Y Combinator to build early founder traction across AI startups.
- Publishing peer- reviewed AI inference and CUDA kernel research on arXiv.
- Providing open- source model optimization documentation to capture developer search traffic.
- Offering a pay- per-token cloud tier alongside self-hosted export options.
Alternatives
Comparison overview
- RunInfra generates custom CUDA kernels and benchmark receipts for open-source models using natural language prompts, whereas traditional inference platforms require manual container configuration and YAML files.
- RunInfra allows exporting the entire optimized deployment stack to self-hosted clouds like RunPod or Modal, whereas closed API providers enforce proprietary cloud host lock-in.