RunInfra helps teams turn open-source models into production inference stacks by benchmarking GPUs, tuning supported runtime paths, and either deploying a managed API or exporting the stack for self-hosting.
RunInfra is a platform for turning an open-source model into a production inference stack. You describe the workload, and the service benchmarks GPUs, compares compatible serving engines, tunes supported runtime paths, and produces either a managed API or an exportable stack your team can inspect and own.
The site shows the product being used for model serving, latency and cost checks, GPU selection, and deployment planning across workloads such as chat, speech, embeddings, and retrieval. Pricing is credit-based: Core is a self-serve monthly plan, while Enterprise adds dedicated infrastructure, compliance, custom volume, and support for self-hosted or custom-GPU deployments.
RunInfra compares serving engines and GPU targets for a described workload, then benchmarks the candidates before selecting a path. The site shows comparisons across engines, GPU classes, p95 latency, throughput, VRAM, and cost.
The product applies optimization steps where supported, including quantization, kernel tuning, KV cache reuse, speculative decoding, prefix caching, FlashAttention v2, and batching-related serving settings. The homepage presents these as part of the tuning workflow rather than manual configuration the user has to write by hand.
A run ends with a benchmark receipt and a deployment kit that can be inspected. The site says the stack is not hidden and can be reproduced or changed before deployment.
RunInfra can deploy a production API endpoint or export the stack for self-hosting. The homepage shows managed endpoints, exportable Docker and deployment files, and examples of deployment on RunInfra, RunPod, and Modal.
The pricing page lists OpenAI-compatible API endpoints, agent chat, plans, benchmarking, unlimited pipelines, and versioning in Core. This suggests the workflow covers planning, testing, and serving in one system.
Enterprise adds self-hosted and custom-GPU deployment, audit logs, RBAC, dedicated support, and access to B200/H200 GPUs. The security page adds documented controls such as encryption, logging, and incident response.
Use RunInfra to choose a model, compare GPU candidates, and validate latency, throughput, VRAM, and cost before you deploy. This fits teams that want an evidence-based serving choice rather than a default GPU pick.
Describe a chat, speech, embedding, or retrieval workload and let the platform generate an optimized serving path. The homepage shows examples for Qwen, Whisper, and BGE-style workloads.
Start with a managed endpoint when you want the service to host the stack, then export the same runtime kit if you later need to run it in your own environment. The site shows deployment on RunInfra, RunPod, and Modal, plus downloadable stack files.
Enterprise teams that need private infrastructure, audit logs, RBAC, and a documented security posture can use the Enterprise plan instead of Core. The security page and pricing page describe these controls and support arrangements.
Teams that want a reproducible record of benchmark results can use the receipt and versioned pipeline workflow. The pricing page mentions unlimited pipelines and versioning, and the homepage shows benchmark receipts with before-and-after metrics.
RunInfra takes a described inference workload, matches it to compatible open models, benchmarks GPU options, and produces a deployment path or exportable stack. The output can be used as a managed endpoint or taken elsewhere for self-hosting.
The pricing page shows Core as a self-serve monthly credit plan and Enterprise for dedicated infrastructure, compliance, custom credit volume, and contract terms. Core includes a shared credit balance for optimization, deploys, and the agent.
The homepage and pricing page show support for open-source models and common serving engines such as vLLM and SGLang, along with OpenAI-compatible API endpoints. The site also shows model families such as Llama, Qwen, Mistral, Whisper, Gemma, DeepSeek, and others.
The product can deploy a managed endpoint on RunInfra or export a runnable stack, including files such as Dockerfile, compose.yaml, k8s/deployment.yaml, serve.py, benchmark.md, and runinfra.yaml. The site also shows options to deploy on your own RunPod account or Modal workspace.
RunInfra’s security page states that the service is SOC 2 Type II attested, uses TLS in transit, AES-256 encryption at rest for the primary database and object storage, and role-based access control. Enterprise includes audit logs and RBAC on the pricing page.
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