RunInfra
A service that figures out the fastest, cheapest way to run an open-source AI model in production for you — picking the right GPU, serving engine and settings automatically — instead of you spending weeks tuning it yourself.
🔗 Visit RunInfra
Description
Running an open-source AI model well in production is its own specialty: which GPU is worth the money, which serving engine is fastest for this exact model, how to shrink it without hurting quality. Most teams either hire for this or burn weeks of trial and error. RunInfra automates that process — describe the model and workload you need in plain English, and it benchmarks the options, picks a serving engine, tunes the configuration, and hands you either a ready-to-run deployment package or a fully managed hosted endpoint.
RunInfra benchmarks across 25+ open models and compares six serving engines (including vLLM, SGLang and TensorRT-LLM) and multiple GPU tiers (L4, L40S, A100, H100, H200, B200), reporting transparent metrics — p95 latency, throughput, VRAM usage, cost-per-token — for each combination. Runtime optimizations applied include speculative decoding, custom kernel generation, quantization, KV cache reuse and FlashAttention v2. You can export the optimized deployment kit and run it anywhere with no lock-in, or use RunInfra's managed hosting (or deploy through Modal or RunPod) on a pay-per-token basis, with a scale-to-zero "Flex" mode for bursty workloads and an always-on "Active" mode with sub-2-second cold starts for latency-sensitive use. It's backed by Y Combinator, is SOC 2 Type II certified, and participates in the NVIDIA Inception Program.
💬 Our review
The short version: for a team deploying an open-source model to production who doesn't want to become GPU-optimization specialists, RunInfra's automated benchmarking is worth the $4.50-50 one-time optimization cost — it's cheap insurance against picking the wrong, more expensive GPU/engine combination.
Compared to Modal or RunPod, which give you raw GPU infrastructure and expect you to know how to optimize serving yourself, RunInfra's differentiator is doing that optimization work automatically and showing its reasoning (the benchmark numbers), rather than being a black box. Against Together AI or Replicate, which mostly offer pre-hosted popular models at fixed rates, RunInfra is more useful specifically when you need a less common model optimized for your exact workload rather than picking from a menu. The pricing structure is honestly a bit complex — different rates for input vs. output tokens, tiered pricing that gets steeper for 100B+ parameter and mixture-of-experts models, and TensorRT-LLM optimization gated behind a paid plan — so budget-conscious teams should run the numbers on their specific model size before assuming it's the cheapest option; for very large or MoE models, the cost curve steepens noticeably.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Optimisation ponctuelle 4,50$-50$ selon la taille du modèle (7B-745B paramètres). Déploiement managé facturé au token (mode Flex scale-to-zero, ou mode Active avec charge minimale de 60s).
Pros
Benchmark automatique et transparent (latence, débit, coût par token) sur 25+ modèles
Optimisations avancées automatisées (quantization, speculative decoding, kernels sur mesure)
Kit de déploiement exportable, sans dépendance à un fournisseur unique
Certifié SOC 2 Type II, soutenu par Y Combinator et NVIDIA Inception
Cons
Structure tarifaire complexe (tokens entrée/sortie, paliers selon la taille du modèle)
Optimisation TensorRT-LLM réservée au plan payant
Coût qui grimpe nettement pour les modèles 100B+ ou mixture-of-experts