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The Fastest Multimodal Inference OS
Cumulus Labs lets engineering teams ship AI in production without needing a dedicated ML platform team. Right now, companies building AI products are forced to stitch together separate vendors for routing, observability, evaluation, fine-tuning, and inference. This fragmented approach is brittle, expensive, and is a common reason enterprises fail with AI. We replace that entire stack with a single unified platform. Developers can keep their existing code while instantly upgrading to a unified platform that handles routing, semantic caching, continuous shadow evaluation, simulated data, and one-click fine-tuning. Behind the platform is Ion, our proprietary inference engine running on a custom NVIDIA Grace GPU fleet. Ion uses in-house custom GPU kernels to deliver 30 to 50 percent more throughput than standard vLLM or SGLang, giving our customers SOTA inference economics.
Cumulus Labs provides a production-grade inference platform that unifies routing, evaluation, and fine-tuning for AI workloads. It emphasizes OpenAI-compatible gateways, per-workflow routing, caching, real-time observability, continuous evaluation, and one-click LoRA fine-tuning on its Ion inference engine.
The platform offers an OpenAI-compatible gateway, a per-workflow router with deterministic routing rules, a multi-tier cache (exact-match, prefix, and semantic) to reduce tokens, real-time observability with dashboards and audit logs, continuous evaluation using synthetic data and LLM judges, one-click LoRA fine-tuning that trains on production traffic and gradually migrates traffic, and custom hosting to run open weights or fine-tunes on Ion. It runs on NVIDIA Grace and Blackwell with custom attention kernels and delivers 30–50% higher throughput than comparable engines, with failover routing and continuous health checks across providers. The architecture comprises eight subsystems: Gateway, Router, Cache, Observability, Evaluation, Fine-tune, Custom hosting, and Ion, designed to work together across multi-provider setups.
Who it’s for: Teams and organizations deploying production AI workloads that require routing across providers, rigorous evaluation, fast inference, and fine-tuning on production data (e.g., enterprise IT, healthtech, voice AI, and related sectors).
Hiring and growth activity implied by product expansion (e.g., eight subsystems and fleet engineering) and mention of real deployments and book-a-demo lifecycle; no explicit funding round details provided.
Veer studied Computer Science at the University of Wisconsin—Madison, graduating in December 2025. During college, he worked at an aerospace startup where he led a Space Force SBIR contract for military satellite communications and contributed to several NASA SBIR programs, two of which were commercialized and are currently being flight tested in space. Before college, he captained his FIRST Robotics Team 5422: Stormgears, qualifying for Worlds all four years.
Suryaa Rajinikanth studied computer science at Georgia Tech, where he concurrently worked at TensorDock as a Lead Engineer, building the first distributed GPU marketplace serving thousands of consumers and businesses. He went on to deploy critical AI systems and infrastructure in high-performance environments at Palantir.
Meet Cumulus, the GPU cloud where you’re charged by physical resource usage for 50-70% savings.
Cumulus offers a GPU cloud that aggregates compute from multiple sources into a single pool, optimizing training and inference workloads. It predicts resource usage, live-migrates training to cheaper/faster clusters, and serves inference from a global CDN to enable ultra-fast cold starts, aiming to reduce costs by 50-70% and simplify infrastructure management.
AI for AI Infrastructure
Distributed Compute for AI
