OpenRelay
openrelay.incOpenRelay operates a distributed GPU network delivering resilient, hardware-agnostic inference and on-demand compute for developers.
About
OpenRelay builds a distributed GPU network for hosted model inference and dedicated GPU compute, serving developers and teams that have outgrown hyperscaler costs. Its differentiation is a hardware-agnostic overlay with automatic failover and load balancing, aggregating fragmented GPU capacity to offer substantially lower prices than major clouds.
Market
OpenRelay competes in AI infrastructure and GPU-cloud markets, combining hosted model inference with dedicated GPU compute through a distributed overlay network that connects consumer GPUs into a fault-tolerant mesh. Its positioning emphasizes lower cost, built-in failover and load balancing, an OpenAI-compatible API, and simpler Docker-based deployment without YAML or Kubernetes; this differentiates it from hyperscale and specialized GPU clouds such as CoreWeave and Lambda, as well as GPU marketplaces and decentralized networks such as Vast.ai, RunPod, io.net, and Akash.
OpenRelay is best suited to AI developers and engineering teams—particularly cost-sensitive startups and product teams deploying large-language-model, image/video-generation, or real-time voice-AI workloads—that want hosted inference or GPU compute without managing Kubernetes. Its provider-side market also includes consumer GPU owners and operators who want to monetize idle hardware through the network.
At a Glance
Problem
OpenRelay addresses the high cost and fragility of running AI inference and other GPU workloads on conventional cloud infrastructure. It positions itself for teams that have outgrown hyperscaler bills, claiming savings of up to 90% versus AWS, Azure, and GCP; its listed prices range from roughly $0.29 per hour for an RTX 4090 to $2.60 per hour for an H100. The core pain is economic—expensive, scarce accelerator capacity—as well as operational: a single provider or node failure can interrupt production inference.
The main use case is production model serving for teams that need affordable, available GPU capacity without building their own cluster. A related demonstrated use case is replacing expensive hosted build infrastructure: OpenRelay says one team moved Vercel builds to self-hosted GitHub runners and reduced its monthly bill by $4,000 while improving build speed.
Product / Service
OpenRelay is a distributed overlay network that connects consumer GPUs into a fault-tolerant mesh. Requests are automatically load-balanced across available nodes and rerouted around failures, while customers can either deploy dedicated GPU virtual machines with SSH access and persistent storage or call hosted open models through an OpenAI-compatible inference endpoint. Hosted inference is metered by input and output tokens, while GPU VMs are billed hourly, with no contract or minimum commitment.
The delivery model is two-sided: developers obtain on-demand inference and compute through a managed platform, while GPU owners can contribute idle hardware and earn revenue; OpenRelay handles scheduling, isolation, billing, and customer acquisition. This gives customers a simpler deployment experience, faster access to capacity, and resilience that a single machine or datacenter cannot provide, while allowing the network to aggregate lower-cost hardware globally.
Market
OpenRelay competes in distributed GPU cloud, hosted AI inference, and AI compute infrastructure. Its named comparison set includes RunPod, Vast.ai, Lambda, AWS, and GCP. The company differentiates itself through a hardware-agnostic, distributed architecture, automatic failover, OpenAI-compatible APIs, and materially lower advertised prices than hyperscalers; RunPod and Vast.ai appear to be its closest marketplace-style GPU-cloud comparators, while AWS, GCP, and Lambda represent more conventional managed or specialized alternatives.
OpenRelay is an early-stage commercial company rather than an established infrastructure provider. It was founded in 2026, is part of Y Combinator’s Summer 2026 batch, and is listed by YC as an active two-person Seattle company. Its production environment and live billing were launched in June 2026, and its public materials include a $4,000-per-month customer case study. The available evidence does not disclose revenue, customer counts, or recurring usage, so it is best characterized as launched and early-commercial, with traction signals but no publicly documented scale metrics.
Founders & Leadership
Funding History
Y Combinator
Recent News
OpenRelay describes itself as a distributed GPU cloud for production inference, with hardware-agnostic, token SLA-backed inference advertised as up to 90% cheaper than AWS, Azure, and GCP. The profile also highlights automatic failover and API-driven cluster endpoints.
Y Combinator’s company directory lists OpenRelay as an active Summer 2026 company founded by Prashant Patel and Jaden Wang in Seattle. The listing identifies the company’s focus as distributed, hardware-agnostic AI inference.
OpenRelay published a product page for its Batch Inference API for open models. The page positions OpenRelay as a distributed GPU cloud for production workloads and lists batch inference among its products.
OpenRelay’s inference catalog supports using an existing OpenAI SDK by changing the model, with a common request shape across catalog models. The page highlights support for streaming and tool-related workflows.
OpenRelay launched a documentation site generated from its control-plane OpenAPI specification, covering 89 customer-facing operations in 16 sections. It also added hand-written guides for authentication, errors, pagination, and webhooks.
OpenRelay updated its availability API to report each GPU model’s allocation granularity and to reject unsatisfiable GPU counts immediately with valid options. The dashboard deployment wizard now offers only counts that can be placed.
OpenRelay announced that it is backed by Y Combinator and is building a distributed GPU overlay network for fault-tolerant AI compute. The announcement says the network aims to provide GPUs at 50–80% lower cost than major clouds and that YC’s backing will accelerate the company’s mission.
An OpenRelay case study says a team replaced Vercel’s build infrastructure with OpenRelay self-hosted GitHub runners, saving $4,000 per month in build minutes while achieving faster builds and zero configuration overhead.
OpenRelay published a hardware guide covering NVIDIA H200, GB200 NVL72, B200, and AMD MI300X GPUs, including specifications, pricing, availability, and when each is appropriate for AI workloads.
OpenRelay published an industry article arguing that reusing existing consumer GPUs for AI inference can be greener than building new data centers. It presents the environmental rationale for distributed GPU networks.
Active Roles
0No active roles right now.
Get notified when they postBusiness Model
OpenRelay charges customers per token, per request, or by the hour for hosted inference and dedicated GPU virtual machines. It also takes a percentage of inference revenue generated by third-party compute providers, while providers receive a utilization-based revenue share.