Running your own AI models has historically required either expensive reserved GPU instances or complex orchestration with spot instances. Railway's GPU offering simplifies this dramatically. You deploy a container with your model, attach a GPU, and Railway handles scaling, networking, and billing. The per-second billing means you only pay for actual inference time, not idle hours.
The supported hardware covers the sweet spot for inference workloads. NVIDIA T4 instances are suitable for smaller models and embedding generation, while A10G instances handle larger language models and image generation. Railway's autoscaling can scale instances to zero when there is no traffic, which means your inference endpoint costs nothing during off-hours. When a request arrives, cold start times range from 15-45 seconds depending on model size, which is acceptable for non-real-time use cases.
For startups that want to self-host models for cost or privacy reasons, this removes the infrastructure barrier. You can deploy an open-source model like Llama or Mistral on Railway, point your application at it, and pay only for the inference you use. Compared to API-based providers, self-hosting becomes cost-effective at around 10,000 requests per day for a 7B parameter model. Below that threshold, API providers like Cloudflare Workers AI or Together AI are still cheaper.
Consider Railway GPU instances if you need self-hosted AI inference but want to avoid the complexity and cost of managing cloud GPU infrastructure.
