Fly.io vs Railway: Quick Verdict
Both Fly.io and Railway are developer-friendly platforms for deploying web apps without managing Kubernetes. For AI applications specifically, the right choice depends on one core question: do you need GPU access or not?
Choose Fly.io if you need: edge deployments close to users, GPU machines for inference, fine-grained control over machine size and regions, or high-traffic applications that require horizontal scaling.
Choose Railway if you need: the fastest time from code to deployed app, simpler pricing, built-in databases, and you're running CPU-only AI workloads (API wrappers, orchestration layers, embeddings via external APIs).
Platform Overview
| | Fly.io | Railway | |--|--------|---------| | Launch year | 2020 | 2021 | | Model | Container-based, global edge | Git-connected PaaS | | GPU support | ✅ Yes (A10, A100) | ❌ No (CPU only) | | Free tier | Limited free allowance | $5/month hobby plan | | Pricing model | Pay-per-second compute | Monthly subscription + usage | | Cold starts | Fast (persistent VMs) | Possible on free tier | | Regions | 30+ globally | ~10 regions | | Database | Managed Postgres (Fly Postgres) | Postgres, MySQL, Redis built-in |
Deployment Experience
Railway
Railway is the fastest path from a GitHub repo to a live URL. Connect your repo, Railway detects the runtime, and you're deployed in minutes. No Dockerfile required for most stacks. The UI is clean, the CLI is simple, and environment variable management is straightforward.
For AI apps that are essentially Python/Node API servers wrapping OpenAI or Anthropic APIs — the majority of AI MVPs — Railway is genuinely hard to beat on developer experience. You can deploy a FastAPI or Express backend serving LLM responses in under ten minutes.
Where Railway falls short: No GPU support means you can't run local inference (Llama, Mistral, Whisper). You're entirely dependent on external AI APIs. For many products that's fine; for others it's a dealbreaker.
Fly.io
Fly.io requires a bit more configuration — you'll write a fly.toml and usually a Dockerfile — but the payoff is significantly more flexibility. You can choose machine size down to the vCPU/RAM level, deploy to specific regions for latency optimization, and access GPU machines for inference.
The Fly CLI is powerful and the documentation is thorough. It's not as "magical" as Railway but that's a feature when you need to debug production issues.
Fly.io's GPU machines are a genuine differentiator for AI teams. Running Whisper for transcription, embedding models locally, or self-hosted Llama inference becomes viable without standing up a full cloud instance and managing it yourself.
GPU and Inference Workloads
This is where the comparison becomes decisive for AI teams:
| Workload | Fly.io | Railway | |----------|--------|---------| | OpenAI/Anthropic API wrapper | ✅ Both work | ✅ Both work | | Embeddings via external API | ✅ Both work | ✅ Both work | | Local inference (Llama, Mistral) | ✅ GPU machines available | ❌ Not supported | | Whisper transcription (local) | ✅ GPU machines available | ❌ Not supported | | Fine-tuned model serving | ✅ GPU machines available | ❌ Not supported | | Vector DB (Qdrant, Weaviate) | ✅ Deploy as app | ✅ Deploy as app |
If your AI app calls external model APIs (the pattern for most early-stage products), Railway handles it fine. If you're self-hosting models for cost, latency, or privacy reasons, Fly.io is the only viable choice of the two.
Pricing Comparison
Railway
- Hobby: $5/month (includes $5 usage credit)
- Pro: $20/month + usage
- Usage: ~$0.000463/vCPU-minute, ~$0.000231/GB RAM-minute
- Practical cost for a small AI API server: $10–30/month
Fly.io
- No base subscription — pure usage-based
- Shared CPU machines: from $1.94/month
- Dedicated CPU: from ~$5/month per machine
- GPU machines: A10 at ~$1.50/hour, A100 at ~$2.50/hour
- Practical cost for a small AI API server: $5–20/month
Fly.io tends to be cheaper for CPU workloads at scale because there's no platform fee. Railway is cheaper for simple, low-traffic apps where predictable billing matters.
Cold Starts and Reliability
Fly.io keeps VMs warm by default. Apps don't spin down unless you explicitly enable scale-to-zero. For AI inference where cold start latency is painful, this matters.
Railway can scale services to zero on lower-tier plans. A sleeping service adds 2–5 seconds to the first request. For non-interactive workloads (batch jobs, webhooks) this is fine. For user-facing AI responses, it's noticeable.
When to Choose Each for AI Apps
Choose Fly.io when:
- You need GPU access for local model inference
- Your app serves global users and latency matters by region
- You want persistent VMs without scale-to-zero behavior
- You're deploying LLM orchestration infrastructure that requires fine-grained resource control
Choose Railway when:
- You're building an MVP and speed of deployment matters most
- Your AI app wraps external model APIs (OpenAI, Anthropic, Gemini)
- You want managed databases co-located with your app in one platform
- Your team prefers a simpler mental model over maximum control
For most AI MVPs, Railway gets you to production faster. As requirements grow — more traffic, need for local inference, multi-region — Fly.io's flexibility pays off.
The Hybrid Approach
Many production AI teams use both. Railway for stateless API layers and background workers that call external AI APIs; Fly.io for GPU-intensive inference services that require dedicated hardware. This gives you Railway's developer experience where you don't need GPUs and Fly.io's power where you do.
Related: Why Startups Fail at AI · What is LLM Orchestration? · Build vs Buy Your AI MVP
Need help deciding on your deployment architecture? Talk to our engineering team — we'll map your requirements to the right infrastructure setup in 15 minutes.
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