Deployment Platforms
Deployment platforms handle the infrastructure complexity of running AI agents in production — from GPU provisioning for model inference to auto-scaling for variable workloads. These platforms let you focus on your agent logic while they manage containers, networking, and scaling.
Modal
A serverless cloud platform purpose-built for AI/ML workloads that provides on-demand GPU access, instant cold starts, and a Python-native developer experience. Modal lets you define infrastructure as code using Python decorators, eliminating the need for Dockerfiles, Kubernetes configs, or cloud console clicks.
Key Features
- GPU access (A10G, A100, H100) with per-second billing and instant cold starts
- Python-native infrastructure — define compute, storage, and scheduling with decorators
- Built-in cron scheduling and webhook endpoints for production agent workflows
- Automatic container building with dependency caching for fast iteration
Integrations
Railway
An instant deployment platform for full-stack applications and backend services that abstracts away DevOps complexity. Railway auto-detects your framework, provisions databases, and deploys from GitHub pushes, making it ideal for teams that want a Heroku-like experience with modern infrastructure.
Key Features
- One-click deployments from GitHub with automatic framework detection
- Managed databases (Postgres, Redis, MySQL, MongoDB) with one-click provisioning
- Private networking between services with automatic service discovery
- Preview environments for every pull request with isolated databases
Integrations
Fly.io
A global application platform that runs full-stack apps and databases close to users worldwide using lightweight micro-VMs. Fly.io excels at latency-sensitive applications by deploying your code to data centers in 30+ regions, with built-in load balancing and auto-scaling.
Key Features
- Global deployment across 30+ regions with automatic geo-routing
- Lightweight Firecracker micro-VMs for fast boot times and efficient resource use
- Built-in Postgres, Redis, and LiteFS for globally distributed data
- GPU machines available for running model inference at the edge
Integrations
Comparison
Each deployment platform has different strengths depending on your workload type and operational preferences.
| Feature | Modal | Railway | Fly.io |
|---|---|---|---|
| Primary Strength | Serverless AI/ML + GPUs | Full-stack app deployment | Global edge deployment |
| GPU Support | Yes (A10G, A100, H100) | No | Yes (A100, L40S) |
| Managed Databases | Volumes + Dicts | Yes (Postgres, Redis, MySQL) | Yes (Postgres, Redis) |
| Deploy Method | Python decorators (CLI) | Git push / Docker | CLI / Dockerfile |
| Global Regions | US (AWS) | US + EU | 30+ worldwide |
| Best For | GPU workloads, model inference, batch jobs | Full-stack apps with databases, fast setup | Low-latency global apps, edge computing |