You Built the Agent. Now Where Does It Live?

Quick Comparison

1. Modal -- Best for GPU-Intensive AI Agents

2. Trigger.dev -- Best for Serverless Agent Background Jobs

3. Railway -- Best for Quick Docker-Based Agent Deploys

4. DigitalOcean Gradient -- Best for Enterprise Agent Infrastructure

5. Nebula -- Best for Zero-Config Managed Agents

How to Choose TL;DR: Modal for GPU-heavy workloads. Trigger.dev for serverless background jobs. Railway for simple Docker deploys. DigitalOcean Gradient for enterprise GPU infrastructure. Nebula for zero-config managed agents with built-in scheduling. Pick based on whether you need GPUs, how much infra you want to manage, and what language you work in. Every AI agent tutorial ends the same way: a working prototype running on localhost. Then reality hits. Your agent needs to run on a schedule, persist state between runs, connect to external APIs, and recover from failures -- all without you babysitting a terminal. The hosting landscape for AI agents looks nothing like traditional web hosting. Agents need persistent execution, cron-like scheduling, API connectivity, memory management, and observability. A static site host will not cut it. I evaluated five platforms across pricing, GPU support, scheduling, framework compatibility, auto-scaling, setup time, and developer experience. Here is how they stack up. Modal is a serverless compute platform built for Python ML workloads. If your agent runs custom models, fine-tunes embeddings, or needs GPU inference, Modal is the go-to. Pricing: Free tier with $30/month credits. CPU starts at ~$0.192/vCPU-hour. GPU pricing varies: A10G ~$1.10/hour, A100 ~$3.00/hour. Best for: Data scientists and ML engineers building agents that run custom models, process large datasets, or need GPU compute for inference. Trigger.dev positions itself as the infrastructure for long-running background jobs. It is increasingly popular for AI agent workloads that need retries, scheduling, and observability without managing queues. Pricing: Free tier includes 50,000 runs/month. Paid plans start at $25/month for higher concurrency and longer timeouts. Best for: TypeScript developers building agents that run on schedules, process webhooks, or need reliable background execution with built-in retry logic. Railway is the "just deploy it" platform. If you want to go from a GitHub repo to a running agent in under 15 minutes, Railway makes it painless. Pricing: Free trial with $5 credits. Usage-based after that: ~$0.000231/vCPU-minute, ~$0.000231/MB-minute for memory. Typical small agent runs $5-15/month. Best for: Full-stack developers who want a simple PaaS experience. Great for always-on agents that need a database, persistent storage, and minimal ops overhead. DigitalOcean Gradient is DO's AI-focused platform, offering GPU droplets and managed Kubernetes for teams that need enterprise-grade infrastructure without the complexity of AWS or GCP. Pricing: CPU droplets from $4/month. GPU droplets from ~$50/month. Managed Kubernetes from $12/month per node. Best for: Engineering teams deploying multi-agent systems that need dedicated GPU resources, Kubernetes orchestration, and enterprise support. Good for teams already in the DigitalOcean ecosystem. Nebula takes a fundamentally different approach. Instead of giving you infrastructure to deploy agents onto, it provides a managed platform where agents run out of the box with scheduling, integrations, and memory built in. Pricing: Free tier available. Usage-based scaling beyond that. Best for: Developers who want to build workflow agents, automation pipelines, or multi-step AI tasks without managing infrastructure. Ideal when the bottleneck is integration and orchestration, not raw compute. The right platform depends on three questions: 1. Do you need GPUs?

If yes, your options are Modal (serverless GPU) or DigitalOcean Gradient (dedicated GPU). Most agents calling OpenAI or Anthropic APIs do not need local GPU -- the LLM provider handles inference. 2. How much infrastructure do you want to manage?From most to least ops overhead: DigitalOcean Gradient > Railway > Modal > Trigger.dev > Nebula. If you want zero infrastructure management, Nebula or Trigger.dev are your best bets. 3. What is your language ecosystem?Python-heavy teams should look at Modal first. TypeScript teams fit well with Trigger.dev. Polyglot teams using Docker can go with Railway or DigitalOcean. Nebula works across languages via its built-in agent runtime. The pattern I see most often: teams start with Railway or Nebula for prototyping, then graduate to Modal or DigitalOcean Gradient when they need GPU compute or enterprise scale. There is no single "best" platform -- just the right fit for your current stage. Building with one of these platforms? Drop a comment with your setup -- I am always curious what hosting stacks developers are running their agents on. Templates let you quickly answer FAQs or store snippets for re-use. as well , this person and/or - Per-second billing with zero idle costs. You pay only when code executes.- Access to A100 and H100 GPUs without managing CUDA drivers or Kubernetes.- Python-native developer experience using decorators. Write @app.function(gpu="A100") and deploy with modal deploy.- Sub-second cold starts for most workloads.- Built-in cron scheduling via @app.function(schedule=modal.Cron("0 9 * * *")). - Python only. No TypeScript or JavaScript support.- Volumes are ephemeral -- persistent state requires external storage or their Volume primitives.- Steeper learning curve for developers unfamiliar with serverless patterns. - Built-in cron scheduling, retries with exponential backoff, and concurrency controls.- Runs up to 300 seconds per task (or longer on paid plans). No timeout anxiety.- TypeScript-first with strong type safety. Python support via HTTP triggers.- Integrated dashboard showing every run, its logs, duration, and status.- Open-source core -- self-host if you want full control. - No GPU support. Agents calling external LLM APIs work fine, but local model inference is off the table.- TypeScript-focused ecosystem. Python developers may feel like second-class citizens.- Newer platform with a smaller community compared to Modal or Railway. - One-click deploy from GitHub. Push code, Railway builds and ships automatically.- Persistent volumes up to 50GB for agent state, SQLite databases, or file artifacts.- Built-in managed databases: Postgres, Redis, MySQL. No separate provisioning.- Environment variable management with team sharing.- Supports any language and runtime via Docker or Nixpacks auto-detection. - No GPU support. You are limited to CPU-bound workloads.- Scaling is manual -- you configure replicas and resources yourself.- No built-in cron or scheduling. You need to bring your own scheduler (or use a cron service alongside). - GPU droplets with NVIDIA H100 access for local model inference.- Managed Kubernetes (DOKS) for orchestrating multi-agent systems at scale.- Predictable pricing -- flat monthly rates instead of per-second billing surprises.- Strong compliance and security features for regulated industries.- App Platform for simpler deploys without Kubernetes expertise. - More setup and configuration compared to serverless platforms. You manage the infrastructure.- Higher minimum costs. GPU droplets start around $50/month even when idle.- No built-in agent-specific abstractions like scheduling or retry logic. - Zero-setup deployment. Go from idea to running agent in under 5 minutes.- Built-in triggers: cron schedules, email triggers, webhook triggers -- no external scheduler needed.- 1,000+ app integrations (Gmail, Slack, GitHub, Notion, and more) available without writing API connectors.- Persistent agent memory and state management across runs.- Multi-agent delegation: agents can spawn and coordinate sub-agents. - No GPU compute. Agents call external LLM APIs (OpenAI, Anthropic, etc.) rather than running models locally.- Less customization for low-level ML workloads or custom model serving.- No self-hosting option. You are on the managed platform.