Strengths:
- 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.
Weaknesses:
- More setup and configuration compared to serverless platforms. You manage the infrastructure.
- Higher minimum costs. GPU droplets start around 50ドル/month even when idle.
- No built-in agent-specific abstractions like scheduling or retry logic.
Pricing: CPU droplets from 4ドル/month. GPU droplets from ~50ドル/month. Managed Kubernetes from 12ドル/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.
5. Nebula -- Best for Zero-Config Managed Agents
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.
Strengths:
- 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.
Weaknesses:
- 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.
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.
How to Choose
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.