dokploy vs pyspur
dokploy and pyspur serve very different purposes despite both being open-source and written in TypeScript. dokploy is an infrastructure and deployment platform positioned as a self-hosted alternative to services like Vercel, Netlify, and Heroku. Its primary focus is on deploying, managing, and operating applications, especially web services and APIs, with an emphasis on DevOps workflows and production readiness. pyspur, by contrast, is a developer productivity and experimentation tool focused on building and iterating on agentic workflows. It provides a visual playground for designing, testing, and refining AI agents and multi-agent systems. While dokploy targets application deployment and lifecycle management, pyspur targets rapid iteration, debugging, and visualization of agent behavior, making the two tools complementary rather than directly competing. The key differences lie in their target users and problem domains: dokploy is geared toward backend engineers and DevOps teams running production workloads, whereas pyspur is aimed at AI engineers, researchers, and developers experimenting with agent-based systems and workflows.
dokploy
open_sourceOpen Source Alternative to Vercel, Netlify and Heroku.
✅ Advantages
- • Purpose-built for application deployment and infrastructure management
- • Stronger fit for production environments and long-running services
- • Larger GitHub community and higher adoption signals
- • Comparable to established PaaS offerings like Vercel and Heroku
- • Useful for a wide range of web frameworks and backend stacks
⚠️ Drawbacks
- • Not designed for AI agent development or workflow visualization
- • Can be more complex to set up and operate than a pure development tool
- • Requires infrastructure and DevOps knowledge to use effectively
- • Less focus on rapid experimentation and iteration
- • License clarity is less explicit compared to pyspur
pyspur
open_sourceA visual playground for agentic workflows: Iterate over your agents 10x faster
✅ Advantages
- • Specialized for designing and iterating on agentic workflows
- • Visual interface improves understanding and debugging of agent behavior
- • Faster experimentation cycles for AI and LLM-based systems
- • Clear Apache-2.0 license for enterprise-friendly usage
- • Strong extensibility for custom agents and workflows
⚠️ Drawbacks
- • Not suitable for deploying or hosting production applications
- • Smaller community compared to dokploy
- • Limited scope outside of agentic and AI-focused use cases
- • Less relevant for traditional web or backend developers
- • May require familiarity with AI agent concepts to be effective
Feature Comparison
| Category | dokploy | pyspur |
|---|---|---|
| Ease of Use | 4/5 Familiar workflows for developers used to PaaS platforms | 3/5 Visual but requires understanding of agent concepts |
| Features | 3/5 Focused on deployment and infrastructure features | 4/5 Rich features for agent design, testing, and iteration |
| Performance | 4/5 Well-suited for running production workloads | 4/5 Responsive for interactive experimentation workflows |
| Documentation | 3/5 Functional but still maturing | 4/5 Clearer guidance for intended AI-focused use cases |
| Community | 4/5 Larger GitHub presence and broader user base | 3/5 Smaller but more specialized community |
| Extensibility | 3/5 Extensible within deployment and infrastructure boundaries | 4/5 Designed for extending and composing custom agents |
💰 Pricing Comparison
Both dokploy and pyspur are open-source and free to use from a licensing perspective. dokploy may incur indirect costs related to hosting, infrastructure, and operations when self-hosted, similar to running your own PaaS. pyspur typically has lower operational costs, as it is primarily a development and experimentation tool, though costs may arise from underlying AI models or compute resources used during agent execution.
📚 Learning Curve
dokploy has a moderate learning curve, especially for users without DevOps or infrastructure experience. pyspur’s learning curve is tied more to understanding agentic workflows and AI concepts; once those are familiar, the visual interface can accelerate learning and iteration.
👥 Community & Support
dokploy benefits from a larger and more general open-source community, which can translate to more discussions, examples, and third-party resources. pyspur has a smaller but more focused community, with discussions centered specifically on agent workflows and AI development.
Choose dokploy if...
dokploy is best for teams and developers who want a self-hosted alternative to managed PaaS platforms and need to deploy, scale, and operate production applications.
Choose pyspur if...
pyspur is best for AI engineers, researchers, and developers who need to rapidly design, test, and iterate on agentic workflows in a visual and interactive environment.
🏆 Our Verdict
Choose dokploy if your primary need is deploying and managing applications in production with a self-hosted PaaS-style solution. Choose pyspur if your focus is on experimenting with and refining agent-based AI workflows. The right choice depends less on feature overlap and more on whether your core problem is infrastructure or agent development.