n8n vs Dify vs Mastra: choosing a self-hosted AI automation stack

Why teams self-host, and what it actually costs

The case for self-hosting AI automation is real. Your data stays on your infrastructure, which matters when workflows touch customer records, financials or anything a regulator cares about. Compliance teams sign off faster when nothing leaves the network. And at meaningful volume, running your own instance can beat paying per execution forever. You also keep control of your roadmap: no surprise deprecations, no feature you depend on moving behind a higher tier.

But be honest about the trade. When you self-host, you become the vendor. Upgrades are your job, and skipping them means running known vulnerabilities in a system that holds API keys to half your business. Scaling is your job: queue workers, database tuning, webhook throughput. Security hardening, backups, disaster recovery, all yours. The license may be free. The operation is not. Teams that thrive with self-hosted stacks treat them as production software with an owner, not as a side project someone installed once.

With that framing, here are the three self-hosted stacks we see most often in serious deployments, and where each one actually fits. If you are weighing managed platforms instead, our companion comparison of hosted AI automation platforms covers that side of the decision.

n8n: visual workflow automation you can run anywhere

n8n is a source-available workflow automation platform. You can run it on your own infrastructure or use their cloud, and the core experience is a visual editor where you wire triggers, transformations and actions across a very large library of prebuilt integration nodes. Its AI story is built on top of that engine: agent nodes, LLM calls and vector store steps sit inside ordinary workflows, alongside the boring glue work that real automation always needs.

Where it shines: ops-heavy automation on your own infra. If your problem is connecting systems, syncing data, routing tickets and adding AI judgment at specific steps, n8n gets you there fast. The node library saves weeks of integration code, and the visual canvas keeps workflows legible to people who do not write code. The self-hosting path is well documented and widely used.

Where it is not the right fit: n8n is a workflow engine first and an agent platform second. Deeply dynamic agent behaviour, complex memory, or agents embedded inside your own product push against the grain of a node-based canvas. And note the license: n8n is source-available under its own fair-code terms, not OSI open source, which matters to some legal teams.

Dify: an open-source platform for LLM apps

Dify is an open-source LLM application platform. Where n8n starts from workflows, Dify starts from the AI app itself: RAG pipelines with document ingestion and retrieval, visual agent builders, prompt orchestration, and model routing so you can swap providers without rebuilding. You self-host it, point it at your model endpoints, and ship chat-style applications with a management layer around them.

Where it shines: product and platform teams building conversational AI on their own infrastructure. If the deliverable is a knowledge assistant, an internal copilot or a customer-facing chat product grounded in your documents, Dify covers the RAG plumbing, the app scaffolding and the operational dashboard in one stack. Model routing is genuinely useful for teams that want to stay provider-neutral.

Where it is not the right fit: Dify is not a general automation tool. Its integration surface is narrow compared to n8n, so cross-system business process automation is awkward. And if your engineers want full programmatic control over agent behaviour rather than a platform's abstractions, they will eventually fight the builder instead of using it.

Mastra: agents as TypeScript code

Mastra is an open-source TypeScript framework for building AI agents. There is no canvas. You define agents, tools, workflows, memory and evals in code, run them wherever you run Node, and version everything in git like the rest of your codebase. It is a framework in the honest sense: a set of primitives for engineers, not a platform for operators.

Where it shines: engineering teams building agents into their own products. If AI behaviour is part of your application, code-first wins. You get real code review, real tests, typed interfaces, and eval harnesses that run in CI. Workflows are durable and debuggable with the tools your team already uses. For TypeScript shops, it fits the existing stack instead of adding a parallel one.

Where it is not the right fit: everyone who does not write TypeScript for a living. There is no visual builder for ops teams, no node library for quick integrations, and every connector is code you write and maintain. Choosing Mastra means committing engineering capacity for the life of the system, not just the build.

How to choose between them

These three tools overlap less than their marketing suggests. Match the tool to who will own it and what they are building.

• Choose n8n if an ops or platform team wants visual, cross-system workflow automation on infrastructure you control, with AI steps embedded where they earn their place.
• Choose Dify if a product team is shipping LLM apps, especially RAG-backed assistants and chat products, and wants the pipeline, app layer and model routing handled in one self-hosted stack.
• Choose Mastra if TypeScript engineers are building agents into your own product and want them expressed as tested, versioned, evaluated code rather than platform configuration.
• Reconsider self-hosting entirely if nobody on the team can own upgrades, scaling and security as a standing responsibility. A managed platform run well beats an open one run badly.

What self-hosting does not solve

Here is the part the comparison charts skip. None of these stacks answers the questions that decide whether AI automation survives contact with production. Who approves the action that moves money or emails a customer? What happens when a workflow half-completes at 2am? Who can see, after the fact, exactly what an agent did and why? Self-hosting gives you control of the infrastructure. It gives you nothing on approvals, observability or accountability. That operations layer has to be designed, whichever engine you run underneath.

This is the layer we build at Kraftbyte. Our own product, Brahmalabs, and that disclosure matters, exists precisely because we kept rebuilding it for clients: durable execution that survives failures, human approval gates in front of consequential actions, and observability that shows what every agent did. It complements a self-hosted engine rather than replacing it. However you host, the discipline is the same. Pick the stack your team can genuinely own, then design the accountability around it before the first workflow touches anything real. That second part is where automation projects live or die.