COMPARE THE OPERATING MODEL
Do not compare boxes. Compare who owns the workflow.
IronFlow, n8n and Dify can all connect steps, and all three now have code-authoring or headless paths. The useful distinction is what gets deployed, what executes the workflow and which platform services remain in the runtime boundary.
“Workflow” describes different products.
- IronFlowexecution runtime
- n8nvisual automation
- DifyAI app platform
START WITH THE JOB
Three centers of gravity.
There is no universal winner. Pick the surface that matches the people building the flow and the system they must operate.
IRONFLOW
Workflow as software.
Lua-defined graphs, a Rust runtime and built-in primitives for teams engineering automation, ETL and RAG.
Inspect the engine manual →IRONFLOW / N8N
Direct flow or automation platform?
Compare direct Lua execution with n8n’s canvas, TypeScript Workflow SDK, connector strategy and full application runtime.
Open comparison →IRONFLOW / DIFY
Primary runtime or AI platform?
Compare direct Lua execution with Dify Studio, portable DSL files, CLI automation and its multi-service application runtime.
Open comparison →THE DECISION FRAME
A smaller matrix. A better question.
Feature inventories age quickly. These operating-model differences are the durable part of the decision.
| Decision | IronFlow | n8n / Dify |
|---|---|---|
| Product center | Code-defined workflow runtime | Visual automation or AI application platform |
| Code authoring | Native Lua files, code review and version control | n8n TypeScript/JSON; Dify YAML DSL and CLI |
| Running artifact | Run the Lua source directly with a standalone binary | Standard n8n/Dify paths import, store or publish the workflow in a platform instance |
| RAG control | Explicit primitives across ingestion and retrieval | Opinionated AI nodes or knowledge workflows |
| Execution placement | CLI process inside CI, batch and externally scheduled jobs | Long-running visual application or workflow-platform services |
| Script boundary | Restricted Lua; external effects through explicit Rust nodes | Platform-specific node, code and extension controls |
| Best fit | Engineered automation, ETL and bespoke RAG | Faster visual assembly and managed product surfaces |
RUN IT WHERE THE WORK RUNS
A workflow step, not another control plane.
IronFlow validates and executes source-controlled Lua as an ordinary process inside the infrastructure you already operate. No imported workflow record or editor service sits between the file and execution.
01 / CI CONTRACT
Validate. Run. Fail the job.
ironflow validate flow.lua
ironflow run flow.lua --context "$JOB_CONTEXT"Static validation happens without executing the graph. An unsuccessful run returns a failing process status, so IronFlow can gate the job.
02 / EXECUTION PLACEMENT
Bring the binary to the data.
- GitHub Actions
- Azure Pipelines
- Cron / CronJobs
- Isolated batch workers
- Job containers
- Databricks job compute
Timed execution comes from an external scheduler in v1.15.0. Databricks placement requires job compute or a container policy that permits a custom binary.
NO FALSE PARITY
IronFlow does not have a visual builder.
That is a trade-off, not a missing footnote. IronFlow is strongest when source control, explicit graph semantics and deployment ownership are deliberate requirements.
101 built-in nodes in documented release v1.15.0, direct Lua-file execution, CLI/API/webhook entry points and an MIT-licensed Rust runtime.
GO DEEPER
Choose the comparison that matches your shortlist.
Each page includes an honest case for the other product, product boundaries and links to primary sources.