n8n vs Kestra: Which Self-Hosted Engine to Run

Updated 2026-10-23 · 1182 words

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Verdict: these two sit in genuinely different categories more than they compete head-on. If you're automating business workflows across SaaS apps, especially ones that need an AI-agent step, n8n's visual canvas and AI Agent node are the better foundation. If you're orchestrating data pipelines, scheduled or event-driven infrastructure jobs, or backfills at scale, Kestra's declarative YAML flows and 1,800-plus plugin library are built specifically for that job in a way n8n isn't. We self-hosted both on the same VPS to see where the line between them actually falls.

What n8n and Kestra actually are

n8n is a node-based workflow automation platform: you build on a visual canvas, dragging in triggers, app-integration nodes, logic branches, and, since its AI Agent node shipped, LLM-powered reasoning steps. It's designed around connecting everyday business apps, CRMs, spreadsheets, messaging tools, support platforms, with execution-based pricing on its cloud tier and a free, Sustainable Use License self-hosted core.

Kestra is an open-source orchestration platform built around YAML-defined flows rather than a drag-first canvas, though it does ship a visual topology view for inspecting and editing those flows. It's designed for event-driven and scheduled orchestration at a broader scope than SaaS automation: data pipelines, infrastructure jobs, backfills, and triggers that react to events across cloud and data systems, backed by a plugin library of more than 1,800 integrations. Its core engine is Apache 2.0 licensed with no enterprise feature carve-out in the public repository.

Comparison table

Building the same thing: canvas-first vs. YAML-first

We built a straightforward daily job in both: pull new rows from a database, transform them, and push a summary to Slack on a schedule. In n8n, this was a short chain of nodes, a schedule trigger, a database node, a Function node for the transform, and a Slack node, built entirely by dragging and configuring on the canvas. Testing and adjusting the flow meant clicking through the same visual nodes.

In Kestra, the same job was a YAML file defining the schedule trigger, a database task, a transform task, and a Slack notification task, editable in Kestra's visual topology view or directly as code. Because it's YAML, the flow lived naturally in version control and could go through a normal code-review process the way application code does, something n8n's canvas-based workflows support through its Git integration but in a less native, more bolted-on way. Where Kestra pulled further ahead was retries and backfills: re-running the job for a range of past dates with its built-in backfill feature took one command, while the equivalent in n8n meant manually re-triggering the workflow for each date or building separate logic to loop through a date range.

Integrations: breadth vs. fit

Kestra's plugin count is more than four times n8n's, but that comparison is misleading without context. Kestra's 1,800-plus plugins lean heavily toward cloud infrastructure, data warehouses, message queues, and developer tooling, AWS, GCP, Kafka, dbt, Snowflake, categories n8n doesn't meaningfully compete in at all. n8n's 400-plus nodes are weighted toward the everyday SaaS tools a small business or solopreneur actually touches day to day, CRMs, spreadsheets, support platforms, email, where Kestra's library is comparatively thin.

If your automation lives in the data-and-infrastructure world, Kestra's library is the better fit by a wide margin. If it lives in the SaaS-business-tool world, n8n's narrower but more targeted node set gets you connected faster, with less custom plugin work.

AI tooling: a real gap for Kestra

This is the clearest place n8n pulls ahead for agent-style use cases. Its dedicated AI Agent and LangChain nodes let a workflow reason through a multi-step task, call tools, and loop toward an answer, with native MCP (Model Context Protocol) client and trigger support for connecting to external AI tooling directly. Kestra can call an LLM API as a task inside a flow just like it can call any other API, but it has no equivalent built-in concept of an autonomous reasoning agent; you're scripting a call to a model, not orchestrating agent behavior. If an AI-agent step, triaging a request, researching a topic, drafting a context-aware response, is central to what you're building, n8n is the materially better starting point.

Pricing and licensing

Both are free to self-host at their core, with your real cost being the server you run them on. n8n's Sustainable Use License permits normal internal use but restricts reselling n8n itself as a competing hosted service; Kestra's Apache 2.0 license is more permissive still, with the public repository carrying no feature gate at all between the free and paid tiers. Kestra's Enterprise Edition, SSO, RBAC, audit logs, hybrid deployment, is a quote-based annual subscription layered on top of the same free engine, and a Kestra Cloud managed option exists in early access with usage-based pricing. n8n's path to avoiding self-hosting is its Cloud plans, starting around $24 a month for a capped tier of active workflows and executions.

When NOT to use n8n

Skip n8n if your actual job is data-pipeline orchestration, scheduled or event-driven jobs across cloud infrastructure, backfills across date ranges at scale. Our own best open source workflow orchestration tools roundup makes the same point about n8n relative to a true data orchestrator like Airflow, Dagster, or Temporal; Kestra sits in that same broader category, and n8n's canvas isn't built around backfills and dependency-heavy scheduling the way those tools, Kestra included, are.

When NOT to use Kestra

Skip Kestra if your workflows are everyday SaaS business automation, especially anything that needs an AI-agent step. You'll be writing more custom plugin or task code to reach the business apps n8n already covers as a drag-and-drop node, and you'll have no native agent-reasoning capability to build on if that becomes part of the job later.

Which should you pick?

Choose Kestra if you're orchestrating data pipelines, infrastructure jobs, or event-driven triggers at a scope broader than SaaS business automation, and your team is comfortable with YAML-defined, version-controlled flows; our n8n vs Apache Airflow comparison covers a similar category question against the other major open-source data orchestrator if you're still narrowing the field. Choose n8n if you're automating business workflows across SaaS apps, want a visual canvas over YAML, or need an AI-agent step as part of the job; our guide to self-hosting n8n covers the deploy details if you've already decided.

Learn more directly from the source: n8n publishes full documentation and a large template library, and Kestra covers its plugin catalog and current Enterprise and Cloud pricing in its own docs, both worth reading before you commit infrastructure to either one.

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