Kestra vs n8n

At a Glance

Kestra and n8n are the two workflow automation platforms in our directory you can run entirely on your own servers, free, with no execution caps. Both were founded in 2019, both are developer-facing, and both let engineering teams keep every byte of workflow data inside their own infrastructure. That shared foundation makes this the rare comparison where self-hosting is not the differentiator — it is the starting assumption.

The split runs through what a workflow actually is. For Kestra, it is a versioned YAML document that schedules and governs code running on infrastructure you operate: pipelines, containers, Terraform plans. n8n treats it instead as a node graph on a canvas that calls APIs, moves records between services, and — increasingly in 2026 — runs AI agents with humans in the loop.

Two Ways to Define a Workflow

Everything downstream follows from the authoring model, so start there.

A Kestra flow is a YAML file: tasks, triggers, retries, timeouts, and SLAs declared as configuration. Those files live in Git, ship through the same CI/CD pipeline as application code, and carry version history by default. Tasks themselves can run Python, Bash, Node.js, Go, or any container, so the declarative wrapper does not dictate the language underneath. The trade-off is upfront: someone has to learn Kestra's YAML vocabulary before the first flow runs.

n8n starts from the canvas instead. You drag nodes, wire them together, and watch data move between them in the execution view — then drop into a Code node whenever JavaScript or Python expresses the logic better than boxes can. That interactivity makes debugging tangible; each run shows exactly what entered and left every node. The cost is that a sprawling canvas is harder to code-review than a YAML diff, even with n8n's sub-workflows keeping things modular.

We rate both 7.0 for ease of use, which really means each is easy for a different kind of engineer. Config-first platform teams read Kestra fluently. Product engineers who think in API calls build faster in n8n.

Edge: Kestra when workflows must live in Git and pass review like code. n8n when iterating visually against live API responses is how your team actually works.

The Workloads Underneath

Look past the category label and the two tools carry different cargo.

Kestra's catalogue — more than 1,900 plugins by the vendor's current count — reads like a data platform inventory: AWS, Google Cloud, Azure, Snowflake, Databricks, dbt, Airbyte, Terraform. Add event-driven triggers, per-task retries, and audit logs, and you get an engine built to run scheduled and real-time pipelines at data-engineering scale. It competes with Apache Airflow and Prefect far more directly than with any Zapier-style tool, and its younger community relative to Airflow remains its most honest weakness.

n8n ships around 500 integration nodes aimed at the SaaS layer: Google Workspace, Slack, HubSpot, Salesforce, PostgreSQL, GitHub, plus OpenAI and Anthropic. The smaller number matters less than it looks, because the HTTP Request and Code nodes reach any API a developer can authenticate against. What n8n does not try to be is a heavy-compute scheduler; its unit of work is an API call or a script, not a warehouse job carrying an SLA.

Edge: Kestra for pipelines whose payload is data and infrastructure. n8n for automations whose payload is API traffic between the products your company already uses.

AI Agents vs AI Pipelines

The AI question splits the same way, and in 2026 it is the question buyers actually ask.

n8n has turned agentic automation into its identity. Native agent nodes handle multi-step reasoning, memory, and tool use, with human-in-the-loop checkpoints for decisions that need sign-off and error paths for the moments agents fail. The market noticed: a $180 million Series C in October 2025 valued n8n GmbH at $2.5 billion, and the agent builder is the feature pulling AI-curious teams toward the platform this year. Among self-hostable tools, its agent tooling has no serious European rival.

Kestra approaches AI from the orchestration side. Its AI Copilot drafts workflow YAML, and its plugin catalogue covers OpenAI, Mistral, and AWS Bedrock for building RAG and model pipelines. In Kestra's world, an AI system is another workload to schedule, retry, and audit — the machinery behind an agent rather than the agent itself. Kestra Technologies raised a $25 million Series A in March 2026, a fraction of n8n's war chest but real momentum for an orchestrator.

Edge: n8n, clearly, for building and supervising AI agents. Kestra for governing the data and model pipelines those agents depend on.

Licences and the Price of Free

Both platforms cost nothing to self-host at any volume. The fine print differs in ways procurement teams should read.

Kestra's core is Apache 2.0 — a genuine OSI-approved open-source licence with no strings on commercial use. Its paid layers are opaque, though. Kestra Cloud bills on usage and the Enterprise Edition sells as an annual per-instance licence, but neither price appears on the website, so budgeting starts with a sales call.

n8n inverts that. Its source is available under the fair-code Sustainable Use License, which permits free internal use and modification but is not open source by OSI definition. Offering n8n itself to others as a paid service is off the table. Its cloud pricing, by contrast, is fully published: Starter at EUR 20 per month billed annually (EUR 24 monthly) for 2,500 executions, and Pro at EUR 50 for 10,000. A Business tier at EUR 667 adds SSO. Execution-based billing stays predictable because a fifty-node workflow still counts as one run.

Edge: Kestra on licence openness and a truly uncapped free ceiling. n8n on knowing what the managed option costs before you talk to anyone.

Sovereignty, Certifications, and Where Data Sits

Self-hosting answers the residency question identically for both: your servers, your jurisdiction, your keys. The differences appear once you consider the managed clouds and the paperwork.

n8n Cloud runs in Frankfurt on Azure Germany West Central, encrypts at rest with AES-256 under a FIPS 140-2 implementation, and provides a DPA with Standard Contractual Clauses. A SOC 3 report is public and SOC 2 is in progress, though ISO 27001 is still missing — a checkbox some enterprise reviews will flag. Being a Berlin GmbH with German cloud residency earns n8n a 9.0 EU-compliance rating from us.

Kestra Cloud is SOC 2 compliant, and the French company's stronger card is architectural: air-gapped deployment support for networks with no public internet at all, which n8n does not advertise. We rate Kestra 8.0 for EU compliance, with the gap reflecting a thinner published certification stack rather than any structural weakness.

Edge: n8n for a managed cloud with German residency and clearer transfer paperwork. Kestra for the isolated, air-gapped extreme of sovereignty.

When to Choose Kestra

Choose Kestra when the work being automated is engineering output: warehouse loads, dbt runs, container jobs, Terraform applies, model training. Its declarative model keeps hundreds of pipelines reviewable in Git rather than scattered across a canvas, and per-task retries and SLAs treat failure as a first-class design concern. High-volume teams also never meet a pricing ceiling, because the Apache 2.0 core has none.

Kestra is the pick if you would otherwise be evaluating Airflow or Prefect, and you want that class of orchestration from an EU company under the most permissive licence in this comparison.

When to Choose n8n

Choose n8n when the work being automated is connective: syncing tools, routing requests, enriching records, and wiring AI agents into processes that still need human judgement at key steps. The code-when-you-need-it canvas keeps one platform viable from a simple webhook relay up to a supervised agent workflow. Teams that prefer managed hosting get published EUR pricing and Frankfurt residency without a procurement negotiation.

n8n is the pick for developer teams replacing Zapier with something they can self-host — and for anyone whose 2026 roadmap says "agents" more often than "pipelines". Non-technical teams weighing a visual-first option should read our Make vs n8n comparison instead.

The Verdict

This comparison has no loser, because the two platforms automate different layers of the same stack. At heart, Kestra is an orchestrator for the infrastructure layer; n8n is an integration-and-agent platform for the business layer — two European tools, born the same year, both free to self-host, aimed at different jobs.

Kestra wins on licence purity, orchestration depth, and air-gapped sovereignty. Its costs are a younger ecosystem than Airflow's, community-only support on the free tier, and paid tiers you cannot price without a call. n8n wins on AI-agent capability, published pricing, and a managed German cloud, at the cost of a fair-code licence and a certification stack still maturing past SOC 3.

Our ratings land close — 8.2 for n8n, 8.0 for Kestra — and the deciding vote belongs to your workload. Data platform teams should reach for Kestra. Teams automating SaaS processes and building agents should reach for n8n. A company doing both at scale can defensibly run each where it is strongest.

FAQ

Are Kestra and n8n both open source?

Only Kestra is open source in the strict sense, with its core under the OSI-approved Apache 2.0 licence. n8n publishes its source under the fair-code Sustainable Use License, which allows free internal use and modification but restricts reselling n8n as a hosted service.

Which is better for building AI agents in 2026?

The answer is n8n, decisively. Its native agent nodes support multi-step reasoning, memory, tool use, and human-in-the-loop approval, and its October 2025 Series C funded further agent development. Kestra's AI strength is orchestrating the RAG and model pipelines that sit behind agents, not building the agents themselves.

Can I self-host both platforms for free?

Yes. Kestra's open-source core and n8n's Community Edition both run on Docker or Kubernetes with unlimited executions and no feature-gated trial mechanics. In each case you pay only for your own infrastructure and the operational time to maintain it.

Is Kestra or n8n the better Zapier alternative?

For the SaaS-to-SaaS automation Zapier is known for, n8n is the closer replacement, pairing 500+ integration nodes with code flexibility Zapier lacks. Kestra targets data and infrastructure orchestration, a job Zapier was never designed to do.

Do teams ever run Kestra and n8n together?

Running both is a defensible architecture, not a duplication. Kestra schedules and governs the data pipelines feeding a warehouse or model, while n8n handles business-facing automations and agent workflows on top. Since both self-host for free, trialling them side by side costs only setup time.