n8n Alternatives: 10 Workflow and AI Agent Tools Compared

n8n earned its following honestly. The node graph is fast to reason about, the self-hosted Community Edition runs anywhere, and the native AI nodes cover a lot of ground. So the question is rarely whether n8n is good. The question is whether it still fits once your team hits a real constraint: a license that blocks the thing you want to build, a self-hosted instance that has become a second job, or AI work that needs governance n8n was never designed to give you.

This guide compares 10 alternatives across three camps: open-source and self-hosted, cloud convenience, and AI-agent platforms. Each verdict is honest about where the tool wins and where it does not. If your real need is a more permissive open-source automator, we say so plainly, and the answer is probably Activepieces or Windmill.

Key Takeaways: Activepieces (MIT) is the closest low-friction open-source swap for n8n. Windmill is the code-first pick. Make and Zapier trade control for cloud convenience. Gumloop and Stack AI lead the visual AI-agent camp. Kestra, Temporal, and Apache Airflow win when orchestration or durability matters more than connectors. Major fits one specific lane: governed agents that act through deterministic apps, with RBAC and audit built in, for teams that want to stop running automation infrastructure.

Why people look for n8n alternatives

Four triggers push teams off n8n, and only one of them is a complaint about quality.

• Licensing. n8n ships under the Sustainable Use License, a fair-code model that allows internal business use, modification, and non-commercial redistribution, but restricts hosting it as a competing commercial service. Teams that want to embed automation in a product they sell, or want a plain permissive license, hit the wall here. Activepieces answers this directly with MIT.
• Self-host maintenance. The Community Edition costs nothing to license. Running it in production is a different bill: version upgrades, Postgres and queue backups, worker scaling for heavy executions, and securing the instance. That work never appears on a pricing page.
• AI and agent gaps. n8n has strong AI nodes, but a node graph follows fixed rules. Teams building agents that reason over a goal and choose actions want a model built for that, which is where Gumloop, Stack AI, and Major come in.
• Governance. Role-based access at the data layer, audit logs your security team can export, and per-user identity on every action are enterprise table stakes. On self-hosted n8n you assemble most of that yourself.

One distinction matters before the list. Automation runs fixed steps you wired in advance. An agent reasons over a goal and decides which actions to take. Most tools here are automation engines. A few are agent platforms. Read each entry for which one you are actually buying. For the deeper version of that split, see reasoning agents, not fixed nodes.

The true cost of self-hosting: A $0 license is not a $0 deployment. Budget for a server or cluster, scheduled version upgrades that can break custom nodes, automated backups of the database and execution queue, worker autoscaling for spiky loads, TLS and network hardening, and someone on call when a run fails at 2am. For a small team this is often one to several engineer-days per month of carrying cost. Self-hosting is the right call when data residency or air-gap is a hard requirement. Otherwise, price the ops time honestly before you choose it.

Quick comparison

Ten tools across the columns that decide a switch: license, hosting, interface, AI and agent support, governance, and pricing. n8n sits in the first row as the baseline.

• n8n (baseline)
• • License / Model: Sustainable Use (fair-code)
  • Self-hosting: Yes, Community Edition
  • Code vs visual: Visual + code nodes
  • AI / agent support: Native AI nodes; fixed-rule graph
  • Governance (RBAC/audit): Enterprise tier; self-assemble on CE
  • Pricing: Cloud from ~€24/mo; CE free to self-host
• Activepieces
• • License / Model: MIT (community)
  • Self-hosting: Yes, Docker/Helm
  • Code vs visual: Visual + code pieces
  • AI / agent support: Native MCP; AI agents on all plans
  • Governance (RBAC/audit): Enterprise tier (SSO/RBAC)
  • Pricing: Free tier; from $5/active flow/mo
• Windmill
• • License / Model: AGPLv3 (community)
  • Self-hosting: Yes, unlimited execs
  • Code vs visual: Code-first (TS/Py/Go/Bash)
  • AI / agent support: AI script gen; not agent-native
  • Governance (RBAC/audit): Audit/SSO in Enterprise Edition
  • Pricing: Free tier; Team $10/user/mo
• Kestra
• • License / Model: Apache 2.0 (OSS)
  • Self-hosting: Yes
  • Code vs visual: Declarative YAML
  • AI / agent support: AI/data orchestration plugins
  • Governance (RBAC/audit): RBAC/SSO/audit in Enterprise
  • Pricing: OSS free; Enterprise custom
• Make
• • License / Model: Proprietary SaaS
  • Self-hosting: No
  • Code vs visual: Visual scenarios
  • AI / agent support: Make AI Agents on paid plans
  • Governance (RBAC/audit): Plan-gated team controls
  • Pricing: Core from $9/mo (annual)
• Zapier
• • License / Model: Proprietary SaaS
  • Self-hosting: No
  • Code vs visual: Visual Zaps
  • AI / agent support: AI Agents as paid add-on
  • Governance (RBAC/audit): Plan-gated controls
  • Pricing: Free 100 tasks; Pro from $29.99/mo
• Gumloop
• • License / Model: Proprietary SaaS
  • Self-hosting: No
  • Code vs visual: Visual, drag-and-drop
  • AI / agent support: Agent-native builder
  • Governance (RBAC/audit): Gumstack monitoring; SSO on Enterprise
  • Pricing: Free 2K credits; Pro ~$97/mo
• Stack AI
• • License / Model: Proprietary SaaS
  • Self-hosting: No (managed)
  • Code vs visual: Visual agent builder
  • AI / agent support: Agent-native; LLM workflows
  • Governance (RBAC/audit): Enterprise compliance tiers
  • Pricing: Free tier; paid plans scale up
• Temporal
• • License / Model: Apache 2.0 (OSS)
  • Self-hosting: Yes, self-host free
  • Code vs visual: Code-first SDKs
  • AI / agent support: Durable exec for agent backends
  • Governance (RBAC/audit): Namespace controls; Cloud audit
  • Pricing: OSS free; Cloud usage-based
• Apache Airflow
• • License / Model: Apache 2.0 (OSS)
  • Self-hosting: Yes
  • Code vs visual: Code-first (Python DAGs)
  • AI / agent support: ML/data pipelines, not agents
  • Governance (RBAC/audit): RBAC in core; audit via setup
  • Pricing: Free; managed via vendors
• Major
• • License / Model: Commercial; Helm self-host
  • Self-hosting: Optional. Yes, in your VPC via Helm
  • Code vs visual: Apps + agents (build by prompt or code)
  • AI / agent support: Governed agents over apps
  • Governance (RBAC/audit): RBAC at query layer; audit to SIEM
  • Pricing: Contact for pricing

Prices and license terms reflect public sources as of June 2026 and change often. Verify against each vendor's pricing and license pages before you commit.

Open-source and self-hosted alternatives

Activepieces

Best for: teams that want n8n's openness with a permissive MIT license and a cleaner UI.

What it does. Activepieces is an open-source automation platform with a visual builder and reusable code pieces. The Community Edition is MIT licensed and free to self-host on Docker or Helm. It ships native MCP support, both as a server that exposes your flows as tools and as client pieces that call external MCP tools, plus AI agents on every plan including the free tier.

Key advantages. The MIT license removes the fair-code restrictions that send teams looking in the first place. The connector set and roughly 400 MCP servers make it the broadest open-source option for AI-agent integration, and the UI is closer to Zapier than to a developer tool.

Ideal users. n8n users whose main complaint is the license or UI complexity, and who still want to self-host.

Pricing. Free tier with 10 active flows. Paid from $5 per active flow per month, with Plus and Business tiers above that. Self-hosted Community Edition is free under MIT.

Windmill

Best for: engineering teams that want code-first workflows with Git-native version control.

What it does. Windmill turns scripts in TypeScript, Python, Go, Bash, and 20-plus other languages into webhooks, workflows, and internal UIs. The Community Edition is AGPLv3 and runs self-hosted with unlimited executions. It auto-generates a frontend from a script's input parameters, which makes internal tools fast to ship.

Key advantages. No proprietary SDK lock-in. You write normal code, generate it with an LLM, and port it out anytime. Windmill benchmarks its engine well ahead of Airflow on raw throughput.

Ideal users. developers who find visual node graphs limiting and prefer code in source control.

Pricing. Free tier with a daily execution cap. Team plan at $10 per user per month with unlimited executions. The self-hosted Enterprise Edition adds SSO and audit and requires a separate license. Note the AGPLv3 obligation: distributing a derivative work may require open-sourcing it, so commercial embedders often move to the Enterprise Edition.

Kestra

Best for: orchestration that spans data, infrastructure, and AI systems.

What it does. Kestra is an Apache 2.0 declarative orchestration platform. Workflows are defined in YAML, triggered by events or schedules, and extended through a large plugin set. The open-source edition includes unlimited workflows and the visual editor.

Key advantages. Declarative YAML opens orchestration to people who do not want to write DAG code, while staying API-first for those who do. It handles event-driven execution at scale, which is where lighter automation tools struggle.

Ideal users. platform and data teams coordinating pipelines across many systems.

Pricing. Open-source edition is free. Enterprise adds RBAC, SSO, multi-tenancy, and audit logs at custom pricing.

Cloud convenience alternatives

Make

Best for: teams that want zero ops and a visual builder priced by operations, not tasks.

What it does. Make is a hosted visual automation tool built around scenarios. Every paid plan includes Make AI Agents and related AI features rather than gating them behind add-ons.

Key advantages. Operations-based pricing is far cheaper at volume than per-task billing. There is no instance to maintain.

Ideal users. operations and growth teams that value convenience over self-hosting and control.

Pricing. Core plan from $9 per month on annual billing for 10,000 operations, scaling up by volume.

Zapier

Best for: the widest app catalog when breadth of integrations beats cost control.

What it does. Zapier is the largest hosted automation network by connector count. AI Agents, Copilot, and Chatbots exist as separate paid products layered on the base subscription.

Key advantages. If a SaaS tool has an integration anywhere, it usually has one here first. Setup is quick for non-technical users.

Ideal users. non-technical teams that need a specific long-tail connector right now.

Pricing. Free plan capped at 100 tasks. Professional from $29.99 per month for 750 tasks. AI Agents and chatbots are add-ons that can push a fully agentic setup to $150 to $200 per month.

AI-agent platforms

Gumloop

Best for: non-developers building AI agents with a drag-and-drop canvas.

What it does. Gumloop is a visual AI automation platform with over 115 pre-built blocks. It targets employees who want to build agents without code and can deploy proactive agents into Slack or Microsoft Teams.

Key advantages. Low barrier to entry for agent building, and a maturing governance story through Gumstack, which monitors AI usage across tools for enterprise oversight.

Ideal users. business teams standardizing on a no-code agent builder.

Pricing. Free plan with 2,000 credits. Solo at $37 per month, Pro around $97 per month, and Enterprise with SSO/SAML and Gumstack at custom pricing.

Stack AI

Best for: building LLM-driven agent workflows with a managed, compliance-minded backend.

What it does. Stack AI is a visual builder for AI agents and LLM workflows, fully managed, with enterprise compliance tiers for regulated teams.

Key advantages. Agent-native from the ground up, with compliance options that matter to teams in regulated sectors.

Ideal users. teams that want hosted agents and a documented compliance posture.

Pricing. Free tier to start, with paid plans that scale by usage and enterprise needs. Confirm current numbers on the vendor site.

Durable execution and data orchestration

Temporal

Best for: developers who need durable, stateful execution for long-running processes.

What it does. Temporal is a workflow-as-code platform for durable execution. You write workflows in application code, and Temporal guarantees they survive crashes, retries, and restarts. Self-hosting is free under Apache 2.0.

Key advantages. Reliability for processes that run for minutes, days, or weeks. It is the right backend when an agent's actions must never silently drop.

Ideal users. engineering teams embedding durable workflows inside their own applications.

Pricing. Open source is free to self-host. Temporal Cloud is managed with usage-based pricing.

Apache Airflow

Best for: Python-defined batch data pipelines with a mature ecosystem.

What it does. Airflow is the long-standing standard for scheduling data pipelines as Python DAGs. It is battle-tested with a vast provider ecosystem.

Key advantages. Deep ecosystem and broad hiring pool. Most data engineers already know it.

Ideal users. data engineering teams running scheduled ETL and ML pipelines.

Pricing. Free and open source. Managed offerings are available through cloud vendors. One caution: Airflow 2.x reached end of life in April 2026, so plan around a supported 3.x line or a managed provider.

Major

Best for: teams that want governed AI agents and want to stop running automation infrastructure.

What it does. Major lets you build internal apps and agents by describing them. Agents reason over a goal and act through deterministic apps, so the action layer behaves predictably while the reasoning layer stays flexible. RBAC is enforced at the query layer, every action is captured in audit logs, and agents run with the identity of the person who invoked them. It self-hosts in your own VPC via Helm when control is non-negotiable.

Key advantages. Governance is built into the platform rather than assembled by hand: audit logs you can export to a SIEM, and run-as-invoker OAuth so an agent acts as the user, not a shared service account. You get the control teams originally wanted from self-hosting without carrying the full maintenance bill.

Honest limits. Major has fewer prebuilt automation connectors than n8n and is not a like-for-like node editor. If your need is a large library of drag-and-drop integration nodes, Activepieces or n8n itself is the better tool. Major is built for governed agents and apps, not as a drop-in n8n clone.

Ideal users. platform and security-conscious teams putting AI agents into production with audit and access control as requirements.

Pricing. Contact for pricing. Self-hosting via Helm is available for teams with data-residency or air-gap requirements.

How to choose

Match the tool to your center of gravity, not to a feature checklist.

• Open-source and self-hosted: Activepieces for a permissive MIT license and an approachable UI. Windmill when your team would rather write code than wire nodes.
• Code-first: Windmill for scripted workflows and internal tools. Temporal when durability and long-running state are the hard requirement.
• Data pipelines: Airflow if your team already lives in Python DAGs. Kestra for declarative, event-driven orchestration across many systems.
• Cloud convenience: Make for cheaper operations-based pricing. Zapier when you need a specific connector from the largest catalog.
• Governed agents: an agent platform with RBAC and audit built in. This is the lane Major is built for.

Worked example: migrate an n8n workflow into a governed agent

Here is a concrete migration path that keeps your existing n8n logic readable while moving the decision-making into a governed agent. The example moves a lead-routing workflow.

• Step 1, export the source. In n8n, the workflow uses a Webhook Trigger node, an HTTP Request node to enrich the lead, an IF node to branch on company size, and two Set nodes that prepare the payload for a CRM Create node. Pull the full definition through the public API so you have the exact node and connection graph.
• Step 2, read it with the n8n public API. Authenticate with the X-N8N-API-KEY header against /api/v1/workflows. The snippet below creates a new workflow from a definition, which is the same shape you read back when exporting.

# Create (or re-create) an n8n workflow via the public REST API.
# Real endpoint and auth header, n8n public API v1.
curl -X POST "https://YOUR-N8N-HOST/api/v1/workflows" \
-H "X-N8N-API-KEY: $N8N_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "Lead routing (export)",
"nodes": [
{
"parameters": { "path": "lead-intake", "httpMethod": "POST" },
"name": "Webhook Trigger",
"type": "n8n-nodes-base.webhook",
"typeVersion": 1,
"position": [250, 300]
},
{
"parameters": {
"url": "https://api.example.com/enrich",
"method": "GET"
},
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4,
"position": [500, 300]
}
],
"connections": {
"Webhook Trigger": {
"main": [[{ "node": "HTTP Request", "type": "main", "index": 0 }]]
}
},
"settings": {}
}'

• Step 3, separate decision from action. The IF node was making a routing decision with a hand-coded threshold. In the agent model, that branch becomes a reasoning step: the agent reads the enriched lead and decides the route. The CRM Create call becomes a deterministic app action with a fixed, typed interface, so the model never improvises the write.
• Step 4, wrap the action in governance. Configure the CRM action to run with run-as-invoker OAuth, so the write is performed as the sales operator who triggered it, not a shared key. RBAC at the query layer limits which records the agent can touch. Every run lands in the audit log.
• Step 5, verify and ship. Replay a sample lead, confirm the audit entry shows the invoker identity and the exact action taken, then export those logs to your SIEM. If you want the full build walkthrough, see build a governed agent.

The payoff: the brittle IF-node threshold becomes a reasoning step that adapts, while the CRM write stays deterministic and fully governed. When something looks wrong, you can audit what an agent did down to the individual action.

Can you build AI agents in n8n?

Yes, to a point. n8n has native AI nodes and an AI Agent node that can call tools and chain LLM steps inside a workflow. That covers many assistant-style tasks. The limit is structural: a node graph executes a fixed path you defined, so the further you push toward open-ended reasoning and autonomous action, the more you are bending an automation tool to do agent work. When the action side needs hard guarantees, governed access, run-as-invoker identity, and exportable audit, a platform built for agents over deterministic apps fits the job better than a node graph retrofitted for it.

What this doesn't cover: This comparison weighs license, hosting, interface, AI and agent support, governance, and headline pricing. It does not benchmark raw execution throughput, rate the depth of any single connector, model total cost of ownership at your specific volume, or evaluate regional data-residency certifications. Treat it as a shortlist tool. Before you commit, run a proof of concept on your real workflows and confirm each vendor's current pricing and license terms directly.

The Major take

Teams adopt n8n to get control. The catch is that control arrives as a bill you keep paying: the upgrades, the backups, the hardening, and the governance you assemble by hand, all while the engine still only follows the fixed rules you wired. The thing you bought for freedom slowly becomes the thing you maintain.

Major resolves that constraint from the other side. Agents reason over a goal and act through deterministic apps, with RBAC, audit logs, and rollback built into the platform layer instead of bolted on. Two capabilities carry the governance story: audit logs you can export straight to your SIEM, and run-as-invoker OAuth so every action is attributable to a real person. When you genuinely need to self-host, Helm puts it in your own VPC.

Major is not a cheaper open-source n8n. For pure open-source automation, Activepieces and Windmill are the honest picks. Major is the governed, lower-ops path for teams ready to stop running the infrastructure and start shipping agents they can actually audit. If that is the constraint you are hitting, build a governed agent and keep the control without the maintenance tax.

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