Lindy vs n8n: Which AI Agent Platform Should You Use in 2026?

Lindy vs n8n is the decision most teams hit the moment they stop experimenting with AI agents and try to put one into production. Lindy sells you an AI employee you configure in plain language; n8n sells you a fair-code workflow engine with 70+ LangChain-powered AI nodes that you wire together yourself. The two platforms bill on completely different axes — per seat plus credits versus per workflow execution — and that billing difference, more than any feature, is what should drive your choice.

Quick Comparison

What Is Lindy?

Lindy is a cloud platform for building AI agents — it calls them Lindies — that you configure by describing what you want rather than by assembling logic. An agent gets a trigger, a set of connected tools, and instructions in natural language, then runs on its own.

Every Lindy plan includes the same core capability set: Slack integration, scheduled routines, 40+ skills, meeting tools, inbox management, computer use, model selection, and access to the integration library. You are not buying features as you move up tiers — you are buying usage.

That library covers 1,400+ integrations, and crucially an admin connects them once for the workspace. A non-technical colleague can then build an agent against Gmail, HubSpot, or Slack without ever touching an API key.

What Is n8n?

n8n is a workflow automation platform distributed under a fair-code model, with a self-hosted Community Edition available free on GitHub — where the project sits at roughly 206,000 stars after adding more than 112,000 during 2025, the largest single-year total in the JavaScript Rising Stars ranking's ten-year history.

Its AI story is built on LangChain. n8n ships 70+ AI-specific nodes covering AI agents, RAG pipelines, vector database integrations, memory, and connections to a dozen-plus LLM providers including OpenAI, Anthropic, Google Gemini, Mistral, and local models through Ollama. The AI Agent node acts as the orchestration layer, reasoning over which connected tool to call.

The trade-off is explicit: n8n gives you a node graph and expects you to understand it. You bring API keys, you choose your models, and if you self-host, you own the server.

Key Differences

Pricing and Value

This is the comparison that actually matters, because the two platforms meter fundamentally different things.

n8n's cloud pricing runs €20/mo for Starter (2,500 executions, 5 concurrent), €50/mo for Pro (10,000 executions, 20 concurrent), and €667/mo for Business (40,000 executions, 30 concurrent), all billed annually. Business is currently self-hosted only with the cloud version on a waitlist. Every paid tier includes unlimited users, unlimited workflows, and every integration.

The important detail is that n8n bills per execution, not per step. A workflow with 40 nodes and a workflow with 4 nodes cost the same to run once. At Pro, 10,000 executions for €50 works out to roughly €0.005 per workflow run regardless of internal complexity.

Lindy's pricing is $29.99/user/mo for Plus (3,000 credits/user), $99.99 for Pro (15,000 credits), $199.99 for Max (35,000 credits), and custom Enterprise. Credits are consumed per action the agent takes, so a single multi-step agent run draws several credits rather than one. That makes forecasting genuinely harder: the same agent handling a simple email versus a research task with a dozen tool calls costs materially different amounts.

The practical upshot: n8n's model rewards complex, deterministic, high-volume workflows — you pay once per run no matter how much happens inside. Lindy's model rewards a modest number of users running open-ended agentic work where you cannot predict the step count in advance, and where you would rather pay for outcomes than run infrastructure.

Watch the cliff on both sides. n8n jumps from €50/mo to €667/mo between Pro and Business with nothing in between. Lindy's per-seat pricing means rolling agents out to twenty non-technical staff costs twenty seats, even if most of them barely use it.

Setup and Time to First Agent

Lindy wins this cleanly, and it is not close. Because the workspace admin connects integrations once and no API keys are required per user, a first working agent is a matter of describing it. Lindy also leans on Slack as an interface — you can @mention an agent in a channel and get an answer back rather than opening a builder at all.

n8n requires you to obtain and configure credentials, pick an LLM provider, and understand the node model before anything runs. That is real friction on day one. It is also the source of n8n's long-run advantage: once you understand the graph, you can express logic that a natural-language agent configuration simply cannot.

Control, Debugging, and Complex Logic

n8n's node graph is a debugging surface. You can inspect what entered and left every node, and the AI Agent node's ReAct execution mode surfaces intermediate reasoning steps in the execution log rather than only the final output. When an agent makes a bad decision, you can see where.

The platform also offers four memory backends for agents — in-memory, Redis, Postgres, and Motorhead — which matters once you need state to survive restarts.

Lindy's abstraction is the point, and also the ceiling. You get a well-behaved agent quickly, but when behaviour needs conditional branching, custom transforms, or precise retry semantics, you are working against an interface designed to hide exactly those things.

Privacy, Hosting, and Compliance

If your data cannot leave your infrastructure, this section decides the comparison for you.

n8n can be self-hosted on your own servers, a Kubernetes cluster, or a private cloud. Workflow data, credentials, and LLM API calls stay in your environment — and paired with Ollama for local models, an n8n deployment can run without any third-party AI provider at all. For regulated industries, that is often non-negotiable.

Lindy is cloud-only. There is no self-hosted or air-gapped option. It does address compliance at the top of the range: Enterprise includes HIPAA compliance with a signed BAA, audit logs, shared usage and bonus credits, and dedicated support. That covers a lot of real-world healthcare and enterprise requirements — but it is compliance within Lindy's cloud, not data residency in yours.

Licensing

Lindy is straightforward proprietary SaaS. n8n's Sustainable Use License needs reading before you build a business on it. Version 1.0 permits use and modification "only for your own internal business purposes or for non-commercial or personal use," and permits distribution to others "only if you do so free of charge for non-commercial purposes." Files with .ee. in the filename require a separate n8n Enterprise License.

Translated: running n8n internally, including at a for-profit company, is fine. Reselling it or wrapping it into a commercial product you distribute is not. Agencies planning to host n8n for clients should get that reviewed.

Who Should Choose Which

Choose Lindy if you:

• Want agents running today without provisioning servers or gathering API keys
• Have a small team of heavy users rather than a large team of light ones
• Need non-technical staff to build and modify their own automations
• Value built-in computer use, inbox management, and meeting tooling over assembling equivalents
• Need HIPAA with a signed BAA and are comfortable with a cloud vendor
• Prefer paying a predictable per-seat fee over operating infrastructure

Choose n8n if you:

• Need data to stay on your own infrastructure, or want fully local models via Ollama
• Run high-volume workflows where per-execution billing is dramatically cheaper
• Have someone technical who can own the deployment and the node graph
• Want unlimited seats without per-user cost as the team grows
• Need real debugging, persistent agent memory, and precise control over agent logic
• Want to start genuinely free and self-hosted before committing budget

Final Verdict

These two tools are not really competing for the same job, and picking well means being honest about which job you have.

Lindy wins on time-to-value and accessibility. Zero API keys, 1,400+ integrations pre-connected by an admin, and a Slack-native interface mean non-technical teams ship working agents in an afternoon. For customer support, sales follow-up, and inbox triage staffed by people who will never open a node editor, Lindy is the stronger buy — and the credit model is a reasonable trade for never thinking about servers.

n8n wins on cost at scale, control, and data sovereignty. Execution-based billing means workflow complexity is free, which inverts Lindy's economics the moment your automations get deep. Self-hosting plus Ollama is the only option here if data cannot leave your environment. The 70+ LangChain nodes and four memory backends give you room that a natural-language agent builder does not.

For most small teams: start with n8n's free self-hosted Community Edition if you have technical capacity, and evaluate Lindy Plus at $29.99/user if you do not. For high-volume automation, n8n's per-execution model wins on arithmetic alone. For regulated environments needing on-premise data, n8n self-hosted is the only real candidate. And for a non-technical team that needs results this week, Lindy's abstraction is worth paying for.

One caveat on both: verify pricing before you commit. Lindy's tiers and n8n's Business cloud availability have both shifted during 2026, and the figures here reflect the vendors' published pages as of September 2026.