n8n vs LangGraph in 2026: Automation or Agents? Licensing, Hosting, and Running Costs

As businesses look for ways to automate operations, two tools worth understanding are n8n and LangGraph. Both can support AI-powered workflows, but they approach development differently.

n8n provides a visual workflow automation platform that connects applications, APIs, and AI capabilities. LangGraph is a programming framework designed to build stateful, controllable AI agents and workflows.

Choosing between them is not simply about deciding which tool is better. It is about understanding your requirements, development resources, licensing obligations, hosting preferences, and long-term operating costs.

What Is n8n?

n8n is a workflow automation platform that allows developers and businesses to connect applications and automate repetitive tasks using a visual workflow editor.

Users can build workflows by connecting triggers, integrations, conditional logic, API requests, and other processing steps. It also supports AI-related functionality, including AI agents and integrations with language models.

For example, a business could create a workflow that receives a customer enquiry, categorizes it, retrieves relevant information, drafts a response, and routes it to an employee for approval.

Key Benefits of n8n

• Visual Workflow Building: Create workflows by connecting nodes instead of writing every integration from scratch.
• Application Integrations: Connect supported business applications, APIs, databases, and communication tools.
• AI Capabilities: Incorporate language models and agent-based steps into automated workflows.
• Self-Hosting Options: Run n8n on infrastructure you manage, subject to its licensing terms.
• Faster Prototyping: Build and test many business automations without developing a complete application from the ground up.

When Should Businesses Choose n8n?

n8n is a strong option when the primary objective is connecting existing systems and automating clearly defined business processes.

Typical use cases include:

• Synchronizing customer information between applications.
• Automating notifications and approval workflows.
• Processing incoming emails and support requests.
• Moving data between business systems.
• Creating AI-assisted workflows with defined steps.

For many routine business processes, a visual workflow is easier to build, inspect, and maintain than a fully custom application.

What Is LangGraph?

LangGraph is a framework for building stateful AI applications in which developers define how an agent or workflow progresses through different steps.

It allows developers to model workflows as graphs, with nodes representing operations and transitions determining what happens next. Applications can maintain state, handle branching logic, pause for human input, and resume execution when appropriate.

For example, a customer support agent might classify an enquiry, search documentation, retrieve account information, prepare a response, and request human approval before sending it.

LangGraph gives developers explicit control over how these steps are organized and how the application behaves when different situations arise.

Key Benefits of LangGraph

• Custom Agent Logic: Build workflows around specific business requirements.
• State Management: Maintain information across multiple steps in a process.
• Conditional Workflows: Route execution based on model outputs, business rules, or application state.
• Human-in-the-Loop Controls: Pause workflows for approval or additional information.
• Error Recovery: Design retry, recovery, and interruption handling into the application.
• Developer Flexibility: Integrate custom code, APIs, databases, and model providers.

When Should Businesses Choose LangGraph?

LangGraph is particularly useful when an application needs more control over complex or long-running workflows.

Examples include:

• Multi-step customer support agents.
• Research assistants that gather and evaluate information.
• Applications that need explicit approval checkpoints.
• Agents that interact with several tools and maintain state.
• Custom AI applications with complex branching and recovery requirements.

Because LangGraph is a programming framework, development typically requires more software engineering than assembling a basic workflow visually.

Automation vs Agents: Understanding the Real Difference

It is tempting to describe n8n as an automation tool and LangGraph as an agent framework. That distinction is useful, but it is not absolute.

n8n can build AI agents, and LangGraph can implement deterministic workflows.

The more useful distinction is how you want to build and control the system.

With n8n, developers typically assemble integrations and workflow steps through a visual interface. AI capabilities can be added where needed.

With LangGraph, developers define the application's state, operations, transitions, and control logic through code.

Consider an invoice-processing example.

An n8n workflow could receive an invoice, extract its details, validate required fields, and route it to an approver.

A LangGraph application could manage a more complex process in which an AI agent investigates discrepancies, gathers information from several systems, retries recoverable operations, and pauses when a decision requires human judgment.

Both tools can support sophisticated solutions. The appropriate choice depends on the complexity of the workflow, the desired level of control, and the team's development capabilities.

Licensing: Is n8n Free? Is LangGraph Open Source?

Licensing is an important consideration, particularly when a business plans to use these tools in commercial products or client projects.

n8n Licensing

n8n offers a free, self-hosted Community edition under its Sustainable Use License, alongside commercial plans and licensing arrangements.

The Community edition can be used for internal business purposes under the applicable license conditions. However, some commercial use cases require a separate commercial agreement.

For example, building and operating workflows for a client may be permitted under the applicable terms when the client only receives the results and does not access or configure the n8n instance. Offering customers a product that lets them create or configure their own workflows can require a commercial license.

Businesses should review the current license terms before embedding n8n into a customer-facing product or offering it as a hosted service.

LangGraph Licensing

The LangGraph open-source framework is available under the MIT License. This permits broad use, modification, and distribution subject to the license conditions.

However, the framework's license does not mean that every associated service is free. Managed deployment, monitoring, tracing, infrastructure, model APIs, and other third-party services can introduce additional costs.

The important distinction: LangGraph's open-source framework can be used without a framework license fee, while n8n's Sustainable Use License includes restrictions that businesses must evaluate against their intended use.

Neither licensing model eliminates infrastructure, development, maintenance, or AI usage costs.

Hosting: Cloud or Self-Hosted?

Both tools can be deployed in different environments, but the operational responsibilities vary.

Hosting n8n

Businesses can choose n8n Cloud or run a self-hosted instance.

With n8n Cloud, the provider manages the underlying hosting. This can reduce infrastructure administration and simplify setup.

With self-hosting, the organization manages the server environment, updates, backups, monitoring, security configuration, and scaling. This offers greater control over deployment but also creates additional operational responsibilities.

Hosting LangGraph

LangGraph applications can be deployed on infrastructure managed by the business or through supported deployment services.

Self-hosting provides control over the application's runtime environment and supporting infrastructure. However, the development team must plan for application availability, persistence, monitoring, scaling, and recovery.

LangGraph also has a managed deployment ecosystem associated with LangSmith, which can reduce some operational work. Managed services have their own pricing and service terms.

Which Hosting Option Makes More Sense?

Choose managed hosting when you want to reduce infrastructure administration and get started quickly.

Consider self-hosting when you need greater infrastructure control, have suitable technical expertise, and can manage the associated maintenance and security responsibilities.

The right decision depends on data requirements, availability expectations, operational skills, and the total cost of ownership.

Running Costs: What Will You Actually Pay?

The cost of running an automation or agent application involves more than the platform itself.

For either tool, the main cost components may include:

• Platform or Hosting Costs: Subscription fees or the infrastructure required to run the application.
• Model API Costs: Charges for model input and output tokens, where applicable.
• Database and Storage Costs: Persistent state, workflow data, logs, and supporting records.
• Integration Costs: Fees charged by connected applications or external APIs.
• Monitoring Costs: Logging, tracing, evaluation, and observability services.
• Engineering Costs: Development, maintenance, troubleshooting, and security updates.

The balance between these expenses depends on the architecture and workload.

n8n Running Costs

For n8n Cloud, pricing is based on workflow executions rather than charging separately for every step. One complete workflow run counts as an execution, regardless of the number of steps it contains.

At the time of writing, the official pricing page lists the Starter plan at €20 per month when billed annually, with 2,500 workflow executions per month. The Pro plan is listed at €50 per month when billed annually, with 10,000 executions per month. Prices and plan details can change, so check the official n8n pricing page.

For self-hosted deployments, businesses must account for infrastructure and maintenance. Commercial licensing may also apply, depending on the intended use.

Model API charges and third-party service fees can remain separate from the platform subscription.

LangGraph Running Costs

The LangGraph framework itself does not require a framework license fee under its MIT License. However, running a production application still requires resources and operational support.

A self-hosted deployment may incur costs for compute, databases, persistence, monitoring, model APIs, and engineering time.

Managed deployment adds the applicable service charges, which depend on the selected plan and usage.

The total cost can vary significantly depending on how often the agent runs, how much context it processes, how many model calls it makes, and how long each workflow takes.

A Simple Cost Calculation

Suppose a business processes 10,000 requests each month using an AI-powered application.

Assume, purely for illustration, that each request generates $0.004 in model API charges.

Monthly model cost:

10,000 × $0.004 = $40 per month.

If hosting costs $30 per month and monitoring costs $10 per month, the illustrative monthly total becomes:

$40 + $30 + $10 = $80 per month.

This calculation excludes development, maintenance, additional third-party integrations, and any applicable platform or managed-service fees. The numbers are examples, not actual price quotes for n8n or LangGraph.

The same cost model can be applied to either tool. What matters is the architecture, usage pattern, and operational requirements.

How to Choose the Right Tool for Your Business

Before choosing between n8n and LangGraph, start with the problem you need to solve.

Choose n8n if:

• You want to connect business applications quickly.
• Your workflow has mostly defined steps and predictable transitions.
• Visual development and easier workflow inspection are priorities.
• You need integrations and automation without building every component from scratch.

Choose LangGraph if:

• You are building a custom AI application.
• The workflow needs explicit state management and conditional control.
• You require complex recovery, interruption, or human-review logic.
• Your engineering team needs detailed control over agent behavior.

Consider combining them if:

• n8n can handle business integrations and event-driven automation.
• A custom LangGraph application handles complex agent reasoning.
• Both components can communicate through well-defined APIs and permissions.

A combined architecture can be useful, but it introduces additional integration, monitoring, and maintenance requirements. It should be adopted when the benefits justify the complexity.

How Tecneural Can Help

At Tecneural, understanding business requirements is an important starting point when evaluating software and automation solutions.

Businesses exploring workflow automation or custom AI applications should assess their existing systems, integration requirements, data security needs, expected workload, and long-term maintenance plans before choosing a technology stack.

A structured evaluation can help organizations select an approach that fits their operational goals and technical resources.

Conclusion

n8n and LangGraph offer different approaches to building automation and AI-powered applications.

n8n is a strong choice for connecting applications and building visual workflows, including workflows that use AI agents. LangGraph provides developers with greater control over stateful, custom agent applications and complex execution logic.

Licensing and hosting also matter. n8n's self-hosted Community edition has specific Sustainable Use License conditions, while the LangGraph framework uses the MIT License. Both approaches can involve infrastructure, model usage, and ongoing maintenance costs.

The best choice is not necessarily the tool with the lowest subscription price. It is the one that delivers the required functionality, control, and reliability at a sustainable total cost.

Looking to explore automation or custom software solutions for your business? Connect with Tecneural to discuss your technology requirements and implementation goals.

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