Langflow vs n8n: which fits your team's first AI project
Choose Langflow when the first project is an AI product: an agent, a retrieval-augmented generation (RAG) assistant or a workflow exposed as an API or MCP server, built by Python engineers who want to write custom components. Langflow is released under the MIT license, so your legal review of the core is short, and its README describes Python-customizable components, an interactive playground, multi-agent orchestration and deployment of flows as an API, JSON export or MCP server. Choose n8n when the first project is business process automation: moving data between many SaaS systems, with business users building and changing workflows. n8n advertises more than 1,500 integrations, but it is distributed under the Sustainable Use License, which permits use only for your own internal business purposes or non-commercial use. If you plan to sell n8n-based automation to customers or embed it in a commercial product, that license is the first thing to resolve with counsel.
The decision turns on three questions. First, who builds and maintains the flows. Langflow suits teams with engineers who will version Python components and review changes like application code. n8n suits teams where operations or analysts own the canvas and need many prebuilt connectors. Second, the license terms for the intended use. Confirm the Sustainable Use License terms against your actual deployment model before committing, and remember that files with .ee in their name or directory need a valid n8n Enterprise License. Third, scale and identity requirements. Langflow installs with SQLite by default, and its docs say PostgreSQL is better suited for production, while Kubernetes is documented for high availability. n8n queue mode, with a main instance, Redis and a worker pool on PostgreSQL, is the route to scaling executions, and multi-main high availability is restricted to Enterprise self-hosted deployments.
Before you pilot either tool, check identity and operating needs with the vendor and in your own environment. For n8n, SAML single sign-on is available on the Business and Enterprise plans, and the pricing page places SSO and LDAP in those tiers, so confirm which edition you need. For Langflow, confirm how authentication and superuser setup work in your release, since official packages require a password set through LANGFLOW_SUPERUSER_PASSWORD. Ask your team to name the owner of each flow or workflow, the person who approves changes and the backup and upgrade routine. Those answers, more than the feature list, will show which platform your team can run after the first project ships.
Which scenario favors Langflow and which favors n8n
This table maps common team situations to the platform that usually fits better and the condition that tips the balance. Read each row as a starting point: the condition column is what you should verify against your own team, data and integration needs before deciding.
| Scenario | Better fit | Condition that makes it so |
|---|---|---|
| Internal RAG assistant built by an engineering team | Langflow | Engineers will write custom Python components, the flow needs a visual playground for iteration, and the team is comfortable running a Python service with PostgreSQL for production. |
| Customer-facing AI product | Langflow | The product is an AI workflow exposed as an API or MCP server, and you need an MIT-licensed codebase you can modify and redistribute without a commercial license from n8n. |
| Back-office automation with occasional AI steps | n8n | Most of the work is moving data between business systems, and an LLM call is one step among many. Confirm that your intended use is internal business use, which the Sustainable Use License covers. |
| High-volume webhook processing | n8n | Throughput depends on scaling execution workers through queue mode with Redis and PostgreSQL. Confirm the scaling design with n8n and plan key management for all worker nodes. |
| Team with mostly non-developer builders | n8n | Builders need a general automation canvas with many prebuilt integrations and a familiar trigger-and-action model. Python component authoring in Langflow is a developer task. |
| Team that must redistribute or commercialize the platform | Langflow | Your distribution model depends on license terms, so have counsel review the Sustainable Use License against the intended use before choosing n8n. |
How Langflow and n8n are built and what that means for operations
Langflow and n8n both run as self-hosted services, but they put state, scaling and secrets in different places. The cards below separate the install paths, database choices and queue-mode components so you can estimate patching, backup and on-call work for each before you commit.
Langflow install paths
Langflow runs as a Python package (Python 3.10 to 3.14, with uv recommended), as a Docker image, or as Langflow Desktop for Windows and macOS. Desktop suits individual builders, while the Python package and Docker image are the paths for shared servers. Each path is a separate upgrade process, so confirm which one your team will patch and who owns that upgrade cycle.
SQLite default and PostgreSQL for production
Langflow uses SQLite out of the box, and its documentation says PostgreSQL is better suited to production because of concurrent users, data integrity and scalability. Moving to PostgreSQL means you own database backups, restore tests and connection limits. Ask whether your flows, credentials and run history must survive a host loss before you accept the default.
Kubernetes and high availability guidance
Langflow's deployment docs cover Docker, remote servers behind Caddy or Nginx with HTTPS, and Kubernetes for production-grade, high-availability setups. For a single team, a container behind a reverse proxy is simpler to patch. Choose Kubernetes only if you already run a cluster team that can manage persistent volumes, probes and rolling upgrades.
n8n Node.js and TypeScript core
n8n is a fair-code platform built with TypeScript and Node.js, combining a visual canvas with custom JavaScript or Python code. Your engineers patch the Node runtime, the n8n image and any custom nodes together. Track the stable line closely, since the current stable release is 2.43.7, and test workflows against each upgrade before rolling it to production.
Queue mode with Redis and PostgreSQL
In queue mode, the main instance receives triggers and webhooks, passes executions through Redis to a worker pool, and workers read and write workflow data in PostgreSQL. Backups must cover PostgreSQL, and Redis holds in-flight work that you need to monitor. Expect on-call alerts on queue depth and worker health, not only on the web process.
Encryption key sharing and multi-main limits
In queue mode, the main instance's encryption key must be shared with every worker and webhook processor so they can decrypt stored credentials. Key rotation and secret distribution become a shared task across all those nodes. Multi-main high availability is limited to Enterprise self-hosted deployments, so confirm whether your uptime needs require that license before you design the cluster.
How the two platforms compare on features and controls
This table sets Langflow, an MIT-licensed builder for agents and RAG, beside n8n, a general automation platform under the Sustainable Use License. Read each row against your own use: AI-first product work favors the first column, while business process automation across many systems favors the second.
| Capability | Langflow | n8n |
|---|---|---|
| License | MIT license, per the langflow-ai/langflow repository metadata | Sustainable Use License: internal business or non-commercial use only; .ee files need an n8n Enterprise License |
| Core model | Visual builder with Python-customizable components and an interactive playground | Visual canvas with custom JavaScript or Python code inside workflows |
| Integrations | Built around LLM, agent and tool components; confirm the connector set you need against the current README | Advertises more than 1,500 integrations, and all plans include every integration |
| Agent and MCP support | Multi-agent orchestration, and flows can be deployed as an MCP server | AI nodes exist in the workflow canvas; confirm agent and MCP depth with n8n before relying on it |
| Observability integrations | Lists LangSmith, Langfuse and other observability integrations | Log streaming is included only in Business and Enterprise plans |
| Default database | SQLite by default | Queue mode uses PostgreSQL for workflow data |
| Production database guidance | Docs say PostgreSQL is better suited for production because of concurrent users, data integrity and scalability | PostgreSQL is the database for queue-mode deployments |
| Queue or multi-worker scaling | Kubernetes is documented for production-grade, high-availability deployments; confirm worker scaling details with the vendor | Queue mode passes executions through Redis to a worker pool; the encryption key must be shared with all workers |
| SSO and SAML | Confirm SSO options for your edition and version with the vendor | SAML is in Business and Enterprise plans only, and SSO and LDAP sit in paid plans |
| Multi-main high availability | Confirm with the vendor how multiple application instances are supported | Restricted to Enterprise self-hosted deployments; not in Community or Cloud |
What each license allows and what each paid edition adds
Langflow is released under the MIT license, according to the license metadata of the langflow-ai/langflow GitHub repository. In practice, that means you can run it inside your own infrastructure, modify it, and build internal or customer-facing products on top of it without a commercial license from the project. The MIT license does not impose the internal-use restriction that applies to n8n, so the question for Langflow shifts from "am I allowed to use this" to "who supports it and how much of that support do I need to buy."
- Permissive terms: The MIT license places no internal-use limit in the license text itself, so commercial and customer-facing use is not restricted by the license.
- Custom components: Python components you write sit in your own codebase and are governed by your own license choices.
- No paid edition in the supplied facts: No paid Langflow edition is listed in the researched facts, so do not assume one exists or budget for one.
- Support is a separate question: Any commercial support, hardened distribution or SLA must be confirmed directly with the vendor or a services provider.
- Release cadence: Langflow v1.12.6 was published on October 13, 2026, with weekly patch releases through September and October 2026, so plan for a steady upgrade rhythm.
Confirm with the Langflow maintainers or your chosen partner whether any commercial support tier exists, and what it covers, before you write it into a procurement plan.
n8n is distributed under the Sustainable Use License, which allows use or modification only for your own internal business purposes or for non-commercial or personal use, and distribution only free of charge for non-commercial purposes (n8n LICENSE.md). This is a source-available license, not an open-source license, and it changes what you can do with n8n when your product is the workflow platform itself. Using n8n to automate your own business processes is the intended case, while offering n8n-powered automation as a service to third parties needs a closer reading.
- Internal use: Running n8n for your own business processes fits the license's stated purpose.
- Files with .ee: Files with .ee in their name or directory are not covered by the Sustainable Use License and require a valid n8n Enterprise License.
- Community edition: The Community edition is free and self-hosted, and the pricing page presents it as the no-cost option.
- Starter, Pro and Business prices: Starter and Pro cloud plans cost 20 euros and 50 euros per month billed annually, and the Business plan costs 667 euros per month billed annually and is currently self-hosted only (n8n pricing page).
- Business and Enterprise features: SSO and LDAP, environments with Git, and log streaming are included only in Business and Enterprise, and multi-main setup is restricted to Enterprise self-hosted deployments (n8n pricing page and queue mode docs).
Have counsel check your intended use against the Sustainable Use License, especially if you plan to resell automation, embed n8n in a product or distribute it to customers, and confirm current plan terms on the n8n pricing page before you commit.
What drives the running cost of each platform
Running Langflow or n8n costs more than the software itself. Most of the spend sits in infrastructure, the people who build and operate each platform, and the subscription or support terms that apply to your use. The cards below separate one-time setup work from recurring costs and list what to request in a written quote.
Infrastructure that recurs every month
Both platforms need compute, PostgreSQL and Redis for production use. n8n queue mode adds worker and webhook processes that share one encryption key and read and write PostgreSQL. Langflow ships with SQLite by default, and its docs say PostgreSQL is better suited for production. If you build RAG flows, add the vector store and any observability stack such as Langfuse, which self-hosts with PostgreSQL, ClickHouse, Redis or Valkey and S3-compatible storage. Ask the quote to price each of these components separately.
Skilled labor for each platform
Langflow work is mostly Python: custom components, flow debugging and the deployment of flows as APIs or MCP servers. n8n work usually falls to workflow builders who know its canvas, plus operators who can run Node.js services, manage queue-mode workers and rotate the encryption key without losing access to stored credentials. Ask for the expected mix of Python engineers, workflow builders and platform operators, and whether the quote covers knowledge transfer to your own staff.
Subscription and support terms
n8n's Community edition is free to self-host, but its pricing page lists Starter and Pro cloud plans at 20 euros and 50 euros per month billed annually, a Business plan at 667 euros per month billed annually that is currently self-hosted only, and Enterprise at custom pricing. SSO, environments with Git and log streaming need Business or Enterprise. Langflow's supplied facts list no price for any tier, so ask the vendor directly about paid support or hosted options.
Upgrades and licensing exposure
Langflow ships weekly patch releases, so budget recurring time for testing and upgrades. n8n's Sustainable Use License limits commercial use, and any .ee code needs an n8n Enterprise License, so a change in how you use or distribute n8n can create a license cost after go-live. Ask the quote to state who tests each upgrade, how many environments are covered and what happens to your license if your use case changes.
How to move flows from n8n to Langflow or back
This sequence gives you a tested, cut-over plan for moving AI logic and integrations between n8n and Langflow, with a clear record of what was rebuilt, what stayed in place and what still needs manual handling.
Inventory workflows, credentials and triggers
The automation owner lists every n8n workflow, its triggers, schedules, webhooks, downstream systems and the credentials it uses, then marks each as AI-related, integration-only or mixed. Credentials are recorded by name and owner, not by value. Because n8n stores credentials in its database and encrypts them with the main instance key, plan to re-enter secrets in the target tool rather than expect them to move. The step is done when every workflow has a row in the inventory with its owner and dependencies.
Separate AI logic from integration plumbing
A solution architect reviews each mixed workflow and splits it into two groups: model calls, prompt chains, retrieval and agent reasoning, which belong in Langflow flows, and system-to-system steps such as CRM updates, ticket creation or scheduled data pulls, which may stay in n8n. Keeping the plumbing where the connectors already exist usually reduces rebuild work. The step is done when each workflow has a written decision on what moves and what stays.
Rebuild agent and RAG steps as Langflow flows
An AI engineer recreates the model, prompt, retrieval and tool-calling logic as Langflow components, using Python-customizable components where a built-in one does not fit. Model provider settings and vector store connections are configured again in the target environment. The step is done when each rebuilt flow runs against a fixed set of test prompts and returns outputs that the business owner has reviewed against the original n8n results.
Rebuild integrations as components or keep them in n8n
For integrations you decided to move, the developer writes Langflow components or API calls and wires them into the flow. For integrations that stay, the n8n workflow is updated so it calls the new Langflow flow through its API or a webhook, where the endpoint and authentication method are confirmed in both tools. The step is done when every moved integration has a passing test against a non-production system and every retained integration calls the new endpoint successfully.
Move data stores and vector indexes
The platform team identifies any data the workflows read or write, including n8n's PostgreSQL execution data, Langflow's database and any vector index. Langflow defaults to SQLite, and its docs recommend PostgreSQL for production use, so set up the target database before moving anything. Embeddings may need to be regenerated if the embedding model or chunking changes. Confirm with each vendor which export and import paths exist for flows and data before you schedule any migration. The step is done when the target stores hold the agreed data and a sample query returns matching results.
Test parallel runs and compare outputs
The QA lead runs the rebuilt flows alongside the original n8n workflows on the same inputs for a agreed period, then compares outputs, error handling and logging. Check retries, timeouts and what happens when a model or external service fails, since these behaviors often differ between the two tools. Observability can be wired to LangSmith or Langfuse, both listed in the Langflow README. The step is done when the sign-off record shows matching outputs for the agreed test set and documented differences.
Cut over and retire the old path
The owner switches production traffic to the new flows, keeps the original n8n workflows disabled but available for a fallback window, and updates runbooks, access lists and monitoring. Before cutover, confirm the license position for each tool in your environment, since n8n's Sustainable Use License limits commercial use, and check whether any n8n Enterprise features you relied on, such as SSO or queue mode, have equivalents. The step is done when the new path has handled live traffic, the rollback steps are tested and the old workflows are archived under a documented retention decision.
Where Langflow and n8n projects usually run long
Most of the schedule risk on a Langflow project sits in the move from the default SQLite database to PostgreSQL. The Langflow custom database documentation says PostgreSQL is better suited for production because of concurrent users, data integrity and scalability, so the database change, its connection settings and the backup plan belong in the first sprint, not after the flows are built. Teams then need to set LANGFLOW_SUPERUSER_PASSWORD for release packages and Docker images, which disable automatic login by default, and place the instance behind Caddy or Nginx with HTTPS. If the platform will serve more than a pilot group, Kubernetes with high availability is a separate design task. Ask your team to confirm which of these three paths they will run before anyone estimates the work.
Most of the effort in an n8n project sits in queue mode and identity. In queue mode, the main instance handles triggers and webhooks and hands executions through Redis to workers that read and write PostgreSQL, and the main instance's encryption key must be shared with every worker and webhook processor so they can open stored credentials. Get that key handling, the worker count and the Redis sizing written down before rollout, since changing them later touches every node. If you need SAML single sign-on, confirm early that your edition includes it, because the n8n pricing page places SSO and LDAP in the Business and Enterprise plans. Multi-main high availability is limited to Enterprise self-hosted deployments. Have legal review the Sustainable Use License against your intended use, including any customer-facing deployment, before the production design is locked.
Three delays appear on both platforms. The first is unclear AI evaluation criteria: a team that has not agreed on the test set, accuracy thresholds and sign-off owner for a RAG or agent flow will keep rebuilding it. The second is credential sprawl, where API keys for model providers, databases and SaaS tools end up stored in flows, workflow nodes or personal accounts, and the cleanup then competes with go-live. The third is identity integration arriving late, after the builders and users are already working with local accounts. A fixed-scope written plan removes much of this drift because scope, deliverables, the credential storage approach, the identity integration and the acceptance tests are agreed before work starts, so the open questions are answered at the start rather than at cutover.
Questions buyers ask before choosing Langflow or n8n
Can our own team deploy Langflow or n8n without outside help?
Yes, if you already run containers, a relational database and identity integration in-house. Langflow installs as a Python package, a Docker image or Langflow Desktop, so a team comfortable with Python can start quickly. n8n runs as a Node.js application with optional queue mode, which adds Redis and PostgreSQL to the footprint. The harder work is rarely the first install. It is the production layer: HTTPS, backups, upgrades, secret handling, access control and documentation that an auditor or internal security team will ask for. Ask yourself whether someone on your team will own those tasks after go-live. If not, that gap is what an implementation partner would cover, under a written scope.
What drives the cost of running Langflow or n8n ourselves?
Software licensing is only one line. For n8n, the self-hosted Community edition is free, while the Business plan is listed at 667 euros per month billed annually and is currently self-hosted only, according to n8n's pricing page. Langflow is MIT licensed, so its software cost is zero, but infrastructure, database hosting, staff time and any paid add-ons still apply. Costs rise with the number of flows in production, the number of concurrent users, the need for high availability and the number of environments you maintain. Each one-time item (design, integration, hardening) differs from recurring items (hosting, patching, support retainers). Request a quote that separates these components.
How long does a first Langflow or n8n deployment take?
The supplied research does not give a typical duration for either platform, so confirm the timeline with the vendor, your own environment or the written scope rather than relying on a general figure. The main variables are the number of integrations, whether you need SSO through an edition that includes it, how many flows or workflows move into production, the database and network design, and the testing your change process requires. A proof of concept on a single host is a different project from a production build with high availability. Ask for milestones, dependencies on your team and the acceptance criteria before accepting a date.
What does the work need from our internal team?
Plan for named owners on both the platform and the surrounding services. For Langflow, that means people who can write and review Python custom components and maintain the deployment behind Caddy or Nginx or on Kubernetes. For n8n, it means people who can manage workflow code nodes, the database, Redis and the encryption key. In n8n queue mode, the main instance's encryption key must be shared with every worker and webhook processor so they can read stored credentials, so key handling needs a documented process. Your team also needs to approve identity design, data flows to model providers and the backup and restore test plan. Agree these responsibilities in writing before work begins.
Does n8n's license permit our commercial product?
Probably not if the product is sold to customers and n8n is part of what you ship. n8n's Sustainable Use License allows use or modification only for your own internal business purposes or for non-commercial or personal use, and distribution only free of charge for non-commercial purposes, according to its LICENSE.md. Files with .ee in the name or directory require a valid n8n Enterprise License. If you plan to embed n8n in a customer-facing offering, get a written interpretation from n8n and your legal counsel before design work starts. Langflow's MIT license has different terms, so the same question does not carry over automatically.
Does Langflow need PostgreSQL in production?
Langflow does not strictly require PostgreSQL to start, but its documentation says PostgreSQL is better suited for production because it supports concurrent users, data integrity and scalability. Langflow uses SQLite by default, which suits a single-user evaluation or a small pilot. For a shared deployment, plan to configure a PostgreSQL database and test failover and backup restore against it. The Langflow custom database documentation explains how to set this up. Confirm the exact configuration, supported versions and any migration steps with the current release notes before you commit to a design.
How do we get a quote for implementing either platform?
DeployOpen does not publish a price list. Most engagements are fixed-scope, so a quote follows from a written scope that sets out objectives, deliverables, environments, integrations, timelines and responsibilities on both sides. To make the quote comparable, list the platform you are considering, the hosting target (cloud account, data center or air-gapped network), the identity provider, the expected number of users and workflows, and whether you need security testing or compliance evidence. Ongoing support, upgrades and continuous testing are available as retainers, which you should price separately from the initial build.
How DeployOpen works on this
DeployOpen plans, implements, customizes, integrates, hardens, backs up, upgrades, documents and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. For Langflow vs n8n, we start by mapping whether your work is AI-first agent and RAG products or business process automation, then build only the platform that fits that scope.
How we scope
- Written scope names which workflows, integrations and data sources the engagement covers, before any work starts.
- We confirm your n8n usage against the Sustainable Use License, including whether internal use or non-commercial terms apply to your team.
- We agree on the Langflow component inventory, identity requirements and database choice in writing, so the quote follows from defined deliverables.
How we build
- We install Langflow with PostgreSQL in place of the default SQLite, with HTTPS in front of the service through Caddy or Nginx.
- We build or port custom Python components and test flows in a staging environment that mirrors your production network.
- For n8n, we configure queue mode with shared encryption keys across main, worker and webhook nodes, and document each setting.
How we hand over
- You receive runbooks covering deployment, backup, upgrades and the superuser password setup for your chosen platform.
- We document the integration map, credential handling and observability connections, including any LangSmith or Langfuse links.
- Handover includes a test record showing flows or workflows running end to end in your own infrastructure.
How we support
- Ongoing support, upgrades and continuous testing are available as a retainer after handover, sized to your written scope.
- We track Langflow and n8n release notes and plan upgrades in your maintenance window, with rollback steps documented.
- If you later move between the two platforms, we scope the migration in writing, starting from an inventory of flows and workflows.
