Dify vs n8n: when to choose each for internal AI tools
If an internal AI tool is on your roadmap and both Dify and n8n are on the shortlist, the first question is whether you are building an app or a workflow. Dify fits when the tool is centered on a model, a knowledge base and a user-facing interaction: RAG applications, chat assistants that answer staff questions from internal documents, and agent apps where the model decides which tools to call within a bounded set. Its Docker Compose deployment bundles PostgreSQL, Redis, Weaviate, Nginx and a code sandbox alongside the API, worker and web services, and the Dify Docker Compose guide asks for at least 2 CPU cores and 4 GiB of RAM as a starting point. Plan for that stack as one unit to operate, not a single container.
n8n fits when the main job is moving data across many systems on a schedule or on events, such as pulling tickets from a service desk, enriching records from an HR system, posting results to a chat channel and writing to a data store, with LLM steps placed inside the flow for summarization, classification or extraction. Its strength is integration breadth and visual workflow design, so the effort scales with the number of connectors and the complexity of the flow. If the tool is mostly a conversation with a knowledge base, n8n will work but you will build chat, retrieval and memory yourself, which is usually more work than Dify asks for.
The decision turns on three things. First, whether the tool is an app or a workflow. Second, the license terms for your use: Dify uses a modified Apache License 2.0 under which operating a multi-tenant environment from its source or removing the logo or copyright in the console requires a commercial license from LangGenius, while n8n is under the Sustainable Use License, which permits use only for internal business purposes or non-commercial use, and its .ee files require a valid n8n Enterprise License. Confirm with counsel whether an internal tool used by partners, contractors or customers falls inside those terms. Third, who maintains it. Ask whether your platform team can run upgrades for either stack (Dify 1.17.1 and n8n 2.43.7 are current stable lines at the time of writing) and who owns the prompts, retrieval settings and credentials after go-live.
Which internal AI tool fits Dify and which fits n8n
This table maps common internal AI scenarios to the platform that usually fits better, based on whether the work centers on building a conversational or retrieval app or on orchestrating steps across systems. Where both platforms can handle a scenario, the tipping factor is named so you can decide with your own team and licensing position in mind.
| Scenario | Better fit | Why |
|---|---|---|
| Knowledge assistant over internal documents | Dify | Dify is built around RAG apps, with dataset ingestion, retrieval and a chat interface in one product. Confirm the document types and retrieval settings you need against the current release. |
| Employee support bot with a defined knowledge base | Dify | Prompt, knowledge and agent configuration live together, so support staff can adjust behavior without rebuilding flows. Both can work if the bot mainly routes tickets, which tips toward n8n. |
| Document intake with approvals | n8n | Intake, routing, human approval steps and downstream system updates are workflow orchestration tasks. Dify can classify documents, but the approval chain is usually easier to keep in n8n. |
| Scheduled reporting that calls an LLM | n8n | Scheduling, data pulls from several sources and delivery to email or chat are native workflow strengths. Both fit if the report is a single prompt over one dataset, which tips toward Dify for simplicity. |
| Cross-system automation with an AI classification step | n8n | The main work is moving data between systems, with the model as one node. Choose n8n for this pattern, and confirm that its integration coverage includes your specific systems. |
| Internal chatbot exposed to many staff with branding needs | Both fit, tipped by licensing | Dify's license requires a commercial license for multi-tenant operation from source and for removing the logo, while n8n's Sustainable Use License limits use to internal business or non-commercial purposes. Have counsel confirm how each license applies to your deployment. |
| Prompt-driven agent that calls internal APIs | Both fit, tipped by who builds it | If application builders own the agent logic, Dify suits that workflow. If integration engineers own it and need many connectors, n8n usually suits that workflow better. |
| Workflows that embed a chat app for end users | Dify | Dify exposes an application layer for end users, whereas n8n is primarily an automation tool and end users seldom interact with its editor directly. Confirm the embedding options in the version you deploy. |
How Dify and n8n are built and what that means for operations
Dify and n8n both run as self-hosted services, but they differ in how many moving parts you own and where state lives. Those differences shape patching windows, backup scope and what wakes someone up at night. The cards below compare the two stacks component by component so you can estimate the operating load before you commit.
Dify service topology
Dify's Docker Compose guide starts an API, a worker and a web service, alongside PostgreSQL, Redis, Weaviate, Nginx and a code sandbox. The current release is Dify 1.17.1, and the guide asks for at least 2 CPU cores and 4 GiB of RAM. Each container is a separate upgrade and failure point, so ask your team who owns the vector store and the sandbox during an upgrade.
Dify state and backup scope
Application definitions, user data and retrieval content sit across PostgreSQL and Weaviate, with Redis holding transient state. A usable backup means capturing both the relational database and the vector index so that restored knowledge bases match the metadata that references them. Confirm the restore procedure for your version in a test environment before you rely on it.
Dify code sandbox risk
The code sandbox runs code produced by workflow nodes, which makes it the component most likely to need separate network controls and resource limits. Treat it as a distinct security boundary in your threat model and patch it on its own schedule. Ask the vendor how sandbox images are versioned and which privileges the container needs in your orchestration platform.
n8n workflow engine model
n8n is a workflow engine, so its runtime is shaped by how many workflows run, how often they trigger and how long they hold data between steps. Execution history, credentials and workflow definitions live in its own database, and the operational weight grows with run volume rather than with the number of AI features. Confirm component counts, queue mode use and sizing for n8n 2.43.7 against your expected trigger load.
n8n patching and on-call work
Because n8n runs your automations and holds integration credentials, a failed upgrade can stop business processes that have nothing to do with AI. Plan maintenance windows around scheduled workflows and test each release against your critical integrations. On-call work usually centers on stalled executions, expired credentials and failing external endpoints, so set alerts on execution failures before you go live.
How Dify and n8n compare on RAG, agents, integrations and deployment
This table compares Dify and n8n by purpose, AI app building, workflow automation, connectors, model providers, self-hosting and current release. Read each row with its condition in mind, because the right platform depends on whether your main need is building AI applications or automating business processes across many systems. Confirm connector and provider coverage against the current release before you commit.
| Area | Dify | n8n |
|---|---|---|
| Primary purpose | Open-source LLM application builder for chatbots, RAG apps and agent workflows | General workflow automation platform that connects business systems, with AI nodes added for LLM steps |
| RAG and agent app building | Built-in RAG pipeline and agent app types; the Docker Compose stack includes Weaviate as the vector store | AI nodes can call models and vector stores, but RAG and agent apps are assembled from workflow steps rather than provided as a dedicated app type |
| Workflow automation | Workflows exist for AI app logic; broad business process automation is not its core focus | Core strength: event triggers and workflow steps across many business applications |
| Integration breadth | Narrower set of connectors aimed at AI data sources and model APIs; confirm the sources you need | Large catalog of application connectors; confirm each connector you depend on in the current release |
| Model provider support | Confirm the specific providers and local model endpoints you need in the current release | Confirm the specific model nodes and providers you need in the current release |
| Self-hosted deployment | Docker Compose guide asks for at least 2 CPU cores and 4 GiB RAM and starts PostgreSQL, Redis, Weaviate, Nginx and a code sandbox alongside API, worker and web services | Self-hosted deployment is supported; sizing depends on workflow volume, execution history and connected systems, so confirm with your own load tests |
| Current release | Dify 1.17.1, released September 10, 2026 | n8n 2.43.7, published October 16, 2026 as the stable release |
What Dify's modified Apache 2.0 and n8n's Sustainable Use License allow
Dify uses a modified Apache License 2.0, so the base terms will be familiar to most legal teams, but two conditions change what you can do with the source. Operating a multi-tenant environment from Dify source requires a commercial license from LangGenius, and so does removing the logo or copyright in the Dify console. Read the full Dify LICENSE file before you plan any deployment that extends past a single organization's own use.
- Multi-tenant operation: If separate customers, business units or partners would share one Dify environment, the tenant model is the first thing to review against the license.
- Logo and copyright: Removing the logo or copyright notice in the console is a separate trigger, so a branded internal portal needs its own review.
- Single-organization use: Map each planned user group, including contractors, against the license text rather than assuming internal staff are the only users.
- Version drift: Dify 1.17.1 was released on September 10, 2026, so confirm the LICENSE file in the exact release you pin, since terms can change between versions.
Check whether any internal app you plan would present a tenant boundary or a branded product before you build on it.
n8n is distributed under the Sustainable Use License, which allows use and modification only for internal business purposes or non-commercial use. That grant is narrower than the permissive licenses many teams know, so the question is whether your planned use is internal business activity. The n8n LICENSE.md sets out the exact wording, and the terms apply to the code you run, not only to the product name.
- Internal business purposes: Automating internal AI tools for your own staff is the central case the grant describes, but the boundary of "internal" needs a written reading.
- Non-commercial use: Personal or non-commercial projects fall under the second grant, which does not cover a service sold or offered to clients.
- Enterprise files: Files with .ee in the name or directory need a valid n8n Enterprise License, so a deployment that touches those paths needs that license in place.
- Version pinning: n8n 2.43.7 was published on October 16, 2026 as the stable release, so record the LICENSE.md version that matches the build you run.
Confirm with n8n which of your workflows, nodes or add-ons touch .ee paths before you move anything into production.
The license texts answer some questions, but many deployment decisions depend on facts only your organization knows. These are the questions to put to LangGenius, to n8n and to your counsel. They are not legal conclusions, and the answers should be recorded in writing before the architecture is fixed.
- Customer-facing internal tools: Ask whether a tool that serves customers directly counts as internal business use under the Sustainable Use License, and whether the same answer holds for Dify's terms.
- Partner and contractor access: Ask whether a partner logging into a shared workflow, chatbot or knowledge base changes the analysis for either platform.
- Products built on the platform: Ask whether a product, managed service or client deliverable built on either tool falls inside the stated grants.
- Enterprise and commercial terms: Ask which license covers the paths, features or multi-tenant model you plan to use, and what your contract includes.
- Written confirmation: Have counsel review the license text for the exact release you deploy, and keep vendor answers on file.
Get these answers in writing before customers or partners depend on the system, because a later change of platform will cost more than the licensing conversation.
How identity and access integration differs between Dify and n8n
Identity decides who can change what in an internal AI tool, so confirm the identity controls of each platform before you pilot either one. In Dify, the questions are who can create or edit apps and workflows, who can attach a knowledge base and which users can query it. In n8n, the questions are who can build or publish workflows, who can view or reuse stored credentials for connected systems such as mail, file stores or CRMs, and who can run flows that act on those systems. A workflow that reads a document store with a service account can expose far more data than the chat interface suggests, so access to the builder and to the credential store matters as much as access to the end user.
No fact supplied for this page confirms single sign-on, SAML or role-based access control for either platform in any version or edition. Treat that as an open item. For Dify, check the current Dify documentation and the release notes for the version you intend to deploy, and ask the vendor which workspace roles exist, whether they can be mapped from your identity provider and whether knowledge base permissions are separate from app permissions. For n8n, check the feature set of the edition you license, because identity features are often tied to paid tiers, and confirm how credentials are encrypted at rest, who can export them and whether they can be rotated without editing every workflow. Write these answers down with the version number, since defaults can change between releases.
Test the answers in a pilot rather than accepting them from a demo. Create two test users with different roles, then confirm that the lower-privileged user cannot open another team's knowledge base in Dify, cannot view a credential in n8n, and cannot publish a workflow that calls a production system. Rotate one test credential and check which workflows break and whether the change is logged. Record which steps required manual work, because that work becomes an ongoing operations cost. If any control is missing in your chosen edition, plan a compensating control, such as a separate instance per data domain or a restricted service account, before the tool reaches real data.
What drives the running cost of Dify and n8n
Running Dify or n8n costs more than the license line on a quote. Infrastructure, staff time, license terms, upgrade cadence and outside support each shift the total, and most of them recur every month.
Infrastructure for the Dify stack
The Dify Docker Compose guide asks for at least 2 CPU cores and 4 GiB of RAM, and starts PostgreSQL, Redis, Weaviate, Nginx and a code sandbox alongside the API, worker and web services. That is several always-on containers with storage to size, so hosting cost recurs and grows with document volume and concurrent users. Ask your platform team to price that footprint in your own cloud or data center, including backups for the database and vector store.
Infrastructure for an n8n deployment
n8n is a single application service plus whatever database and queue topology you choose for production, so its baseline hosting is usually lighter than a RAG stack. Costs rise when you add worker processes for heavy executions or run many workflows against shared systems. Confirm the sizing for your execution volume with n8n's own guidance before you assume the lower figure holds.
Staff skills and tuning time
Dify work centers on prompt design, retrieval settings, chunking and model selection, which suits staff who are comfortable with Python-adjacent tooling and evaluation. n8n work centers on credentials, node logic, error paths and integration maintenance across SaaS APIs. Budget for the people who will own each platform after launch, since their hours are the largest recurring cost in most internal deployments.
License terms that may require a contract
Dify's modified Apache License 2.0 requires a commercial license from LangGenius for operating a multi-tenant environment from its source or removing the logo or copyright in the console. n8n's Sustainable Use License limits use to internal business or non-commercial purposes, and .ee files need an n8n Enterprise License. If you serve customers, partners or affiliates from either platform, ask counsel to confirm the license fit before you price the build.
Upgrade frequency and release churn
Both projects ship often. Dify 1.17.1 was released on September 10, 2026, and n8n 2.43.7 was published on October 16, 2026 as the stable release. Each upgrade needs regression testing of your flows or apps, and both products change APIs and nodes between versions. Plan a recurring window for upgrades and testing, and ask any vendor or partner how they test before they touch production.
External support and retainers
Support can come from in-house staff, the vendor's commercial offer, or an implementation partner. DeployOpen plans, implements, hardens, backs up, upgrades and supports self-hosted platforms in your infrastructure, mostly under fixed-scope agreements, and offers ongoing support and upgrades as retainers. DeployOpen does not publish a price list, so ask for a written quote that names the scope, deliverables, upgrade cycles, response expectations and what happens on a major version change.
Where Dify and n8n implementations usually run long
Implementation time on a Dify project usually goes to knowledge ingestion, retrieval tuning and answer evaluation, and those three stages slip most when nobody has agreed on what a good answer is. Ingestion depends on knowing which document sources are authoritative, how often they change and who may read them. Retrieval tuning means adjusting chunking, the embedding model and retrieval settings in the Dify console until results match the questions staff actually ask. Evaluation then needs a fixed question set with expected answers and a reviewer. Without those three inputs, teams tune endlessly because there is no finish line. Ask the business owner to sign off on a test set before the first document is loaded.
n8n projects run long for different reasons. Each workflow touches credentials for a CRM, a ticketing tool, a mail server or a database, and each credential needs a service account, scoped permissions and a rotation plan. Error handling is the step teams skip: a failed HTTP call, an expired token or a malformed payload should route to a defined path with alerting, not a silent stop. Testing then has to cover many integrations, each with its own sandbox or test tenant, which is often unavailable on the schedule the project assumed. Confirm test access for every integration in writing before build starts.
Projects on both platforms slip for the same non-technical reasons: unclear data sources, access rules that surface late, evaluation criteria nobody wrote down, and license questions raised after go-live. Dify's modified Apache License 2.0 and n8n's Sustainable Use License both restrict some commercial uses, so legal review belongs in the first week, not the last. A fixed-scope written plan reduces this risk because it names the sources, the access model, the acceptance tests and the license assumptions before any build work starts, and it turns each open question into a deliverable with an owner. Ask any implementation partner to put those items in the scope document.
Moving an internal AI tool from Dify to n8n or back
This sequence moves an internal AI tool between Dify and n8n without losing the prompts, data connections and access rules the business depends on. It ends with a verified cutover, a parallel-run record and a retired source platform.
Confirm license fit before any move
Before inventorying anything, the platform owner and legal or procurement lead confirm that the target platform's license allows the intended use. Dify's modified Apache License 2.0 requires a commercial license from LangGenius for operating a multi-tenant environment from Dify source or removing the logo or copyright in the console. n8n's Sustainable Use License permits use only for internal business purposes or non-commercial use, and files with .ee in the name or directory need a valid n8n Enterprise License. Confirm both against the current LICENSE files and your own use case. Done means a written note signed by the owner saying which features, editions and modifications are allowed on the target platform.
Inventory the app or workflow logic
The builder, usually a platform engineer with a domain owner, records every step the tool performs: the retrieval flow in a Dify app, or the trigger, nodes and branches in an n8n workflow, plus any agent tools, scheduled runs and error handling. Dify models a tool as an app with a prompt, knowledge base and tool calls, while n8n models it as a graph of nodes triggered by events. Write the logic as a plain-language flow diagram so both platforms can be checked against the same reference. Done means the diagram covers every input, output and branch, and the owner has signed off that nothing is missing.
Map data sources and credentials
List each connected data source, vector store, API and stored credential the tool uses. Record where each one lives, who owns it and how it is authenticated. Credentials should be reissued in the target platform's secret store rather than copied from one platform to the other, so that the old values can be revoked during decommissioning. Vector indexes built in Dify's Weaviate deployment or another store may need to be rebuilt from source documents when the target uses a different store. Done means a table of sources with owner, auth method and migration action, and each credential has a named replacement in the target environment.
Export prompts and build the evaluation set
Export prompts, system instructions, model settings and retrieval parameters as text and configuration files. Then assemble an evaluation set of representative questions with expected answers or acceptance criteria, drawn from real past usage with sensitive data removed. The evaluation set is the only neutral way to show that the rebuilt tool behaves like the original. Confirm with the business owner which answers must match and which may differ. Done means the evaluation set is stored under version control with a recorded baseline score from the source platform.
Define access rules and rebuild what does not transfer
Document who may use, edit and publish the tool, and map those roles to the target platform's permission model, since the two products handle workspaces, users and sharing differently. Expect to rebuild the workflow graph, agent tool definitions and any console-specific settings by hand, because prompts and data can be exported as files but orchestration logic is modeled differently on each platform. Keep identity provider integration and audit logging as separate checks. Done means a role matrix is approved, and a peer review confirms each rebuilt component matches the logic diagram.
Run both platforms in parallel
Run the source and target versions side by side on the same evaluation set and a controlled sample of live requests, with the source remaining the system of record for users. Compare outputs against the baseline, log errors, latency and any failed credentials, and fix differences in the target build. Confirm the logging and audit trail captures the same events on both. Done means the target meets the acceptance criteria agreed in step four across a full review period that the owner sets in advance, and every discrepancy has a recorded resolution.
Cut over and monitor
Switch users and scheduled jobs to the target platform in a planned window with a documented rollback path that returns traffic to the source. The platform owner runs the cutover checklist, and the on-call team watches the target for the agreed monitoring period. Done means users confirm access, scheduled jobs complete successfully, and no rollback trigger has fired.
Decommission the source and retain evidence
After a stable period, disable the source app or workflow, revoke the credentials it held, delete or archive its vector indexes and remove stored prompts from the old console according to your retention policy. Keep the evaluation records, access matrix, license note and change log as evidence for internal review or assessors. Done means a decommissioning record lists each revoked credential and deleted artifact, signed by the owner, and the archive is retrievable.
Dify vs n8n questions buyers ask before hiring help
What does implementing Dify or n8n for internal AI tools cost?
Cost depends on the scope agreed in writing, not on the software, which is free to download under each license's terms. The software line item is usually small, while the larger costs are the people who plan, configure, connect to your identity provider and data sources, harden, back up and document the platform. Dify also needs PostgreSQL, Redis, Weaviate and sandbox containers running alongside the API, worker and web services, so infrastructure and the people who run it are recurring costs. n8n costs shift with the number of integrations, credentials and workflows you need to govern. Ask any implementation partner for a fixed-scope quote that lists deliverables, environments, integrations, testing and handover, and separate one-time build work from ongoing support or upgrade retainers. DeployOpen does not publish a price list, and a quote follows from the written scope.
How long does a pilot take, and what sets the timing?
The timing depends on how many use cases the pilot covers, how many data sources it must reach, and how much approval your security and compliance teams require before production data is connected. A Dify pilot usually involves knowledge base ingestion, model provider configuration and agent or RAG testing, while an n8n pilot involves mapping each workflow to its systems, credentials and failure handling. No duration is given here because none is supported by researched facts. Ask for a timeline with milestones in the written scope, and check whether it assumes access, accounts and test data are ready on day one.
What does the work need from our own team?
Expect to supply named people for decisions that a partner cannot make alone. You will need an executive owner for the use case, an identity or access contact who can set up single sign-on and roles, a data owner who approves which documents or systems the platform may read, and an infrastructure contact for the cloud account, network, DNS, certificates and backups. Security and compliance staff should review the data flows and logging before go-live. Ask the partner to list these roles and the hours each one needs during the engagement, so the internal commitment is visible before work starts.
Can our internal team run Dify or n8n without outside help?
A capable platform team can run either tool, but the question is whether it has capacity for upgrades, backups, monitoring and security patching on a schedule. Dify ships frequent releases, with 1.17.1 published on September 10, 2026, and its Docker Compose deployment has several stateful services to keep healthy. n8n had its stable release 2.43.7 on October 16, 2026, and workflows need credential rotation and review as integrations change. Decide who owns each recurring task on paper. If nobody can own it, a retainer for upgrades and support is a common alternative to a one-time build.
Which license terms should our counsel review before we build?
Counsel should read the license for the exact tool and version you deploy, because the two are different. Dify uses a modified Apache License 2.0, and running a multi-tenant environment from its source or removing the logo or copyright in the console requires a commercial license from LangGenius. n8n uses the Sustainable Use License, which permits use or modification only for internal business purposes or non-commercial use, and files with .ee in the name or directory need a valid n8n Enterprise License. Ask counsel whether your internal AI tools, any customer-facing use, and any offering you resell fall inside those terms. This page does not give legal advice.
How does switching between Dify and n8n later affect the build?
Switching is costly when prompts, knowledge bases, agent logic and workflow definitions are stored in tool-specific formats that cannot be exported cleanly. Dify builds centre on applications, datasets and model settings, while n8n builds centre on node graphs, credentials and triggers, so the two rarely map one to one. Reduce switching cost by keeping source documents, prompt text, evaluation sets and integration contracts in your own repositories, and by documenting the business logic outside the tool. Ask any partner how exports, backups and rebuild effort are handled in the scope.
What security and data questions should we settle in the scope?
Settle where the platform runs, which data sources it may read, how model providers receive prompts, and who can see logs and stored conversations. Both tools can run inside your own infrastructure, which keeps data in your environment, but model API calls still leave it unless you use a self-hosted model. Ask the partner to state how credentials are stored, how access is reviewed, which logs are retained and where backups live. DeployOpen prepares systems, controls and evidence for compliance assessments, but it does not issue audit reports, attestations or certifications, so ask your independent auditor which evidence it will require.
How DeployOpen helps choose, implement or migrate between them
DeployOpen is a Chainscore Labs company that plans, implements, customizes, integrates, hardens and supports self-hosted open-source platforms inside your own cloud accounts, data centers or air-gapped networks. We start by writing down which internal AI tool you need, which platform fits it, and what your environment requires before any build work begins.
How we scope
- Map which internal AI workflows you need first: RAG assistants, agent tools, or scheduled data pipelines that call models
- Check your planned use against the Dify and n8n license terms, including whether any end user is outside your organization
- Confirm the hosting target, identity provider, model endpoints and data residency rules before estimating any component
- Agree deliverables, timelines and exclusions in writing, since most engagements are fixed-scope
How we build
- Deploy the chosen platform in your infrastructure, including the Docker Compose or Kubernetes components your version requires
- Connect models, vector storage and internal data sources through the integrations your team actually needs
- Configure access controls, secrets handling and network boundaries to match your security baseline
- Run security testing of the built applications and APIs, and document the findings alongside the fixes
How we hand over
- Deliver architecture, configuration and runbook documentation written for the team that will operate the system
- Prepare the upgrade and backup procedures for the exact versions deployed, so later changes are repeatable
- Walk your engineers through administration, workflow ownership and incident response before handover
- Document the license obligations that apply to your deployment so procurement and legal have a clear record
How we support
- Offer ongoing support, upgrades and continuous testing as a retainer after the initial handover
- Review platform release notes and license changes on a schedule you agree, and plan upgrades with you
- Revisit the scope if your usage changes, such as serving external users or moving from internal tools to a commercial product
- Ask us to confirm any capability or license detail against vendor documentation before it goes into your design
