How to Safely Migrate Workflows From Make to n8n

Introduction - What You'll Build

Growth-stage operations teams running production automation on Make (formerly Integromat) inevitably reach an architectural inflection point. Because Make's visual canvas naturally supports complex, multi-branch scenarios, your team has likely built highly sophisticated automation. You have outgrown basic point-to-point tools. However, as your operation scales, new ceilings emerge: the need for genuine, multi-step AI agent orchestration, strict data residency requirements demanding self-hosted infrastructure, and the compounding operational costs of a per-operation pricing model. Partnering with an expert n8n automation agency or managing this internally is often the next logical step to scale your infrastructure.

This Make to n8n migration guide provides a complete, de-risked playbook for transitioning your complex workflows to n8n. Unlike a Zapier migration—where you are moving from linear steps to a graph architecture—migrating from Make is structurally different. Make's modules and routers share a fundamental graph-based mental model with n8n's node architecture. This allows for high-fidelity migrations, provided you carefully rebuild logic rather than attempting hasty translations, ensuring robust n8n workflow automation from day one.

In this guide, you will learn how to systematically audit your existing Make scenarios, translate complex router structures into n8n Switch nodes, properly replicate Make's Iterator and Aggregator patterns using n8n's native batching, and execute a parallel validation phase to guarantee zero coverage gaps. Approaching this with proper custom n8n development practices is critical.

• Cost Reduction: Escape per-operation pricing, saving potentially 60-80% on infrastructure costs at high volume via self-hosted n8n.
• AI Capability: Upgrade from single-prompt OpenAI modules to n8n's advanced AI Agent node with dynamic tool-calling and RAG retrieval.
• Data Sovereignty: Achieve full compliance by controlling exactly where your automation data is processed.
• Zero Downtime: Execute a parallel cutover strategy that ensures critical business processes never drop a payload.

Technical Specifications:

• Difficulty Level: Intermediate to Advanced
• Time to Complete: 10-40 hours (depending on scenario volume and complexity)
• N8N Tier Required: Self-Hosted (recommended for TCO optimization) or n8n Cloud (Pro/Enterprise)
• Key Integrations: Make API, HTTP Requests, OAuth2 Configurations

Why Teams Migrate from Make to n8n

Before diving into the implementation, it is critical to ground this migration in specific business drivers. Make's visual canvas is undeniably excellent for mapping out business logic, and its native app directory is robust. We are not migrating because Make is ineffective; we are migrating because your operational requirements have exceeded its architectural constraints. Specialized n8n integration services exist primarily to solve these exact scaling roadblocks.

AI Capability Depth: Make handles single-step AI actions competently. You can send a prompt to OpenAI and route the response. However, n8n's AI Agent node supports genuinely multi-step, agentic behavior. When your business requires autonomous AI agents that can retrieve context from a vector store, decide which tools to call, execute them, evaluate the result, and iterate, n8n provides the necessary underlying framework for enterprise AI automation.

Self-Hosting and Data Control: Make is a cloud-only SaaS. For healthcare, financial, or strict B2B operations with rigorous data residency and compliance requirements, routing sensitive payloads through a third-party cloud is an absolute non-starter. n8n's self-hosted deployment provides total data sovereignty, a key requirement for modern n8n setup services.

Cost at Scale: Make's pricing scales linearly with your operation count. A scenario processing 50,000 records daily through an iterator, three API calls, and an aggregator consumes hundreds of thousands of operations rapidly. Modeling this against self-hosted n8n—where you pay a flat rate for infrastructure (e.g., $100/month for a robust AWS instance) regardless of execution volume—reveals massive TCO advantages for growth-stage companies.

Prerequisites

To execute a secure and comprehensive migration, ensure you have the following tools, accounts, and knowledge domains prepared before beginning Step 1.

Tools & Accounts Needed

• Active Make Account: Admin-level access is required to view, export, and document all existing production scenarios, including hidden error-handling paths.
• Target n8n Instance: Self-hosted n8n (recommended for the cost and data-control benefits discussed) or an n8n Cloud instance. Ensure you are running version 1.0 or higher to utilize the latest AI and execution features.
• API Credentials: Administrative access or explicit API keys/OAuth credentials for every third-party system your Make scenarios currently touch (e.g., Salesforce, Airtable, Slack, Stripe).
• Staging Environment: A designated staging window where both platforms can run parallel executions against test data or isolated environments.

Skills Required

• Deep understanding of HTTP requests, webhook payloads, and REST API structures.
• Familiarity with JSON data structures and array manipulation.
• Understanding of Make's specific module architecture (Iterators, Aggregators, Routers).
• Basic knowledge of n8n node configuration, specifically the Switch, IF, Loop, and Merge nodes.

Workflow Architecture Overview

A production migration is not a copy-paste exercise; it is an architectural translation. Make and n8n share a visual, node-based graph structure, which accelerates the mapping process, but their data iteration paradigms differ significantly.

Visualizing the migration architecture reveals a sequential, multi-phase pipeline. We do not decommission Make until the n8n equivalent is actively processing payloads flawlessly.

1. Scenario Inventory & Router Mapping: Documenting every trigger, branch, and error handler in Make.
2. Prioritization: Sequencing the rebuild based on operational risk, business criticality, and logic complexity.
3. Rebuild in n8n (Module-by-Module): The core technical phase where Make modules are mapped to n8n nodes (e.g., mapping a Make Router to an n8n Switch node).
4. Parallel Run & Validation: Routing identical staging payloads to both Make and n8n to compare execution outputs and data mutation integrity.
5. Cutover: Redirecting production traffic exclusively to n8n.
6. Make Decommission: Archiving and disabling the legacy Make scenarios safely.

The critical data flow consideration in this architecture revolves around array processing. Make automatically splits arrays into distinct bundles via Iterators, processing each module subsequently per bundle, before an Aggregator recombines them. n8n relies on explicit Loop nodes or native item batching. Understanding this distinction is the key to a successful architectural transition.

Step-by-Step Implementation

Step 1: Full Make Scenario Audit, Including Router Logic

What We're Building: A comprehensive source-of-truth inventory mapping exactly what your current Make infrastructure does. This defines the scope of the rebuild and identifies complex dependencies before you write a single expression in n8n.

Detailed Instructions:

1. Create the Inventory Matrix: Set up a spreadsheet or Airtable base. Columns must include Scenario Name, Trigger Event, External APIs Used, Average Monthly Operations, and a dedicated field for "Router/Branch Count."
2. Document the Main Logic Path: Open each Make scenario. Document the sequence of the primary "happy path" modules. Note the specific trigger (e.g., Stripe Webhook for charge.succeeded).
3. Examine All Router Branches: This is critical. Make's canvas encourages building extensive router branches. Click into the filter icon (the dotted line connecting the Router to the next module). Document the exact conditional logic (e.g., Total Amount > 1000 AND Currency = USD).
4. Identify Error Handling Routes: Look for modules with transparent/hollow connector lines indicating an Error Handler (Ignore, Resume, Rollback, Break, etc.). Make handles errors at the module level. Document exactly what happens when an API call fails in the current setup.

Pro Tip: The most common mistake in a Make-to-n8n migration is documenting only the main path. Visually complex Make scenarios often hide their edge-case routing in tiny filter conditions. Miss these, and your n8n workflow will fail in production edge cases.

Step 2: Prioritization — What to Migrate First

What We're Building: A strategic roadmap that dictates the order of scenario reconstruction, minimizing operational risk while building team proficiency in n8n's specific node paradigms.

Detailed Instructions:

1. Isolate Simple Linear Flows: Identify scenarios with a single trigger, zero routers, and standard API actions (e.g., Webhook -> CRM Create -> Slack Notification). Tag these as "Phase 1: Immediate." Migrating these first validates your webhook routing and credential setups.
2. Identify High-Cost Operational Hogs: Locate scenarios that burn massive operation quotas in Make (usually those leveraging large Iterators/Aggregators). Tag these as "Phase 2: High Value." Moving these yields immediate financial ROI.
3. Flag Complex Routing and Make-Specific Features: Scenarios with 4+ router branches, complex Data Store utilization, or intricate Map/Reduce formulas in Make should be tagged "Phase 3: Advanced." These require architectural redesign rather than direct translation.

Test This Step: Review your prioritized list with operations stakeholders. Ensure no Phase 1 migration acts as a dependency for a Phase 3 process. Success looks like a universally agreed-upon schedule prioritizing risk mitigation.

Step 3: Rebuild — Mapping Make's Module Structure to n8n Nodes

What We're Building: The literal recreation of business logic. We are converting Make's proprietary module behaviors into n8n's standardized execution engine. Because both platforms are graph-based, the physical layout will look similar, but the underlying data processing requires specific n8n node configurations.

Node Configuration & Mapping Matrix:

Make Paradigm	n8n Node Equivalent	Architectural Difference
Router	Switch Node	Switch evaluates all rules on a single node; Router visually splits into distinct physical pathways immediately.
Filter (between modules)	IF Node or Switch Node	n8n uses explicit nodes for logic gating rather than invisible edge conditions.
Iterator	Loop Node / Native Batching	n8n passes arrays inherently. Explicit loops are only needed when subsequent nodes cannot process arrays natively.
Aggregator	Merge Node / Item Lists	n8n recombines items using structural nodes rather than implicit data collectors.

Detailed Instructions for Rebuilding a Complex Router:

1. Add the Switch Node: In n8n, add a Switch node. This replaces your Make Router.
2. Configure Data Type: Set the Data Type to the type of value you are evaluating (e.g., String or Number).
3. Define Routing Rules:
   • Rule 1 (Make Branch 1): Set Value 1 to ={{ $json.status }}. Set Operation to Equal. Set Value 2 to won. Output to 0.
   • Rule 2 (Make Branch 2): Set Value 1 to ={{ $json.status }}. Set Operation to Equal. Set Value 2 to lost. Output to 1.
4. Configure Fallback: In Make, unmatched bundles disappear. In n8n, you must configure the Switch node's Fallback Output. Set it to route unmatched data to a logging node or terminate the flow securely.

Detailed Instructions for Replacing Iterator/Aggregator Pairs:

1. Evaluate Array Needs: In Make, receiving a webhook with 10 line items requires an Iterator to process each. In n8n, if your target node (e.g., Airtable Create) supports batch operations, you do not need a Loop. Map the array directly.
2. Implement the Loop Node (If Required): If the destination API requires singular requests:
   • Add a Loop node.
   • Set Batch Size to 1.
   • Connect the Loop output to your HTTP Request node.
   • Connect the HTTP Request output back to the Loop node's input to complete the cycle.
3. Recombine Data (Aggregation): Once the loop finishes, the Done output of the Loop node provides the final execution data, eliminating the need for a separate Make Aggregator in many architectures.

Pro Tip: Avoid the trap of literal, 1:1 translation for iterators. A literal translation often produces working but inefficient n8n workflows. Always leverage n8n's native ability to process arrays of items where possible; it executes faster and uses fewer memory resources than explicit looping.

Step 4: Credential and Authentication Setup

What We're Building: Establishing secure, least-privilege authenticated connections to all third-party services. Credentials from Make cannot be exported or migrated; they must be re-authorized inside your new n8n integration services.

Detailed Instructions:

1. Audit Scope: Review the required Make connections. Do not blindly authorize full access. If your scenario only reads Airtable data, configure a Personal Access Token with read-only scope in n8n.
2. Configure Global Credentials: Navigate to n8n's Credentials tab. Add a new credential block (e.g., Salesforce OAuth2 API).
3. Input Variables: Supply the exact Client ID and Client Secret from your service provider. Ensure the n8n OAuth Callback URL is correctly registered in the third-party application.
4. Verify Refresh Handling: Test the connection. Keep in mind that Make and n8n handle token refresh timing slightly differently. Ensure n8n is configured to automatically refresh OAuth2 tokens prior to expiration to prevent silent failures during high-volume runs.

Step 5: Parallel Run and Validation

What We're Building: A robust QA phase where the new n8n workflow executes alongside the active Make scenario. This confirms absolute parity in business logic, specifically testing Make's router branch conditions against n8n's Switch outputs.

Detailed Instructions:

1. Duplicate Staging Traffic: If using a webhook, use a fan-out service (like AWS SNS or a basic proxy) to send the exact same payload simultaneously to the Make Webhook URL and the n8n Webhook URL.
2. Nullify Destructive Actions: In the n8n staging workflow, replace actual creation/deletion nodes (e.g., "Delete HubSpot Contact") with HTTP Request nodes pointing to a mock endpoint (like webhook.site) to prevent duplicate data creation in your production CRMs.
3. Validate Router Branch Execution: Deliberately push test payloads designed to trigger Make's secondary and tertiary router branches. Verify that the n8n Switch node routes the payload perfectly to the corresponding pathway.
4. Verify Error Logic: Simulate an API failure (e.g., passing an invalid email format). Verify that n8n catches the error gracefully, matching or exceeding Make's error-handler configuration.

Step 6: Cutover and Make Decommission

What We're Building: The final transition of production authority from Make to n8n, followed by safe archival of legacy infrastructure.

Detailed Instructions:

1. Reconnect Destructive Actions: Replace the mock endpoints in your n8n workflow with live production nodes.
2. Sequential Cutover: Do not cut over everything simultaneously. Pick one low-risk scenario. Update the source system to point its webhooks/triggers exclusively to the n8n production webhook URL.
3. Disable Make Scenario: Toggle the Make scenario to OFF. Do not delete it yet.
4. Monitor Confirmation Window: Watch the n8n execution log strictly for 48 hours. Ensure batch sizes handle production volume without memory spikes.
5. Archive Make Setup: After a 14-day confidence window, export the Make scenario blueprint as a JSON backup, store it in your company's documentation repository, and delete the Make scenario to permanently close the migration.

Complete Workflow JSON

To accelerate your migration, we have provided a foundational n8n workflow demonstrating the exact conversion of Make's most common complex pattern: Webhook Trigger -> Router (Switch) -> Iterator (Loop) -> API Call.

To import this into your n8n instance:

1. Copy the complete JSON block below.
2. In your n8n workspace, click the "..." menu in the top right corner.
3. Select "Import from Clipboard" (or "Import from JSON").
4. Connect your own HTTP Request credentials.

{
  "nodes": [
    {
      "parameters": {
        "httpMethod": "POST",
        "path": "migration-test-webhook",
        "options": {}
      },
      "name": "Webhook Trigger",
      "type": "n8n-nodes-base.webhook",
      "position": [240, 300],
      "webhookId": "dynamic-id-placeholder"
    },
    {
      "parameters": {
        "dataType": "string",
        "value1": "={{ $json.body.eventType }}",
        "rules": {
          "rules": [
            {
              "operation": "equal",
              "value2": "order_created",
              "output": 0
            },
            {
              "operation": "equal",
              "value2": "order_updated",
              "output": 1
            }
          ]
        },
        "fallbackOutput": 2
      },
      "name": "Switch (Make Router Eq)",
      "type": "n8n-nodes-base.switch",
      "position": [460, 300]
    },
    {
      "parameters": {
        "batchSize": 1,
        "options": {}
      },
      "name": "Loop (Make Iterator Eq)",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [700, 200]
    },
    {
      "parameters": {
        "url": "https://api.example.com/process",
        "method": "POST",
        "sendBody": true,
        "bodyParameters": {
          "parameters": [
            {
              "name": "itemId",
              "value": "={{ $json.id }}"
            }
          ]
        },
        "options": {}
      },
      "name": "HTTP Request (Process Item)",
      "type": "n8n-nodes-base.httpRequest",
      "position": [940, 200]
    }
  ],
  "connections": {
    "Webhook Trigger": {
      "main": [
        [
          {
            "node": "Switch (Make Router Eq)",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Switch (Make Router Eq)": {
      "main": [
        [
          {
            "node": "Loop (Make Iterator Eq)",
            "type": "main",
            "index": 0
          }
        ],
        [],
        []
      ]
    },
    "Loop (Make Iterator Eq)": {
      "main": [
        [
          {
            "node": "HTTP Request (Process Item)",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "HTTP Request (Process Item)": {
      "main": [
        [
          {
            "node": "Loop (Make Iterator Eq)",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

Note: You must configure valid authentication for the HTTP Request node before activating this workflow in production.

Testing Your Workflow

Because Make's graph allows for deep, nested logic, comprehensive testing of your n8n replacement is non-negotiable. Maintain a migration tracking sheet for each scenario, explicitly listing every router branch and its validation status.

Test Scenario 1: Typical Use Case (The Happy Path)

• Input: Standard JSON payload simulating a standard customer order (e.g., standard items array, standard payment status).
• Expected Output: Processed successfully; data routed to Branch 0; API calls return 200 OK.
• How to Verify: Check the n8n execution log. Confirm the Switch node correctly routed to Output 0. Verify the destination CRM/Database to ensure all fields mapped correctly (no "null" values where Make previously succeeded).
• What to Look For: Ensure array lengths match. If 5 items entered the Loop, confirm 5 distinct HTTP Requests were executed.

Test Scenario 2: Edge Case (Complex Routing)

• Input: Boundary condition payload—for example, an order where the total amount equals the exact threshold of your Switch condition, or an order with a missing optional parameter.
• Expected Behavior: Workflow should correctly evaluate the strict inequality. If the rule is > 100 and the value is exactly 100, it should fall back or route to the designated lower tier.
• How to Verify: Inject the payload directly into the Webhook node using n8n's test mode. Trace the execution path.
• What to Look For: The specific node output index. Misconfigured Make-to-n8n condition translations (e.g., using String match instead of Number match) often surface here.

Test Scenario 3: Error Condition (Failure Resilience)

• Input: Malformed API credentials or an intentionally invalid email format designed to force a 400 Bad Request response from the target system.
• Expected Behavior: The n8n HTTP Request node should fail, triggering the configured Error node logic (e.g., sending a Slack alert), exactly as Make's Error Handler module would.
• How to Verify: Trigger the payload. Confirm the workflow does not crash silently but routes via the node's Error output or to an Error Trigger workflow.

Production Deployment Checklist

Before decommissioning Make, ensure your n8n instance is hardened for production traffic. An n8n specialist can often help expedite this phase.

• Security Audit: Verify all OAuth credentials have least-privilege scope. Ensure self-hosted n8n instances are behind a reverse proxy (Nginx/Traefik) with valid SSL certificates.
• Error Notification Setup: Implement a global Error Trigger workflow in n8n. Make provides built-in email alerts for scenario errors; in n8n, you must configure a dedicated workflow triggered by the "Error Trigger" node to push alerts to Slack or PagerDuty.
• Concurrency Settings: If migrating high-frequency webhooks, configure your EXECUTIONS_PROCESS environment variable in self-hosted n8n (typically setting it to main for high performance or configuring workers via Redis).
• Data Pruning: Configure n8n's environment variables (EXECUTIONS_DATA_MAX_AGE) to automatically prune execution logs after 14-30 days to prevent database bloating, replacing Make's automatic data retention limits.
• Backup Strategy: Enable automated JSON exports of all n8n workflows to a GitHub repository utilizing the n8n CLI or a scheduled automation.

Optimization & Scaling

Make processes modules iteratively; n8n optimizes via bulk data processing. To truly leverage n8n's architecture, you must optimize for scale.

Performance Optimization

Replace explicit loops with native node batching. If you are migrating a Make Iterator that inserts 1,000 rows into PostgreSQL one by one, do not build a Loop node in n8n. Instead, pass the entire JSON array directly into the Postgres node and configure it to perform a bulk insert. This transforms 1,000 database operations (and 1,000 execution steps) into a single, highly performant node execution.

Cost Optimization

By moving to a self-hosted n8n architecture, you have already solved the per-operation cost trap. However, compute efficiency still matters. Reduce external API call frequency by caching static mapping tables inside n8n (using external memory or Redis) rather than requesting the same reference data from an API during every workflow execution.

Reliability Optimization

Configure specific error handling directly on critical nodes. In the node settings, navigate to "On Error" and select "Continue (Using Error Output)". This creates a dual-path architecture directly comparable to Make's error routing, allowing you to implement dead-letter queues where failed payloads are routed to an Airtable base for manual human review without crashing the primary execution chain.

Troubleshooting Guide

Issue 1: Switch Node Routing Mismatch

• Error Description: "A router branch that worked correctly in Make produces a different output path in the n8n Switch node."
• Root Cause: Make often coerces data types automatically (treating the string "100" and the integer 100 identically). n8n requires strict type matching. If you evaluate a string variable using a "Number" operation in the Switch node, it will fail to route correctly.
• Solution Steps:
  1. Open the Switch node configuration.
  2. Check the Data Type setting. If the incoming JSON payload delivers a string, ensure the Switch node is set to evaluate Strings.
  3. Alternatively, use a Code node before the Switch to cast variables explicitly (e.g., item.total = parseInt(item.total);).
• Prevention: Always pin incoming test data to the node and inspect the schema visually to confirm data types before configuring routing rules.

Issue 2: Iterator/Aggregator Batching Discrepancies

• Error Description: "The n8n equivalent of a Make Iterator/Aggregator pair produces different data counts or structural results at scale."
• Root Cause: Make handles array splitting implicitly. In n8n, the Loop (Split in Batches) node requires an explicit batch size. If your downstream API handles pagination or chunk limits differently, data can misalign.
• Solution Steps:
  1. Identify the API rate limit or payload size limit of the destination system.
  2. Adjust the Batch Size in the n8n Loop node to optimize chunking (e.g., processing 50 items per batch instead of 1).
  3. Ensure you are merging the output correctly using the Done branch of the Loop node, rather than accumulating data manually.

Issue 3: OAuth Credentials Failing in n8n

• Error Message: "Authentication failed: Invalid token or scope."
• Root Cause: Credentials that worked smoothly in Make may fail in n8n if Make was handling OAuth refresh logic behind the scenes in a proprietary manner, or if Make's native module requested a broader default scope than you explicitly requested in n8n.
• Solution Steps:
  1. Review the target API's developer documentation for exact required OAuth scopes.
  2. In n8n's credential setup, explicitly append the necessary scopes.
  3. Force a manual re-authentication by clicking "Connect" in the credential modal to generate a fresh token pair.

Issue 4: Memory Exhaustion on Large Payloads

• Error Message: "Workflow execution failed: Memory limit exceeded" (in self-hosted n8n).
• Root Cause: Unlike Make's cloud handling the compute, self-hosted n8n utilizes your server's RAM. Migrating a massive file-processing scenario without optimizing stream handling will crash Node.js.
• Solution Steps: Set the EXECUTIONS_PROCESS environment variable to main and ensure binary data is saved to disk (using N8N_DEFAULT_BINARY_DATA_MODE=filesystem) rather than keeping multi-megabyte files in active memory.

Advanced Extensions

Enhancement 1: Multi-Step AI Agents

Make users migrating to access superior AI capabilities should implement the n8n AI Agent node. Instead of hard-coding rigid IF/ELSE logic to determine how to categorize a customer request, you can pass the request to an Agent node equipped with a Custom Tool (e.g., "Check Inventory DB"). The Agent dynamically decides whether to query the database, summarizes the findings, and drafts the response, replacing 10+ modules of rigid Make routing with a single, highly intelligent custom n8n AI agent architecture.

Enhancement 2: Centralized Workflow Orchestration (Sub-Workflows)

Make scenarios can become monolithic and difficult to maintain. n8n solves this with the Execute Workflow node. You can extract common tasks (like standardizing customer data formats) into a standalone sub-workflow. Multiple primary workflows can invoke this sub-workflow. This modular architecture drastically reduces maintenance overhead and introduces enterprise-grade code reuse to your automation stack.

Enhancement 3: Global Error Handling Automation

Rather than configuring error paths on every single module as Make often requires, implement a Global Error Workflow. Assign this global workflow in your main n8n settings. If any execution fails unexpectedly, the Error Workflow receives the entire execution payload, the exact node that failed, and the error stack trace, allowing you to automatically create rich Jira tickets or Slack alerts with direct debugging links.

FAQ Section

Q: How long does a full Make to n8n migration typically take?
A: For growth-stage companies with 15-30 complex production scenarios, expect a 2-4 week timeline. This accounts for thorough scenario auditing, credential recreation, architectural logic redesign, and a mandatory parallel validation phase to ensure zero dropped data.

Q: How does Make's Router module translate to n8n?
A: Make's Router maps directly to n8n's Switch node. Instead of visually drawing lines out of a central module, you configure multiple conditional rules within the Switch node. Each rule dictates which numerical output path the data flows down, maintaining identical logic capabilities with cleaner node structuring.

Q: Can I migrate a Make scenario with an Iterator and Aggregator to n8n?
A: Yes. Make Iterators map to n8n's native batching capabilities or the explicit Loop node. Make Aggregators map to n8n's Merge node or the 'Item Lists' node. However, n8n often renders explicit iterators unnecessary if the destination node inherently supports processing JSON arrays directly.

Q: What's the biggest risk when migrating from Make to n8n?
A: The primary risk is failing to document Make's hidden error-handling routes or deep filter conditions attached to routers. Rushing the audit phase and migrating only the "happy path" guarantees production failures when edge cases arise. Detailed scenario audits mitigate this completely.

Q: Does n8n support the same visual complexity as Make's canvas?
A: Yes, n8n utilizes a visually complex, graph-based canvas that handles multi-branch routing effortlessly. While the visual aesthetic differs, n8n's canvas is built for enterprise complexity, allowing for deep zooming, node grouping, and visual sticky notes for documentation.

Q: Should I migrate from Zapier and Make to n8n at the same time if I use both?
A: It is highly recommended to migrate sequentially. Standardize your migration methodology first. Move linear Zapier workflows to establish quick wins and validate infrastructure, then tackle complex Make multi-branch scenarios once your team is comfortable with n8n's execution engine.

Q: How much does it cost to migrate from Make to n8n?
A: Moving to self-hosted n8n eliminates operation-based pricing. You will pay for hosting infrastructure (typically $40-$150/month depending on volume) rather than software tiers. The ROI is usually realized within the first 60 days for high-volume Make users paying for premium operation tiers.

Conclusion & Next Steps

Migrating from Make to n8n is a strategic infrastructure upgrade that transitions your operations from a constrained, per-operation model to a limitless, AI-native automation suite. By systematically auditing your router logic, translating iterators into performant loops, and validating in parallel, you can execute a flawless transition into advanced n8n workflow automation.

You now have a production-ready blueprint that ensures deeper AI capability, complete data sovereignty, and significant cost savings at scale.

Immediate Next Steps:

1. Create your definitive Make Scenario Inventory sheet, paying special attention to every router filter condition.
2. Stand up your n8n infrastructure (cloud or self-hosted) and establish your core API credentials.
3. Select one low-risk, mid-volume Make scenario and build its exact equivalent in n8n for a parallel execution test.

When to Consider Expert Help:
Complex enterprise requirements—such as establishing secure VPC deployments, refactoring massive hundreds-of-modules Make monoliths into modular n8n sub-workflows, or building bespoke AI agents—require deep architectural experience. If your migration risks impacting core revenue operations, partner with certified n8n experts. N8N Lab specializes in zero-downtime, enterprise-grade migrations and custom automation builds, ensuring your transition scales faster and more profitably. Reach out for a strategic consultation to eliminate operational drag permanently.