n8n vs Make: Automation Tools Compared for Your Business

Make Pricing Model

Make charges based on operations. Every step in your workflow that processes data counts as one operation. A five-step workflow that runs once uses five operations. The free tier includes 1,000 operations per month. Paid plans start around $9/month for 10,000 operations. The Core plan at $16/month includes more operations and features like multiple scenarios. The Pro plan adds custom variables, high-priority execution, and more operations.

The operation model means costs scale directly with usage. This is predictable for stable workloads but can surprise you if a workflow triggers more often than expected. A webhook-triggered workflow that processes form submissions could burn through operations quickly during a marketing campaign spike.

Advantage: predictable starting cost. No infrastructure to manage. You pay only for what you use. Disadvantage: costs increase linearly with volume. High-volume workflows get expensive. Enterprise pricing for teams with custom needs requires a sales conversation.

n8n Pricing Model

n8n Cloud starts with a free trial, then paid plans begin at around $20/month for 2,500 executions. The key difference: n8n counts workflow executions, not individual operations. A workflow with 15 steps counts as one execution regardless of how many steps it contains. This makes complex workflows dramatically cheaper per run than Make.

The self-hosted option changes the economics entirely. The community edition is free with some feature limitations. You pay only for the server infrastructure (a basic VPS costs $5-20/month). No per-execution pricing. No volume limits. For high-volume use cases, self-hosting saves thousands of dollars annually.

Advantage: complex workflows with many steps are cheaper. Self-hosting eliminates per-usage costs entirely. Disadvantage: self-hosting requires server management skills. The cloud version’s execution limits can be restrictive for the entry-tier plans. Enterprise self-hosted licensing adds cost.

The Real Math

For a small business running 5-10 simple workflows that each execute a few times per day, Make is likely cheaper and simpler. The operation costs stay manageable and you have zero infrastructure overhead.

For a business running complex workflows with many steps, or high-volume workflows that execute hundreds of times per day, n8n becomes significantly cheaper. A 20-step workflow that runs 100 times per day costs 2,000 operations per day on Make (60,000 per month) versus 100 executions per day on n8n (3,000 per month).

For teams with the technical skill to self-host, n8n at scale is essentially free beyond server costs. This makes it the clear choice for heavy automation users who want to remove per-usage pricing from the equation entirely.

Make AI Capabilities

Make offers pre-built modules for OpenAI (ChatGPT, GPT-4, DALL-E, Whisper), Anthropic (Claude), Google (Gemini), and other AI providers. Each module is configured through the visual interface with fields for model selection, prompt text, temperature, max tokens, and other parameters. No code required.

Make also has a native AI assistant that helps you build workflows. Describe what you want in natural language and the assistant generates a scenario structure. This is useful for getting started but the generated workflows usually need manual adjustment.

The integration depth is adequate for most business use cases: text generation, classification, summarization, image generation, speech-to-text, and embeddings. For advanced AI patterns like function calling, structured output, or multi-turn conversations with context, you may hit limitations in the visual interface.

n8n AI Capabilities

n8n has built AI as a first-class feature with its AI Agent node. This goes beyond simple API calls to AI providers. The AI Agent node lets you build autonomous agents that can reason about tasks, use tools (other n8n nodes), and take multi-step actions. You define the agent’s available tools, give it a goal, and let it determine the steps to accomplish it.

n8n supports LangChain integration natively, which opens up chains, retrieval-augmented generation (RAG), vector store queries, and memory for conversational agents. If you are building AI workflows that go beyond simple prompt-in-response-out patterns, n8n’s AI toolkit is significantly more advanced than Make’s.

The code node means there is truly no ceiling. Any AI API, any model, any prompt pattern, any post-processing logic — you can implement it directly in the workflow. This flexibility comes at the cost of requiring more technical skill, but for teams that have it, n8n enables AI workflows that are not possible in Make without external services.

Practical AI Workflow Examples

On Make: New customer support email arrives. Make scenario triggers. Email is sent to Claude for classification (billing, technical, general). Based on classification, the email routes to the appropriate team in your helpdesk. A draft response is generated and attached for the agent to review. All configured visually, no code.

On n8n: Same trigger. But the AI Agent node reads the email, searches your knowledge base for relevant articles, drafts a personalized response incorporating specific documentation links, checks the customer’s account status for context, and either sends the response automatically (for simple queries) or routes to a human with the pre-built response and context attached. The agent decides the path based on confidence level.

Step 1: Identify the Workflow

Pick a task you do manually at least once per day that follows a predictable pattern. Examples: saving email attachments to a specific folder, logging form submissions to a spreadsheet, sending a Slack notification when a new deal is created in your CRM, or generating a summary of daily customer support tickets. The task should involve two to three apps and a simple trigger-action pattern.

Step 2: Map the Steps

Write down exactly what happens at each step. What triggers the task? What data moves from where to where? What transformation happens to the data? What is the final output? This becomes your workflow blueprint. On Make, each step becomes a module. On n8n, each step becomes a node. The mapping is identical; only the implementation differs.

Step 3: Build and Test

Build the workflow step by step, testing each connection before adding the next. Both platforms let you run individual steps and inspect the data at each point. This incremental approach catches errors early. Trying to build the entire workflow and then debug it as a whole is frustrating and inefficient.

On Make: click each module and use the “Run this module only” option to verify data flows correctly. On n8n: use the “Execute Node” button to test individual nodes before running the full workflow.

Step 4: Add Error Handling

After the workflow works for the happy path, add handling for what happens when things go wrong. An API returns an error. A field is missing. A connection times out. On Make, add error handler routes to critical modules. On n8n, use the error trigger node and try/catch patterns. Good error handling is what separates a demo from a production workflow.

Step 5: Monitor and Iterate

Let the workflow run for a week. Check the execution logs daily. Note any failures and their causes. Fix edge cases. After a week of clean runs, you have a reliable automation. Then pick the next task and repeat. Building automation skills is iterative. Each workflow you build makes the next one faster.