n8n vs Zapier vs Make — I Built the Same AI Workflow in All Three

Table of Contents

• The Workflow I Built in All Three Platforms
• Building It in Zapier: Fast but Expensive
• Building It in Make: Visual Power at Lower Cost
• Building It in n8n: Developer Freedom, Self-Hosted
• Side-by-Side Comparison
• AI-Specific Features: Where It Really Matters
• Pricing at Scale: The Numbers That Change Everything
• Which One Should You Choose?
• Frequently Asked Questions

The Workflow I Built in All Three Platforms

To make this comparison meaningful, I needed a workflow that was complex enough to test each platform's AI capabilities but practical enough that you'd actually want to build it. Here's what I chose:

The "Email Intelligence Pipeline":

1. Trigger: New email arrives in Gmail matching a specific label ("Client Inquiries")
2. AI Step 1: Send the email body to Claude's API to extract key information (sender intent, urgency level, required action, relevant dates)
3. AI Step 2: Generate a draft response using Claude, tailored to the urgency and intent
4. Conditional Logic: If urgency is "high," post to a Slack channel immediately. If "medium" or "low," save the draft to Google Docs for review
5. Logging: Append a row to a Google Sheet with the email subject, classification, urgency, and timestamp

This workflow has five nodes, two AI calls, conditional branching, and three different output destinations — a realistic test that exercises each platform's strengths and weaknesses.

I've previously written about building 7 AI workflows that save 20 hours a week. This article goes deeper on the platform comparison rather than the workflow ideas themselves.

Building It in Zapier: Fast but Expensive

Build time: 15 minutes.

Zapier's biggest advantage is speed. The Gmail trigger was configured in about 60 seconds — select the account, pick the label, done. The Zapier interface guides you through each step with a wizard-like flow that's hard to get wrong.

For the AI steps, Zapier has a native "AI by Zapier" action that lets you write a prompt and get a response without configuring an external API. It runs on GPT-4o by default. I switched it to use Claude via the Anthropic integration, which required entering my API key and selecting the model. This took about 3 minutes.

The conditional logic was straightforward — Zapier's Paths feature handles if/else branching visually. I set up two paths: one for high-urgency emails (post to Slack) and one for everything else (save to Google Docs). The Google Sheets logging step was another 2 minutes.

What worked well:

• Fastest setup by far. If you value your time at $50/hour, the 15-minute build means Zapier costs about $12 in labor to set up.
• 8,000+ native integrations. Every service I needed was available without workarounds.
• Natural language workflow creation. You can describe what you want in plain English and Zapier generates a draft workflow. It got my workflow about 70% right on the first try.

What didn't work well:

• The Anthropic integration doesn't support prompt caching or batch processing. Every AI call costs full price.
• Error handling is basic. When Claude returned an unexpected format, the workflow just failed. Setting up retry logic required a workaround with a separate "error handler" Zap.
• Task-based pricing hurts for AI workflows. Each workflow execution counts as 5 tasks (one per step), and AI actions count as premium tasks.

Building It in Make: Visual Power at Lower Cost

Build time: 35 minutes.

Make (formerly Integromat) took longer to set up, but the extra time bought me a more sophisticated workflow. Make's visual canvas shows the entire workflow as a flowchart, with data flowing between nodes through clearly visible connections. For complex workflows, this visual approach makes debugging much easier.

The Gmail module worked similarly to Zapier's. The Anthropic/Claude module in Make is more configurable — I could set temperature, max tokens, and system prompts directly in the module settings without writing custom code. The built-in prompt engineering interface lets you test prompts against sample inputs before deploying.

The conditional routing in Make is handled through "routers" — visual fork points where you define filter conditions. I set up three routes (high, medium, low urgency) rather than just two, giving me more granular control. Each route can have its own error handler, which is a significant improvement over Zapier's workflow-level error handling.

What worked well:

• The visual builder is genuinely better for complex workflows. I could see the data flow at every step and trace exactly where a value came from.
• Operations-based pricing instead of task-based. My 5-step workflow counts as 5 operations (similar to Zapier), but operations are cheaper — Make's mid-tier plan offers 10,000 operations for $16/month versus Zapier's equivalent at roughly $50/month.
• Built-in data transformation. Make has powerful built-in functions for parsing text, formatting dates, and manipulating JSON without needing separate steps.
• Prompt engineering tools. The ability to test and iterate on prompts within the Make interface — rather than switching to the Claude console — saved time during development.

What didn't work well:

• Steeper learning curve. The interface is powerful but not immediately intuitive. A first-time user would need 2-3 hours to get comfortable with the concepts.
• Fewer integrations than Zapier (roughly 1,500 vs 8,000). Most popular services are covered, but niche tools sometimes require webhooks.
• The Claude integration, while more configurable, occasionally had latency issues — responses took 8-12 seconds versus 3-5 seconds in Zapier.

Building It in n8n: Developer Freedom, Self-Hosted

Build time: 25 minutes.

n8n splits the difference on build time. It took longer than Zapier because of the initial configuration (I self-host n8n on a small VPS), but the workflow itself came together quickly because n8n's AI nodes are more capable than either competitor.

The standout feature: n8n has roughly 70 dedicated AI nodes, including native LangChain integration. Instead of just "send a prompt, get a response," n8n lets you build AI agent workflows with memory, tool use, and multi-step reasoning — all within the visual workflow builder.

For my email pipeline, I used n8n's AI Agent node rather than a simple API call. The agent received the email, used a tool to search for previous emails from the same sender (context from a connected database), and generated a response that referenced prior conversations. Neither Zapier nor Make could replicate this without significant custom code.

The conditional routing was simple — n8n uses an IF node that supports complex conditions with AND/OR logic. The Slack, Google Docs, and Google Sheets nodes worked without issues.

What worked well:

• Self-hosted = unlimited executions at zero per-execution cost. My VPS costs $5/month (Hetzner CX22), and n8n runs alongside other services.
• AI Agent capabilities go far beyond simple API calls. Memory, tool use, LangChain chains — these are first-class features in n8n.
• Custom code nodes. When I needed to parse a non-standard email format, I dropped in a JavaScript function node. Try doing that in Zapier.
• Full execution visibility. Every execution shows the exact prompt sent, model response, and downstream effects. This is invaluable for debugging AI workflows where outputs are non-deterministic.
• You can tell n8n what you want in plain English and get a working workflow back, then refine it through chat.

What didn't work well:

• Self-hosting means self-maintaining. I had to update n8n manually, configure backups, and handle security. The cloud-hosted option ($20/month) eliminates this but adds execution limits.
• The initial setup took 20 minutes just to configure the self-hosted instance, Gmail OAuth, and API keys. This is a one-time cost, but it's real.
• Community is smaller. When I hit an issue with the AI Agent node, the forum had fewer answers than Zapier's help center. I ended up reading the source code on GitHub to understand the behavior.

Side-by-Side Comparison

AI-Specific Features: Where It Really Matters

All three platforms can call an AI API and return a response. The differentiation is in what they do around that API call.

Zapier's AI approach is the simplest. The "AI by Zapier" action gives you a prompt field and a response. You can chain multiple AI actions together, but each one is independent — there's no shared memory or context between steps. Zapier also offers "AI Actions" that let external AI tools (like ChatGPT plugins) trigger Zapier workflows, which is useful but different from building AI workflows yourself.

Make's AI approach adds configuration depth. The prompt engineering interface lets you set temperature, system prompts, and response formats. Make also introduced "AI scenarios" — pre-built templates that combine AI processing with common business workflows. These templates are genuinely useful as starting points, though you'll customize them heavily for production use.

n8n's AI approach is in a different category entirely. The AI Agent node supports:

• Memory: Agents can remember context from previous executions, enabling multi-turn workflows that learn from past interactions
• Tool use: Agents can call external tools (databases, APIs, web search) as part of their reasoning process
• LangChain integration: Full access to LangChain's chain and agent primitives, letting you build sophisticated AI pipelines without custom code
• Human-in-the-loop: Insert approval checkpoints where a human reviews the AI's output before the workflow continues
• Execution tracing: See every prompt, response, and decision point for debugging and auditing

If you're building simple AI workflows (summarize this, classify that, draft a response), all three platforms work fine. If you're building AI agents that reason across multiple data sources and maintain context over time, n8n is the only option that handles it natively.

For more on AI agents and how they differ from simple AI calls, my article on agentic AI covers the conceptual foundation. And for understanding the AI tools you can connect these platforms to, the model comparison helps you pick the right LLM for each workflow step.

Pricing at Scale: The Numbers That Change Everything

This is where the comparison gets interesting. At low volumes, pricing differences are negligible. At production scale, they can mean the difference between a viable product and an unsustainable cost structure.

Let's model costs for our email pipeline running at different volumes:

Note: these costs are for the automation platform only — they don't include AI API costs (Claude, GPT, etc.), which are the same regardless of which platform you use. At 2,000 executions with 2 AI calls each, your Claude API bill would be roughly $8-15/month on Haiku 4.5, as I detailed in my Claude API pricing breakdown.

The pattern is clear: Zapier's pricing makes it the most expensive option at every volume level. Make offers 60-70% savings over Zapier. n8n self-hosted is essentially free beyond the VPS cost, which makes it the obvious choice for high-volume AI workflows.

But cost isn't everything. The time you spend maintaining a self-hosted n8n instance has a cost too. If you're a solo founder processing 100 emails/month, the difference between $20 (Zapier) and $5 (n8n self-hosted) is $15 — roughly 18 minutes of your time at a $50/hour rate. If managing your own server takes more than 18 minutes per month, Zapier is actually cheaper in total cost of ownership.

Which One Should You Choose?

After building identical workflows in all three platforms, testing them in production for two weeks, and analyzing the costs at various scales, here's my framework:

Choose Zapier if:

• You're non-technical and need something working in 15 minutes
• You need integrations with niche services (Zapier's 8,000+ catalog covers almost everything)
• Your workflow volume is low (under 500 executions/month)
• You don't need advanced AI features beyond "send prompt, get response"

Choose Make if:

• You want a visual workflow builder with more power than Zapier at lower cost
• You have moderate technical comfort (you know what JSON is, but you don't want to write code)
• Your volume is moderate (500-5,000 executions/month) and budget matters
• You appreciate strong built-in data transformation and error handling

Choose n8n if:

• You're a developer or have developer resources on your team
• You need advanced AI capabilities (agents, memory, tool use, LangChain)
• Your volume is high or growing — and you don't want execution costs scaling linearly
• Data sovereignty matters (self-hosted means your data never touches third-party servers)
• You want to mix visual building with custom code in the same workflow

Personally, I use n8n for AI-heavy workflows and Zapier for simple integrations where setup speed matters more than cost. Make is the best choice for the majority of business users who need more than Zapier offers but less than n8n demands.

For teams evaluating broader AI business tools alongside automation platforms, my AI for small business guide covers how automation fits into a complete AI toolkit. And if you're interested in what kinds of AI agents you can build with these platforms, the enterprise agent use cases article has concrete examples with ROI data.

Frequently Asked Questions

Can I migrate workflows between platforms?

Not directly. There's no standard format for workflow definitions, so moving from Zapier to Make (or any other direction) means rebuilding manually. n8n exports workflows as JSON, which can be version-controlled and shared, but the JSON format is n8n-specific. Before committing to a platform, consider this lock-in factor. Starting with n8n gives you the most portability since the JSON definitions are fully accessible, even if they can't be imported into competitors.

Do I need coding skills for any of these platforms?

For Zapier, no. For Make, basic understanding of data structures (JSON, arrays) helps but isn't required. For n8n, you can build simple workflows without code, but the platform's full power requires JavaScript knowledge. If you can write a JavaScript function that parses a string and returns an object, you'll be comfortable with n8n. If that sentence doesn't make sense, stick with Zapier or Make.

Which platform handles AI errors best?

n8n, by a significant margin. AI calls are inherently unreliable — the same input can produce different outputs, API rate limits cause failures, and malformed responses can break downstream steps. n8n's retry nodes, fallback paths, and execution tracing make it possible to build AI workflows that gracefully handle failures. Make's per-route error handling is second best. Zapier's workflow-level error handling means one failed AI call often kills the entire execution.

Can I use my own AI models instead of cloud APIs?

Only with n8n. If you're running a local model through Ollama or a similar tool, n8n can connect to it directly since n8n runs in your infrastructure. Zapier and Make can only connect to cloud-hosted APIs. For teams with data sensitivity requirements or who want to use open-source models without cloud costs, this is a compelling reason to choose n8n.

How do these platforms handle GDPR and data privacy?

Zapier and Make process your workflow data on their servers, which means your data passes through their infrastructure. Both offer GDPR compliance features and data processing agreements. n8n self-hosted keeps all data on your infrastructure, making GDPR compliance your responsibility but eliminating third-party data exposure. For workflows that process personal data (customer emails, support tickets), n8n's self-hosted model provides the strongest privacy guarantees. The n8n cloud option has data processing agreements similar to Zapier and Make.

Table of Contents

• The Workflow I Built in All Three Platforms
• Building It in Zapier: Fast but Expensive
• Building It in Make: Visual Power at Lower Cost
• Building It in n8n: Developer Freedom, Self-Hosted
• Side-by-Side Comparison
• AI-Specific Features: Where It Really Matters
• Pricing at Scale: The Numbers That Change Everything
• Which One Should You Choose?
• Frequently Asked Questions

The Workflow I Built in All Three Platforms

To make this comparison meaningful, I needed a workflow that was complex enough to test each platform's AI capabilities but practical enough that you'd actually want to build it. Here's what I chose:

The "Email Intelligence Pipeline":

1. Trigger: New email arrives in Gmail matching a specific label ("Client Inquiries")
2. AI Step 1: Send the email body to Claude's API to extract key information (sender intent, urgency level, required action, relevant dates)
3. AI Step 2: Generate a draft response using Claude, tailored to the urgency and intent
4. Conditional Logic: If urgency is "high," post to a Slack channel immediately. If "medium" or "low," save the draft to Google Docs for review
5. Logging: Append a row to a Google Sheet with the email subject, classification, urgency, and timestamp

This workflow has five nodes, two AI calls, conditional branching, and three different output destinations — a realistic test that exercises each platform's strengths and weaknesses.

I've previously written about building 7 AI workflows that save 20 hours a week. This article goes deeper on the platform comparison rather than the workflow ideas themselves.

Building It in Zapier: Fast but Expensive

Build time: 15 minutes.

Zapier's biggest advantage is speed. The Gmail trigger was configured in about 60 seconds — select the account, pick the label, done. The Zapier interface guides you through each step with a wizard-like flow that's hard to get wrong.

For the AI steps, Zapier has a native "AI by Zapier" action that lets you write a prompt and get a response without configuring an external API. It runs on GPT-4o by default. I switched it to use Claude via the Anthropic integration, which required entering my API key and selecting the model. This took about 3 minutes.

The conditional logic was straightforward — Zapier's Paths feature handles if/else branching visually. I set up two paths: one for high-urgency emails (post to Slack) and one for everything else (save to Google Docs). The Google Sheets logging step was another 2 minutes.

What worked well:

• Fastest setup by far. If you value your time at $50/hour, the 15-minute build means Zapier costs about $12 in labor to set up.
• 8,000+ native integrations. Every service I needed was available without workarounds.
• Natural language workflow creation. You can describe what you want in plain English and Zapier generates a draft workflow. It got my workflow about 70% right on the first try.

What didn't work well:

• The Anthropic integration doesn't support prompt caching or batch processing. Every AI call costs full price.
• Error handling is basic. When Claude returned an unexpected format, the workflow just failed. Setting up retry logic required a workaround with a separate "error handler" Zap.
• Task-based pricing hurts for AI workflows. Each workflow execution counts as 5 tasks (one per step), and AI actions count as premium tasks.

Building It in Make: Visual Power at Lower Cost

Build time: 35 minutes.

Make (formerly Integromat) took longer to set up, but the extra time bought me a more sophisticated workflow. Make's visual canvas shows the entire workflow as a flowchart, with data flowing between nodes through clearly visible connections. For complex workflows, this visual approach makes debugging much easier.

The Gmail module worked similarly to Zapier's. The Anthropic/Claude module in Make is more configurable — I could set temperature, max tokens, and system prompts directly in the module settings without writing custom code. The built-in prompt engineering interface lets you test prompts against sample inputs before deploying.

The conditional routing in Make is handled through "routers" — visual fork points where you define filter conditions. I set up three routes (high, medium, low urgency) rather than just two, giving me more granular control. Each route can have its own error handler, which is a significant improvement over Zapier's workflow-level error handling.

What worked well:

• The visual builder is genuinely better for complex workflows. I could see the data flow at every step and trace exactly where a value came from.
• Operations-based pricing instead of task-based. My 5-step workflow counts as 5 operations (similar to Zapier), but operations are cheaper — Make's mid-tier plan offers 10,000 operations for $16/month versus Zapier's equivalent at roughly $50/month.
• Built-in data transformation. Make has powerful built-in functions for parsing text, formatting dates, and manipulating JSON without needing separate steps.
• Prompt engineering tools. The ability to test and iterate on prompts within the Make interface — rather than switching to the Claude console — saved time during development.

What didn't work well:

• Steeper learning curve. The interface is powerful but not immediately intuitive. A first-time user would need 2-3 hours to get comfortable with the concepts.
• Fewer integrations than Zapier (roughly 1,500 vs 8,000). Most popular services are covered, but niche tools sometimes require webhooks.
• The Claude integration, while more configurable, occasionally had latency issues — responses took 8-12 seconds versus 3-5 seconds in Zapier.

Building It in n8n: Developer Freedom, Self-Hosted

Build time: 25 minutes.

n8n splits the difference on build time. It took longer than Zapier because of the initial configuration (I self-host n8n on a small VPS), but the workflow itself came together quickly because n8n's AI nodes are more capable than either competitor.

The standout feature: n8n has roughly 70 dedicated AI nodes, including native LangChain integration. Instead of just "send a prompt, get a response," n8n lets you build AI agent workflows with memory, tool use, and multi-step reasoning — all within the visual workflow builder.

For my email pipeline, I used n8n's AI Agent node rather than a simple API call. The agent received the email, used a tool to search for previous emails from the same sender (context from a connected database), and generated a response that referenced prior conversations. Neither Zapier nor Make could replicate this without significant custom code.

The conditional routing was simple — n8n uses an IF node that supports complex conditions with AND/OR logic. The Slack, Google Docs, and Google Sheets nodes worked without issues.

What worked well:

• Self-hosted = unlimited executions at zero per-execution cost. My VPS costs $5/month (Hetzner CX22), and n8n runs alongside other services.
• AI Agent capabilities go far beyond simple API calls. Memory, tool use, LangChain chains — these are first-class features in n8n.
• Custom code nodes. When I needed to parse a non-standard email format, I dropped in a JavaScript function node. Try doing that in Zapier.
• Full execution visibility. Every execution shows the exact prompt sent, model response, and downstream effects. This is invaluable for debugging AI workflows where outputs are non-deterministic.
• You can tell n8n what you want in plain English and get a working workflow back, then refine it through chat.

What didn't work well:

• Self-hosting means self-maintaining. I had to update n8n manually, configure backups, and handle security. The cloud-hosted option ($20/month) eliminates this but adds execution limits.
• The initial setup took 20 minutes just to configure the self-hosted instance, Gmail OAuth, and API keys. This is a one-time cost, but it's real.
• Community is smaller. When I hit an issue with the AI Agent node, the forum had fewer answers than Zapier's help center. I ended up reading the source code on GitHub to understand the behavior.

Side-by-Side Comparison

AI-Specific Features: Where It Really Matters

All three platforms can call an AI API and return a response. The differentiation is in what they do around that API call.

Zapier's AI approach is the simplest. The "AI by Zapier" action gives you a prompt field and a response. You can chain multiple AI actions together, but each one is independent — there's no shared memory or context between steps. Zapier also offers "AI Actions" that let external AI tools (like ChatGPT plugins) trigger Zapier workflows, which is useful but different from building AI workflows yourself.

Make's AI approach adds configuration depth. The prompt engineering interface lets you set temperature, system prompts, and response formats. Make also introduced "AI scenarios" — pre-built templates that combine AI processing with common business workflows. These templates are genuinely useful as starting points, though you'll customize them heavily for production use.

n8n's AI approach is in a different category entirely. The AI Agent node supports:

• Memory: Agents can remember context from previous executions, enabling multi-turn workflows that learn from past interactions
• Tool use: Agents can call external tools (databases, APIs, web search) as part of their reasoning process
• LangChain integration: Full access to LangChain's chain and agent primitives, letting you build sophisticated AI pipelines without custom code
• Human-in-the-loop: Insert approval checkpoints where a human reviews the AI's output before the workflow continues
• Execution tracing: See every prompt, response, and decision point for debugging and auditing

If you're building simple AI workflows (summarize this, classify that, draft a response), all three platforms work fine. If you're building AI agents that reason across multiple data sources and maintain context over time, n8n is the only option that handles it natively.

For more on AI agents and how they differ from simple AI calls, my article on agentic AI covers the conceptual foundation. And for understanding the AI tools you can connect these platforms to, the model comparison helps you pick the right LLM for each workflow step.

Pricing at Scale: The Numbers That Change Everything

This is where the comparison gets interesting. At low volumes, pricing differences are negligible. At production scale, they can mean the difference between a viable product and an unsustainable cost structure.

Let's model costs for our email pipeline running at different volumes:

Note: these costs are for the automation platform only — they don't include AI API costs (Claude, GPT, etc.), which are the same regardless of which platform you use. At 2,000 executions with 2 AI calls each, your Claude API bill would be roughly $8-15/month on Haiku 4.5, as I detailed in my Claude API pricing breakdown.

The pattern is clear: Zapier's pricing makes it the most expensive option at every volume level. Make offers 60-70% savings over Zapier. n8n self-hosted is essentially free beyond the VPS cost, which makes it the obvious choice for high-volume AI workflows.

But cost isn't everything. The time you spend maintaining a self-hosted n8n instance has a cost too. If you're a solo founder processing 100 emails/month, the difference between $20 (Zapier) and $5 (n8n self-hosted) is $15 — roughly 18 minutes of your time at a $50/hour rate. If managing your own server takes more than 18 minutes per month, Zapier is actually cheaper in total cost of ownership.

Which One Should You Choose?

After building identical workflows in all three platforms, testing them in production for two weeks, and analyzing the costs at various scales, here's my framework:

Choose Zapier if:

• You're non-technical and need something working in 15 minutes
• You need integrations with niche services (Zapier's 8,000+ catalog covers almost everything)
• Your workflow volume is low (under 500 executions/month)
• You don't need advanced AI features beyond "send prompt, get response"

Choose Make if:

• You want a visual workflow builder with more power than Zapier at lower cost
• You have moderate technical comfort (you know what JSON is, but you don't want to write code)
• Your volume is moderate (500-5,000 executions/month) and budget matters
• You appreciate strong built-in data transformation and error handling

Choose n8n if:

• You're a developer or have developer resources on your team
• You need advanced AI capabilities (agents, memory, tool use, LangChain)
• Your volume is high or growing — and you don't want execution costs scaling linearly
• Data sovereignty matters (self-hosted means your data never touches third-party servers)
• You want to mix visual building with custom code in the same workflow

Personally, I use n8n for AI-heavy workflows and Zapier for simple integrations where setup speed matters more than cost. Make is the best choice for the majority of business users who need more than Zapier offers but less than n8n demands.

For teams evaluating broader AI business tools alongside automation platforms, my AI for small business guide covers how automation fits into a complete AI toolkit. And if you're interested in what kinds of AI agents you can build with these platforms, the enterprise agent use cases article has concrete examples with ROI data.

Frequently Asked Questions

Can I migrate workflows between platforms?

Not directly. There's no standard format for workflow definitions, so moving from Zapier to Make (or any other direction) means rebuilding manually. n8n exports workflows as JSON, which can be version-controlled and shared, but the JSON format is n8n-specific. Before committing to a platform, consider this lock-in factor. Starting with n8n gives you the most portability since the JSON definitions are fully accessible, even if they can't be imported into competitors.

Do I need coding skills for any of these platforms?

For Zapier, no. For Make, basic understanding of data structures (JSON, arrays) helps but isn't required. For n8n, you can build simple workflows without code, but the platform's full power requires JavaScript knowledge. If you can write a JavaScript function that parses a string and returns an object, you'll be comfortable with n8n. If that sentence doesn't make sense, stick with Zapier or Make.

Which platform handles AI errors best?

n8n, by a significant margin. AI calls are inherently unreliable — the same input can produce different outputs, API rate limits cause failures, and malformed responses can break downstream steps. n8n's retry nodes, fallback paths, and execution tracing make it possible to build AI workflows that gracefully handle failures. Make's per-route error handling is second best. Zapier's workflow-level error handling means one failed AI call often kills the entire execution.

Can I use my own AI models instead of cloud APIs?

Only with n8n. If you're running a local model through Ollama or a similar tool, n8n can connect to it directly since n8n runs in your infrastructure. Zapier and Make can only connect to cloud-hosted APIs. For teams with data sensitivity requirements or who want to use open-source models without cloud costs, this is a compelling reason to choose n8n.

How do these platforms handle GDPR and data privacy?

Zapier and Make process your workflow data on their servers, which means your data passes through their infrastructure. Both offer GDPR compliance features and data processing agreements. n8n self-hosted keeps all data on your infrastructure, making GDPR compliance your responsibility but eliminating third-party data exposure. For workflows that process personal data (customer emails, support tickets), n8n's self-hosted model provides the strongest privacy guarantees. The n8n cloud option has data processing agreements similar to Zapier and Make.