n8n vs Zapier vs Make: We Built the Same Automation in All Three

Every automation article compares these three platforms with feature checklists and pricing tables. That's useful for about 30 seconds. What actually matters is: can I build the thing I need, how long will it take, how much will it cost when I'm running it 500 times a month, and what happens when something breaks at 2 AM?

So we did something that, as far as I can tell, nobody else has bothered doing. We built the exact same automation in all three platforms. Same trigger, same logic, same AI steps, same output. Then we ran it for 30 days on real data and measured everything.

The automation we chose: AI-powered lead qualification. A new form submission comes in, gets enriched with company data, scored by an LLM, routed to the right salesperson, and the lead gets a personalized follow-up email — all within 90 seconds. Seven steps, two API calls, one AI decision point, and four conditional branches. Complex enough to stress-test the platforms. Simple enough to build in a day on each.

Here's exactly what happened.

The Test Workflow

Here's what we built, step by step:

• Step 1 — Trigger: New Typeform submission (name, email, company, message, budget range).
• Step 2 — Enrichment: API call to Clearbit using the email to get company size, industry, and location.
• Step 3 — AI Scoring: Send all data (form + enrichment) to GPT-4o-mini. Prompt asks for a 1–100 lead score, qualification reasoning, and recommended next step.
• Step 4 — Branch: If score 70+, route as "hot lead." If 40–69, "warm lead." Below 40, "nurture."
• Step 5 — CRM: Create or update a HubSpot contact with the lead data and AI score.
• Step 6 — Notification: Slack message to the assigned salesperson (hot leads get immediate alert, warm leads go to a digest channel).
• Step 7 — Email: AI-personalized follow-up email via SendGrid, content varies by score tier.

We ran this workflow processing the same set of 50 test leads, then left it live handling 15–25 real leads per day for 30 days.

Build Time: How Long Each Took

Zapier: 2 Hours 15 Minutes

Zapier was the fastest to build by a comfortable margin. The Typeform trigger set up in 30 seconds. The Clearbit integration exists natively — just authenticate and map fields. The OpenAI step is a built-in action now: paste your prompt, map the input variables, done. HubSpot and Slack integrations were similarly plug-and-play.

The branching logic was the only tricky part. Zapier's Paths feature works but it's not the most intuitive interface for multi-branch routing. The if/then conditions needed careful setup to avoid edge cases (what happens when the AI returns a score of exactly 40?).

The SendGrid email step with AI-generated content required a second OpenAI call inside the workflow — one for scoring, one for email drafting. Zapier handles this fine but each OpenAI call is a separate "task" for billing purposes.

Make: 3 Hours 30 Minutes

Make's visual workflow builder is more powerful than Zapier's but has a steeper initial learning curve. The drag-and-drop canvas with connecting lines between modules is intuitive once you get it, but the first time you use it there's a "what am I looking at?" moment.

Where Make gained back time: the conditional routing. Make's Router module is genuinely better than Zapier's Paths. You add a Router, define conditions for each output path, and the visual layout makes it immediately clear which leads go where. Debugging was easier too — you can see the data flowing through each module in real time.

Where Make lost time: the OpenAI integration required more manual configuration than Zapier. You need to set up the HTTP module with the API endpoint, headers, and request body manually (or use the OpenAI module which still needs schema mapping). Not difficult, but more steps.

n8n: 4 Hours 45 Minutes

n8n took longest for the initial build, and the reasons are specific. First, we self-hosted on a $12/month Hetzner VPS. Setup: install Docker, pull the n8n image, configure environment variables, set up a reverse proxy for HTTPS. That's 45 minutes before you've touched the workflow.

The workflow build itself was about 3 hours. n8n's interface is powerful but less polished than Make's. The OpenAI node worked great — n8n's AI integration is actually one of its strongest features. The Clearbit call needed the generic HTTP Request node since there's no native Clearbit integration. HubSpot and Slack integrations are native and worked smoothly.

Where n8n won: the error handling. n8n lets you add error workflows that trigger when any node fails. We set up a fallback that catches Clearbit failures (their API occasionally returns 500s) and continues the workflow without enrichment data rather than failing the whole pipeline. This took 15 minutes in n8n. In Zapier, we couldn't achieve the same granular error handling without a significantly more complex setup.

Cost at Scale: Where the Real Difference Lives

Build time is a one-time cost. Monthly operating cost is forever. This is where the platforms diverge dramatically.

Our test workflow has 7 steps. In Zapier, each step is a "task." One workflow execution = 7 tasks. In Make, each step is an "operation." One execution = 7 operations. In n8n (self-hosted), executions are unlimited for the price of your server.

At 100 Leads/Month

• Zapier: 700 tasks/month. Covered by the Starter plan ($19.99/month, 750 tasks). Total: ~$20/month.
• Make: 700 operations/month. Covered by the Free plan (1,000 operations/month). Total: $0/month.
• n8n Cloud: 100 executions/month. Covered by the Starter plan ($24/month). Total: $24/month.
• n8n Self-Hosted: Server cost only. Total: $12/month.

At low volume, Make is the cheapest (free), Zapier and n8n Cloud are roughly equal, and n8n self-hosted is cheapest if you're already running a server.

At 500 Leads/Month

• Zapier: 3,500 tasks/month. Requires the Professional plan ($49/month, 2,000 tasks) + overage. Effective cost: ~$69/month.
• Make: 3,500 operations/month. Core plan ($10.59/month, 10,000 operations). Total: ~$11/month.
• n8n Cloud: 500 executions. Starter plan ($24/month) covers this. Total: $24/month.
• n8n Self-Hosted: Still $12/month. Doesn't blink.

The gap is opening. Zapier is 6x the cost of Make and 3x the cost of n8n Cloud.

At 2,000 Leads/Month

• Zapier: 14,000 tasks/month. Team plan ($69/month, 2,000 tasks shared) is insufficient. Professional at $49/month hits overage charges hard. Effective cost: ~$200+/month.
• Make: 14,000 operations/month. Pro plan ($18.82/month, 10,000 ops) + overage or Teams plan. Effective cost: ~$30–$50/month.
• n8n Cloud: 2,000 executions. Pro plan ($50/month). Total: $50/month.
• n8n Self-Hosted: Still $12/month. Maybe upgrade to a $24/month VPS for more RAM.

At scale, the math is clear. Zapier costs 4–16x more than the alternatives for the same automation doing the same thing.

AI Capabilities: The New Battleground

This is where the 2026 comparison differs from anything written in 2024 or 2025. All three platforms added serious AI features, and they took different approaches.

Zapier

Zapier's AI approach is "AI as a step." You add an OpenAI, Anthropic, or Google AI action to your workflow like any other integration. It works well for straightforward AI tasks: classify this text, summarize this email, score this lead. The setup is the easiest of the three — authenticate, write a prompt, map fields, done.

Zapier also introduced AI-powered workflow building: describe what you want in English and it generates a workflow. In our testing, this works for simple 3–4 step workflows but produces unreliable results for anything more complex. It's a nice starting point, not a production solution.

Limitation: no native agent capabilities. You can't build an AI agent loop (reason → act → observe → repeat) natively in Zapier. Each AI call is a single, isolated step.

Make

Make's AI integration sits between Zapier and n8n in sophistication. Native OpenAI and Anthropic modules exist but require more manual configuration than Zapier's. The advantage: Make's HTTP module with JSON body parsing gives you access to any AI API, including newer models and providers that don't have native integrations yet.

Make's real AI strength is in complex prompt chains. Because Make's visual builder handles branching and data routing so well, building multi-step AI workflows — classify, then enrich, then score, then route — feels more natural than on the other platforms. You can see the AI decision tree visually, which makes debugging significantly easier.

n8n

n8n went furthest on AI. Their AI Agent node is a genuine agent framework inside a no-code environment. You define tools (other n8n nodes), connect an LLM, and the agent reasons about which tools to use to accomplish a goal. It loops — calling tools, reading results, deciding next steps — just like a LangChain agent, but built visually.

This is a meaningful differentiator. If your automation needs the AI to make multi-step decisions based on intermediate results — not just a single-shot classification — n8n is the only platform of the three that handles it natively. We build roughly 60% of our agentic client workflows on n8n's AI nodes.

n8n also has native vector store nodes (Pinecone, Supabase pgvector, Qdrant), document loaders, and text splitters. You can build a complete RAG pipeline — ingest documents, create embeddings, query with semantic search, generate responses — entirely within n8n. Neither Zapier nor Make can do this without external services.

Reliability: What Happens at 2 AM

We tracked every failure, delay, and error across the 30-day test.

Zapier

99.8% success rate. 2 failures in 30 days, both caused by Clearbit API timeouts (not Zapier's fault). Zapier automatically retried and succeeded. The monitoring dashboard is the best of the three — clear error logs, email alerts, and one-click replay on failed executions. This is Zapier's biggest advantage: when things go wrong, the debugging experience is excellent.

Make

99.5% success rate. 4 failures: 2 Clearbit timeouts (same API issue), 1 Make platform delay (workflow ran 3 minutes late), 1 mysterious failure that resolved itself on retry. The error handling is decent but the error messages are sometimes cryptic. "Module X threw an error" without specifying what the error was. Debugging requires clicking into each module's execution log.

n8n (Self-Hosted)

99.9% success rate on workflow execution, but we had one 23-minute server outage when the VPS ran out of memory (our fault — we were running 4 other workflows on the same server and didn't allocate enough RAM). During that window, 3 leads were missed entirely. n8n Cloud would have avoided this since they manage the infrastructure.

n8n's execution logs are the most detailed of the three. You can see exactly what data entered and exited every single node. For debugging complex AI workflows, this granularity is invaluable.

Our Verdict: When to Use Which

Choose Zapier When:

• You're non-technical and need it working today
• Your workflow is simple (under 5 steps, minimal branching)
• Volume is low (under 500 executions/month)
• You value the best documentation and support ecosystem
• You're connecting well-known SaaS tools with native integrations

Zapier is the Toyota Camry of automation. Reliable, easy to use, gets you from A to B without fuss. It's not exciting and it's not cheap, but it works and anyone can drive it.

Choose Make When:

• Your workflow has complex conditional logic
• You need good AI capabilities without code
• Cost matters and volume is medium to high
• You want visual debugging and execution monitoring
• You're comfortable with a moderate learning curve

Make is our default recommendation for most businesses. It hits the sweet spot of power and usability. The pricing is fair at every scale. The visual builder is the best in class for complex workflows. If you're choosing one platform and don't know which, start with Make.

Choose n8n When:

• You need agentic AI workflows (autonomous decision loops)
• You're building RAG pipelines or complex AI systems
• Volume is high and cost sensitivity is real
• You have someone technical on the team (or an agency managing it)
• Data sovereignty matters — you want everything on your infrastructure
• You need custom integrations that don't exist in other platforms

n8n is where we build our most sophisticated client automations. The AI agent capabilities alone justify choosing it for complex use cases. But it requires more technical comfort than the alternatives, and self-hosting means you own the maintenance. It's the right choice for maybe 30% of projects. For the other 70%, Make or Zapier is a better fit.

The Bottom Line

These are all good tools. None of them is the wrong choice in absolute terms. The wrong choice is the one that doesn't match your team's technical ability, your budget at the volume you'll actually run, and the complexity of what you're building.

Try all three — they all have free tiers. Build the same simple workflow on each. The one that feels most natural to you is probably the right one. Then read this article again to make sure the long-term economics work.