When a company asks us which automation platform to use for AI agent implementation, the answer always starts with a question: how technical is your team, and how many executions do you estimate per month? The right choice can save thousands of euros per year in operational costs — the wrong one can lock you into workflows that don't scale.
We've implemented automations with all three platforms for real clients. This comparison is based on projects involving GoHighLevel, HubSpot, LinkedIn, Claude, and OpenAI integrations — not lab demos.
Quick verdict (if you don't have time to read everything)
Use n8n if you have someone technical on the team, high execution volume, and want full control over data. Use Make if you want a balance between visual flexibility and moderate learning curve. Only use Zapier if your team has zero technical profiles and the workflows are simple — at scale, the per-task pricing becomes unsustainable.
n8n
The engineering option — open-source, self-hostable, unlimited executions
Pros
- ✓Self-hosted: no per-execution cost, infrastructure only
- ✓Native code — write JavaScript/Python in any node
- ✓Perfect for complex flows with advanced conditional logic
- ✓Direct LLM integration (Claude, GPT) via HTTP or native nodes
- ✓Active community, free templates, frequent updates
- ✓Full control over sensitive data (never leaves your infrastructure)
Cons
- ✗Steeper learning curve — needs someone who understands APIs
- ✗Requires a server for self-hosting (or n8n Cloud from $20/month)
- ✗Less visual UI than Make for simple flows
- ✗More technical debugging than no-code platforms
Make (formerly Integromat)
The balance — visual, flexible, with reasonable pricing for medium volume
Pros
- ✓Visual diagram-style UI — easy to understand the full flow
- ✓Per-operation pricing (not per execution) — more economical than Zapier
- ✓Native handling of arrays, iterators, and complex routes
- ✓Good integration library (2,000+)
- ✓HTTP module to call any API without native integration
- ✓Better for flows with complex data transformation
Cons
- ✗Price scales with volume — can get expensive at high frequency
- ✗Less flexible than n8n for advanced programming logic
- ✗Free version is very limited for serious projects
- ✗External platform dependency for data (vs. self-hosted n8n)
Zapier
The simplest — but pricing puts it at a disadvantage for AI use
Pros
- ✓Lowest learning curve — any profile can use it
- ✓7,000+ native integrations — largest library
- ✓Ideal for simple 2-3 step automations
- ✓Excellent support and very complete documentation
- ✓Paths and Filters for basic logic without code
Cons
- ✗Per-task pricing — AI agents that make many calls make costs explode
- ✗Complex flows are hard to maintain visually
- ✗Data volume limits per step
- ✗Advanced logic (loops, arrays, transformations) is frustrating
- ✗Free plan is very restrictive — not suitable for production
Comparison table
| Criteria | n8n | Make | Zapier |
|---|---|---|---|
| Base price (monthly) | Free (self-hosted) / $20 cloud | $9–$16 | $19.99–$49 |
| Cost at 10,000 executions/month | ~$20 (infrastructure) | ~$29 | ~$73+ |
| Learning curve | High (technical) | Medium | Low |
| LLM integration (Claude, GPT) | Native + HTTP | HTTP + modules | Limited HTTP |
| Complex logic (loops, arrays) | Excellent (code) | Good (visual) | Basic |
| Data control / privacy | Full (self-hosted) | Partial | Minimal |
| Native integrations count | 400+ | 2,000+ | 7,000+ |
| Visual UI / experience | Medium | Very good | Excellent |
| AI agent scalability | High | Medium | Low |
What we use at VeryMuch.ai by use case
SDR Agent — automatic incoming lead qualification
Processes GHL webhooks, calls Claude to qualify, updates the CRM, and sends the response message — all in seconds. Execution volume and conditional logic make n8n the only option with sustainable pricing.
Content engine — LinkedIn post generation
Receives intent signals (comments, likes from prospects), calls Claude with company context, generates the draft and sends it via Slack. Code-native integration is key for context handling.
Simple CRM → Email integration (no complex logic)
When the client already has Make configured and the flow is relatively simple (new deal in HubSpot → personalized email → GHL tag), Make is sufficient and more visual for the client's team.
Weekly report automation
Queries Supabase, calculates metrics, generates PDF and sends by email. Make handles tabular data transformation well and the client can modify it without technical help.
Zapier: almost never for AI agents
We only recommend it when the client already has a Zapier account with existing flows and the use case is extremely simple (web form → CRM). For anything involving LLMs, per-task pricing makes Zapier unviable in production.
Frequently asked questions
Can I migrate from Zapier to n8n without losing everything?
Yes, though it's not automatic. The concepts are equivalent (triggers, actions, filters) but the syntax is different. A migration of 10-15 simple flows can take 1-2 days of technical work. For complex flows, it's better to rebuild from scratch than try to migrate directly.
n8n cloud or n8n self-hosted?
It depends. If your company handles sensitive client data (leads, contact information, sales data), self-hosted is the right answer — data never leaves your infrastructure. If the team lacks technical capacity to maintain a server, n8n Cloud at $20/month is a valid option and much more economical than Make or Zapier at volume.
Is there any case where Zapier clearly wins?
Yes: when the team is completely non-technical, flows are simple (2-3 steps), and volume is low (less than 2,000 tasks/month). In that price range, Zapier's simplicity can be justified. But as soon as you introduce an LLM into the loop, task count multiplies and pricing scales quickly.
What about alternatives like Activepieces or Rivet?
Activepieces is interesting as a simpler self-hosted alternative to n8n, but the community and templates are much more limited. Rivet is excellent for orchestrating complex AI agents, but doesn't replace n8n for business system integrations (CRM, email, Slack). We use Rivet for LLM flow prototyping and n8n for the integration layer.