n8n vs. Make vs. Zapier for AI Workflows

Every workflow tool now has an "AI step." Picking the right one matters more than ever, because LLM calls multiply executions fast and pricing models punish you at scale.

Zapier — best UX, worst price at scale

Easiest tool on the market. Anyone can build a useful workflow in an afternoon. Above the free tier, pricing is ~$10 per 1,000 tasks once you factor in AI steps. At 50K AI-touching tasks/month you're at $500+/mo before you've called OpenAI once. Fine for prototypes; brutal at scale.

Make — middle of the road

Better for branching logic and data transformation than Zapier. Cheaper per execution. UI is a learning curve. Sweet spot: SMB workflows with 5K–50K executions/month and meaningful branching logic.

n8n — cheapest at scale, steepest learning curve

Self-hosted on a $40/mo Hetzner box, you can run millions of executions per month at zero marginal cost. Open-source core, paid cloud option if you don't want to operate it yourself. The trade-off: you're now responsible for backups, upgrades, monitoring, and the occasional debugging session at midnight.

Our standardization

For client work above 50K/month executions, we standardize on self-hosted n8n. It pays for itself in 2 days vs Zapier. For one-off automations or sub-50K, Zapier is genuinely the right call — the labor cost saved is worth the platform fee.

What none of these solve

• LLM cost tracking — you need a separate observability layer.
• Retry semantics on LLM failures — bake your own backoff into the prompt step.
• Schema validation on LLM outputs — assume garbage out, validate every response.
• Idempotency on side effects (sending email, charging cards) — the workflow tool will happily retry; your downstream needs to dedupe.

Bottom line

Zapier for under 50K/mo executions or when business users own the workflow. n8n self-hosted above 50K/mo or when you need real engineering control. Make is the safe middle when neither extreme fits.