Make

Make's defining strength is the canvas. Where Zapier and n8n use linear top-to-bottom step lists, Make draws your workflow as a graph — modules connected by lines, branches splitting and rejoining, loops visible as actual loops. For automations with more than five or six steps, this is dramatically more readable, and it makes debugging easier because you can see at a glance where execution branched. The recent AI Agent module lets you drop a Claude or GPT step anywhere in a workflow with structured input and output, which means real agents (model calls embedded in deterministic logic) compose cleanly. The free tier's 1,000 operations/month is generous enough to learn the product without commitment.

Where it falls short is the operations-based pricing model. Each module execution counts as one operation, so a workflow that runs hourly with 10 modules burns 7,200 operations per month — which means the free tier is consumed by a single moderately-active scenario. Costs scale steeply at higher volumes, and the calculation isn't obvious until you've built something and watched the meter. The visual canvas, while powerful, also has a learning curve that tools with linear step lists don't — you have to internalize the data flow conventions before complex scenarios click.

Use Make if you're building workflows with branching logic, parallel paths, or multi-stage transformations where the visual structure earns its keep. For simple one-trigger-one-action automations, Zapier is faster and cheaper. For self-hosted, code-friendly workflows where you want to own the infrastructure, n8n is the right alternative. For workflows that center on a single AI agent with tools, a framework like LangGraph fits the use case more naturally.