Make vs Zapier

Pricing: per-task vs per-operation

This is the most important single difference and the source of most teams pricing surprise. Zapier bills per task -- every action step that fires counts as one task, and the trigger is free. Make bills per operation -- every module in a scenario counts as one op per run, including the trigger and inline transforms. The two models compound very differently on multi-step workflows.

A fair comparison must model the actual scenario. Make now bills in credits, with feature-specific rules for some AI and advanced functions; Zapier uses tasks and product-specific usage rules. Count the modules, actions, retries, fan-out, and AI usage, then compare the current plans. Neither platform has a universal step-count crossover or cost multiple.

Branching, routers, iterators

Make wins decisively. It was designed around a canvas with first-class routers (branch by condition into parallel paths), iterators (split an array, run each item downstream, then aggregate), aggregators (combine parallel results), and error handlers (catch failures and route to a recovery path). These are core primitives on the canvas, not features layered onto a chain.

Zapier has Paths (conditional branching) and Looping (iteration), but both feel like extensions to a fundamentally linear trigger-to-action model. For two or three branches and a loop, Paths and Looping work fine; for five or more branches, nested iterators, or recovery paths, Make is better suited and friendlier -- you see the whole logic on the canvas instead of clicking through tabs.

Debugging and AI

Both debuggers are solid but shaped differently. Zapier has a clean task history with per-step input/output and a Replay button -- enough to triage 2-3 step Zaps quickly. Make has full execution history with canvas-style visual replay, per-module bundles, and configurable error handlers; for 8-12 step branching scenarios, watching data flow through the canvas is far easier to reason about than a list view.

On AI, Zapier has the more polished product surface -- AI by Zapier, Zapier Agents, native OpenAI/Anthropic Actions -- and the cleanest drop-an-AI-step experience. Make has AI modules (OpenAI, Anthropic, Hugging Face, ElevenLabs) plus the canvas to chain them with branching and aggregation, so it wins when AI is embedded in real multi-step logic. For genuinely agentic work, a dedicated agent platform beats either workflow tool.

Lock-in and migration

Both are high lock-in in the same ways: cloud-only, no export to a portable format, manual rebuild to migrate. The realistic risk is not shutdown -- both are mature and well-funded -- but a pricing change or product-direction shift, where your only responses are stay or rebuild. If owning the runtime or portability matters, a source-available self-host tool (n8n, Activepieces, Windmill) is the better fit.

Migration has no importer either direction. Zapier to Make is usually straightforward (Paths become routers, Looping becomes iterators, Formatter steps become inline functions). Make to Zapier keeps fidelity only for simple scenarios -- deep router nesting, iterators feeding aggregators, or error-handler chains cannot map one-to-one and often become two or three Zaps where one Make scenario lived. Cutover pattern either way: rebuild, test with real data, run parallel a week, switch the trigger, keep the old flow disabled 30 days as rollback.

Our take

Frame the choice by workflow shape, not by which product is better overall. Mostly linear glue across a wide SaaS stack can favor Zapier. Multi-step automation with visible routers, iterators, and conditional logic can favor Make, but cost still requires a representative scenario under current plan rules.

A growing team may use both: Zapier for connector coverage and Make for scenarios that benefit from its visual control-flow model. That split adds governance and migration overhead, so compare the combined operating model rather than assuming it reduces cost.