Make vs Zapier

Pricing: per-task vs per-operation

This is the most important single difference, and the source of most teams\u2019 surprise when their bill grows. Zapier bills per task — every action step that fires counts as one task. Make bills per operation — every module in a scenario counts as one op per run, including the trigger and any inline transforms. The two models compound very differently on multi-step workflows.

The math that matters: a 5-step workflow firing 1,000 times/month on Make is 5,000 ops; the same workflow on Zapier is 4,000 tasks (Zapier\u2019s trigger is free). Comparable unit count, but Make ops cost roughly half as much per unit at equivalent tiers — so Make typically wins on cost by 2-3× on workflows with 3+ steps. For simple "form → email" two-step Zaps, the difference is small and Zapier\u2019s polish usually wins. The crossover point is around 3-4 steps and a few thousand runs/month.

Integrations: catalog breadth vs depth

This is where Zapier still wins on raw count. Zapier has ~7,000 apps; Make has ~1,800. The 5,000-app gap is real, but most of it lives in long-tail SaaS that most teams never touch. For the top 200 apps any business actually uses — Google Workspace, Slack, Notion, Airtable, HubSpot, Salesforce, Stripe, OpenAI, Anthropic, the major CRMs, project trackers, and helpdesks — both cover them well, often with comparable depth on triggers and actions.

Where Zapier wins on substance: niche industry SaaS (legal, healthcare, real estate), regional tools (especially outside the US), and the latest startup SaaS that adds Zapier integration as table stakes. Where Make is at least competitive: every mainstream tool, plus a strong HTTP module + JSON Parse combo for any REST API that has no native app. Neither lacks "we have an integration for that" for typical work.

Branching, routers, iterators

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

Zapier has Paths (conditional branching) and Looping (iteration over arrays), but both feel like extensions to a fundamentally linear "trigger → action → action" model. For Zaps that need 2-3 branches and a loop, Paths and Looping work fine. For workflows with 5+ branches, nested iterators, or recovery paths from errors, Make is the more honest tool. The mental model is also better — you see the whole logic on the canvas instead of clicking through tabs to follow a Path.

AI workflow support

Zapier has slightly more polished AI marketing — AI by Zapier, Zapier Agents, native OpenAI and Anthropic actions, AI Workflows feature pages — and the result is a clean "drop an AI step into your Zap" experience. For "summarize this email and post the summary to Slack" or "draft a reply with GPT-4o and queue it for approval", Zapier is fast and obvious.

Make has dedicated AI modules (OpenAI, Anthropic, Hugging Face, ElevenLabs) plus the canvas to chain them with branching and aggregation. For workflows where AI is embedded in real logic — "classify the lead, branch to the right team, generate a custom reply, log the decision and the prompt for audit" — Make wins because the canvas handles the multi-step structure gracefully. For agentic workflows specifically, also look at Dify or LangChain; both Make and Zapier are workflow automation tools that happen to call AI, not AI platforms.

Onboarding and learning curve

Zapier wins on day one. Pick a trigger app, pick an action app, map the fields, hit publish — Zapier walks you through this in five minutes with templates, app suggestions, and inline help. It is the best-in-class onboarding for non-technical users, and it has been for years.

Make has a steeper first day. The canvas, the modules, the way data flows between them, the difference between operations and runs — there is more to learn before your first scenario ships. Once it clicks (usually within a week of regular use), the payoff is significantly more powerful workflows. The honest tradeoff: Zapier optimizes for time-to-first-Zap; Make optimizes for capability-of-final-workflow.

Debugging

Both are solid. Zapier has a clean task history with per-step input/output data, a "Replay" button that re-runs from any step, and email alerts on failures. For 2-3 step Zaps, this is enough to triage anything quickly.

Make has full execution history with canvas-style replay (you watch the data flow through your modules visually), per-module input/output bundles, configurable error handlers that branch to recovery paths, and "rollback" patterns for transactions across multiple modules. For 8-12 step scenarios with branching, Make\u2019s visual replay is significantly easier to reason about than scrolling through Zapier\u2019s list view.

Scaling

Two different shapes, both production-capable on cloud:

• Zapier scales via cloud tiers — higher tiers raise task limits, add multi-step Zaps, unlock Paths and Looping, and bring in admin features. Cost grows linearly with tasks, which can balloon on high-frequency Zaps with many actions. Enterprise tiers add SAML, advanced admin, and SLAs.
• Make scales via cloud tiers — higher tiers raise ops, concurrent scenarios, and execution timeout caps. Per-ops cost grows but at a kinder slope than per-task on multi-step work. Enterprise tiers add Make for Companies (admin, SAML, audit logs).
• Concurrency: both throttle by tier. Make has explicit concurrent scenario limits; Zapier has implicit task throughput caps. For high-throughput burst workloads, either tool will tell you to upgrade or split the workflow.
• At very high volume (100k+ executions/month), self-hosted n8n or Windmill is dramatically cheaper than either Make or Zapier — the cloud tools are not designed to compete on cost-per-execution at scale.

Lock-in risk

Both are high lock-in, in similar ways. Neither exports workflows to a portable format. Neither self-hosts. Migration in either direction is a manual rebuild. The realistic risk is not "the company shuts down" — both are mature, well-funded, and unlikely to disappear. The realistic risk is "pricing changes in a way that hurts" or "the product direction stops fitting your needs" — and your only responses are stay or rebuild.

If lock-in matters more than convenience, the answer is one of the source-available self-host tools: n8n (Sustainable Use License), Activepieces (MIT), or Windmill (AGPLv3). All three self-host on a $5-20/mo VPS, export workflows to portable formats, and remove the cloud-only constraint. See best Zapier alternatives for the full comparison.

Who should use which

Pick Make if any of these are true

• Your workflows have real logic — branching, iteration, error handling, conditional API calls.
• You are building workflows that 3-5 step Zaps cannot express cleanly.
• You want a canvas the ops team can read and edit visually, not a list of steps.
• Per-task pricing on Zapier has started to sting on your multi-step workflows.
• You are comfortable spending a week learning a canvas in exchange for significantly more capable workflows.
• You want first-class routers, iterators, aggregators, and error handlers as core primitives.

Pick Zapier if any of these are true

• You need an integration that exists on Zapier but not Make — long-tail SaaS, niche industry tools, region-specific apps.
• Your workflows are simple "trigger → action" or "trigger → 2-3 actions" glue with no branching.
• Your team is non-technical and you want the easiest first-day experience in the category.
• You want the broadest catalog, period, and you are willing to pay for it.
• You are doing 1-2 step Zaps at low-to-medium volume where per-task pricing is roughly equivalent to per-ops.
• You want the most mature AI Actions and AI Agent product in a no-code workflow tool today.

Migration considerations

Neither platform has an importer for the other. Migration is a manual rebuild, but the shape of the work is different in each direction:

• Zapier → Make: usually straightforward. Most Zapier triggers and actions map cleanly to Make modules. Paths become routers, Looping becomes iterators, multi-step Zaps become canvas scenarios. Budget 20-30 minutes per Zap for the first three, 10-15 minutes after the pattern clicks. The hardest migrations are Zaps that lean heavily on Formatter steps — those become inline Make functions, which is easier once you find them but unfamiliar at first.
• Make → Zapier: straightforward only for simple scenarios. Make scenarios with deep router nesting, iterators feeding aggregators, or error handler chains will lose fidelity on Zapier — Paths and Looping cannot match Make\u2019s structural primitives one-to-one. Many migrations end up as "two or three Zaps where one Make scenario lived" plus some loss of visual coherence.
• Hybrid is legitimate. Many teams run Zapier for long-tail integrations into SaaS Make doesn\u2019t cover natively, and Make for the multi-step workflows where per-ops wins. Combined cost is often less than going all-in on Zapier; combined complexity is two platforms to learn and pay for.
• Cutover pattern (either direction): rebuild → test with real production data → run in parallel for a week → switch the source → keep the old workflow disabled for 30 days as rollback. Never delete the source workflow before parallel testing confirms green.

Best use cases

Make excels at

• Multi-branch lead routing — incoming lead → score → route to the right team → notify → log, all visible on the canvas for ops ownership.
• Iteration-heavy workflows — fetch a list, process each item, aggregate results, branch on the aggregate.
• Error-handler-driven flows — retry → fallback API → notify ops → log to a tracking system, all as first-class structure.
• Cost-sensitive multi-step automations — 5+ step workflows at moderate-to-high volume where per-task pricing on Zapier would dominate the budget.
• AI workflows with real logic — classify, branch, generate, log, all chained on the canvas.

Zapier excels at

• Long-tail SaaS glue — connecting niche tools to mainstream ones, where Zapier\u2019s catalog is the deciding factor.
• Simple two-step Zaps — "new form submission → create CRM contact", "new Stripe charge → Slack notification".
• Marketing operations — campaign triggers, email automation, list management, where the catalog covers every tool in the stack.
• Non-technical team workflows — anything where the ops or marketing team owns the Zap and engineering is not involved.
• AI Actions and AI Agent prototypes — the friendliest first-touch AI automation experience for non-developers today.

Our take

These tools serve overlapping but distinct humans, and the choice gets sharper once you stop framing it as "which is better" and start asking "what shape are my workflows." If your work is mostly 1-2 step glue across a wide stack of SaaS, Zapier is the right answer and has been for a decade. If your work is multi-step automation with real branching and conditional logic, Make wins on both capability and cost — usually by enough to be worth the learning curve.

The honest answer for a growing team is often "both, eventually." Start on Zapier because it ships faster; migrate the heavy multi-step workflows to Make once the bill starts hurting or the Zaps start fighting their own linearity. Keep Zapier for the long-tail integrations that Make doesn\u2019t cover. The combined cost is usually less than going all-in on Zapier at equivalent volume, and each tool ends up doing what it is best at.

Two caveats worth naming: both are cloud-only, so if data residency, air-gap, or owning your runtime matters, neither is your tool — look at n8n, Activepieces, or Windmill instead. And both are high lock-in, so commit with eyes open: migration is a real project in either direction.

Next reads

• Make review — the honest deep-dive
• Zapier review — the honest deep-dive
• Make vs n8n — managed cloud vs self-host
• Make vs Pipedream — cloud canvas vs code-first serverless
• Make vs Activepieces — proprietary cloud vs MIT self-host
• Zapier vs Activepieces — proprietary giant vs MIT challenger
• Best Zapier alternatives — the wider buyer guide
• Best Make alternatives — the wider buyer guide
• All head-to-head comparisons