Zapier fits app-connected automations that follow a known sequence, and its wide app catalog makes first automations quick to build. Whether it suits you depends on workflow shape, task volume, and which governance controls your plan includes. For agent work that needs durable state, permissions, and audit, Major builds that into the app the agent creates.

What are the main differences between Make and Zapier?

Make builds scenarios on a visual canvas with routers and data mapping, and meters usage in credits. Zapier builds Zaps as a trigger plus actions, with paths and filters, and meters usage in tasks. Both add AI features, so confirm current names and availability on each vendor's pages before comparing.

Can Make and Zapier be used together?

Both can send and receive webhooks and call external APIs, so a team could in principle hand data from one to the other. Neither vendor's documentation we reviewed describes a native cross-platform integration, so test any hand-off yourself. Running two builders also doubles the places where logic, usage, and permissions have to be maintained.

When does an agent need an app layer instead of a workflow builder?

An agent needs an app layer when the work has to keep state across runs, enforce scoped permissions, and leave an audit trail. On Major, the agent builds a deterministic app with a managed database, storage, and logs, so repeatable steps run as code and the agent reasons only on judgment calls.

Make vs Zapier: Compare Automation, Then Look at Major

Make and Zapier both automate app workflows, but they differ in how you build, scale, and govern them. Compare their real operating models, then see when repeatable work calls for an app layer.

Jason BaoOctober 28, 2026

Operations lead mapping an automation workflow on paper while comparing Make and Zapier

Make and Zapier both connect apps and run automations when something happens in one of them. Make builds scenarios on a visual canvas and meters usage in credits. Zapier builds Zaps as a trigger plus actions and meters usage in tasks. For the make vs zapier decision, those two facts drive most day-to-day differences. If the work also needs durable state, permissions, and an audit trail, Major is where agents build the deterministic apps that run it.

Both tools automate a process a person has already defined. A growing share of internal work starts with an agent that has to work out the process first, and that work needs somewhere to keep data and history. That artifact is an app, not a workflow graph, and it is what Major produces. For broader buyer criteria, see our guides to workflow automation tools and AI workflow automation tools.

Everything attributed to a vendor below comes from its own product and pricing pages, so treat it as a vendor claim. Plans and limits change, so this article names what a capability sits behind rather than printing prices.

1. Major: agents build the software that runs repeatable work

Major is the enterprise platform where agents build the software they run on. A person can describe an internal app and ship it to production in minutes. An agent can do the same.

When an agent on Major works out a repeatable part of a task, it builds an app for that part. The app is deterministic code with its own managed database, file storage, and logs. From then on the agent runs the app instead of reasoning through the step again. Reason once. Run forever.

That gives three properties from one move. Repeatable work runs the same way every time because it is code. The work is stateful because the app holds the data and history, so a process resumes next week without a context window to refill. And it is governable because the app carries permissions and audit trails, with SSO handled at the platform layer.

The model still works on Major. It handles judgment calls, such as whether an ambiguous inbound lead fits your ideal customer profile, and stops re-reasoning steps that never change. Cost is front-loaded, then flat, because repeatable work leaves the model after the first pass. See AI agent cost for the structure of that argument.

The same app serves two audiences. A person manages work through it as a control surface. An agent executes through it as an execution layer. IT governs both. Every workflow an agent figures out becomes an app the whole organization can run and oversee, so the library compounds with use.

Major fits best when work must persist, be audited, and get steadier as it repeats. A one-off notification between two apps is a lighter job than the platform was designed for.

2. Make

Make assembles a scenario on a canvas. You connect modules for each app action and map data between them. Routers split a scenario into branches, and iterators and aggregators handle lists of records. For logic that forks and reshapes data, many teams find the canvas easier to read than a flat sequence.

Per its pricing page, most module actions use one credit, some AI-provider features use more, and router and error-handler modules use none. A scenario that loops over a long list spends credits on every iteration of every module in the loop, so count module calls per run before you commit.

Make's AI pages describe AI modules and an agent offering on the same canvas; confirm current naming there. SSO and audit logging depend on plan, so ask for the feature-by-tier matrix before you assume a control exists.

A scenario with forty modules, several routers, and error branches is software someone has to read, test, and version. The definition lives in the graph and the data lives in whatever systems the graph touches.

3. Zapier

Zapier is built around breadth of app connections and speed to a first working automation. A Zap starts with a trigger, runs actions, and can add filters, paths, and formatting steps. A non-technical operator can ship one the same afternoon, which explains its adoption.

Roughly, a task is a successful action step, so a Zap with five action steps spends more tasks per run than one with a single step. Check Zapier's help documentation for exactly which steps count, since the rules have shifted over time.

Zapier's AI story has moved too. Its agents page describes Zapier Agents as AI teammates built with help from Zapier Copilot, and its materials have also used the name AI by Zapier. Verify current naming in its help center before you plan around either. Admin controls and audit capabilities sit on higher plan tiers.

Teams run into trouble when complexity grows. A three-step Zap becomes a nest of paths, filters, and lookups, and each added step spends tasks. Zapier connects apps well. It is not the place where a process keeps its own durable state.

4. n8n

n8n is a node-based workflow tool with code steps. Per its pricing page, an execution is one complete workflow run, counted once regardless of step count or data volume. That meter behaves differently from per-step billing for workflows that loop or fan out.

Deployment is the distinguishing feature. Starter and Pro are hosted by n8n, a Community Edition can be self-hosted from GitHub, and Enterprise can be hosted or self-hosted. SSO and LDAP sit on the Business tier and above, and external secret storage and log streaming sit on Enterprise. Self-hosting means your team runs, patches, and scales the automation infrastructure.

5. Stack AI

Stack AI is a visual builder for AI workflows. You connect model calls, data sources, and tools on a canvas. Its workflow page makes enterprise deployment and governance claims, which you should validate in a demo. The model carries the logic of the workflow, so steps that run the same way every time still pass through it unless you structure them otherwise.

6. Pipedream

Pipedream targets developers. Workflows are event-driven and mix code steps with prebuilt app actions, and its documentation emphasizes working directly with APIs. The people maintaining those workflows need to be engineers, so process ownership narrows to the technical team. Confirm current plan and governance details in its documentation.

7. Workato

Workato is an enterprise integration platform built around recipes that connect systems of record across large organizations. It suits integration programs with central IT ownership. Governance and security positioning comes from Workato's own materials, so verify specifics in its official documentation and ask how licensing meters your volume.

8. Microsoft Power Automate

Power Automate is strongest inside the Microsoft ecosystem, where connectors to Microsoft 365 and Azure services and Microsoft's admin and licensing tooling already exist. Cloud flows cover app-to-app automation, and desktop automation covers UI-driven tasks. Check Microsoft's licensing documentation for how flows are metered and what each license includes.

Build the lead-intake agent on Major

This is a scenario, not a benchmark. We did not run it on each platform. A form submission arrives, the lead is deduplicated, enriched, written to the CRM, and the owner is notified. Anything that does not fit cleanly goes to a person.

In Make, that is one scenario: webhook, CRM search, router, enrichment, CRM write, notification, with credits spent per module call. In Zapier, it is a Zap with a lookup step, a path, and action steps, with tasks spent on each action that runs.

On Major, the agent works out the process once and builds an intake app.

Connectors: HubSpot for the CRM write and Slack for the owner notification, both attached with scoped credentials.
App that holds state: an intake app with a managed database of submissions, a dedupe table, and a view where an operator filters by status.
Deterministic steps: field validation, dedupe, and the CRM write run as code on every submission, with no model call.
Agent judgment: the agent reasons only on ambiguous cases, such as unclear company fit or a record that conflicts with an existing account, and routes them to an owner.
Trigger: a workflow fires on each form submission.
Governance: role-based access decides who sees submissions, SSO covers sign-in, and the audit log records every step and every agent action.

The intake history lives in the app, so a person can open it and audit what happened to any submission. Start building on Major and describe your own intake process.

How to choose

Run five questions against every platform on your list, using one real process as the test case.

Workflow complexity. How many branches, loops, and exception paths does the process have today, and after a year of edits?
App coverage. Do your systems have maintained connectors, or will you write API calls anyway?
Usage unit. Credits, tasks, executions, and platform usage meter differently. Model the same run volume, retries included.
Governance. Which of SSO, role-based access, and audit logs sit on the plan you would buy?
Durable state. Does the process need its own database, files, and history that outlive a run? Ask where that state lives and who owns it.

The fifth question is where the platforms separate. A workflow builder can call out to a database you provide. On Major, the app the agent builds comes with a managed database, storage, and logs, so state ships with the work. For agent-specific criteria, see our guides to AI agent builders and enterprise AI platforms.

The Major take

Make and Zapier orchestrate known steps, and for many processes that is the whole job. The constraint appears when an agent does the work. Someone has to decide where workflow state lives, where permissions are enforced, where the audit history sits, and who maintains the definition when it changes.

Major resolves that by having the agent build a deterministic app with a managed database, storage, and logs, then run the repeatable work from it. In the lead-intake case, validation, dedupe, and CRM writes run as code every time, and the agent spends its reasoning on the leads that need judgment. The repeatable logic lives in software the agent wrote once, which your team can inspect, permission, and audit.

This article does not cover a hands-on benchmark of every platform, a price comparison, or migration steps from an existing Make or Zapier setup.

Start building on Major

Describe your lead-intake or exception-handling workflow and let an agent build the governed app that runs it, with state, permissions, and audit included.

Frequently asked questions

Is Zapier good?: Zapier fits app-connected automations that follow a known sequence, and its wide app catalog makes first automations quick to build. Whether it suits you depends on workflow shape, task volume, and which governance controls your plan includes. For agent work that needs durable state, permissions, and audit, Major builds that into the app the agent creates.
What are the main differences between Make and Zapier?: Make builds scenarios on a visual canvas with routers and data mapping, and meters usage in credits. Zapier builds Zaps as a trigger plus actions, with paths and filters, and meters usage in tasks. Both add AI features, so confirm current names and availability on each vendor's pages before comparing.
Can Make and Zapier be used together?: Both can send and receive webhooks and call external APIs, so a team could in principle hand data from one to the other. Neither vendor's documentation we reviewed describes a native cross-platform integration, so test any hand-off yourself. Running two builders also doubles the places where logic, usage, and permissions have to be maintained.
When does an agent need an app layer instead of a workflow builder?: An agent needs an app layer when the work has to keep state across runs, enforce scoped permissions, and leave an audit trail. On Major, the agent builds a deterministic app with a managed database, storage, and logs, so repeatable steps run as code and the agent reasons only on judgment calls.