Make vs Zapier for AI Agents

Make is cheaper than Zapier at every volume we priced, by roughly ten times at 10,000 units a month and roughly three times at 1 million. The gap shrinks at scale because Zapier's per-task price falls faster, and because the two platforms count different things. You need both halves of that to compare them properly.

Start with the units. Zapier bills in tasks: every successful action step is one task. Triggers, polling, failed actions and Zapier's built-in tools (Filter, Paths, Formatter, Delay, Looping, Storage, Tables and others) cost nothing. Make moved from operations to credits in August 2025. Every module action consumes credits, most of them one credit each, and that includes the trigger module reading from an app or webhook. Make's Router and its error handler modules are free. AI steps on either platform can cost more than one unit, depending on the model and tokens used.

*Make prices are monthly billing, read from the credits slider on make.com/pricing in October 2026. Make says annual billing saves 15% or more. Check the slider before you buy, because Make has changed its tiers more than once since moving to credits.

At 10,000 tasks or credits a month

Make lists its Core plan at $12 a month for 10,000 credits on monthly billing. Zapier Professional at 10,000 tasks is $193.50 month to month, or $129 a month on annual billing. Even against Zapier's annual price, $129 divided by $12 is about 10.75, so Make is roughly ten times cheaper per unit. In dollars that is $117 a month, or about $1,400 a year.

At 100,000 a month

Zapier Professional at 100,000 tasks is $733.50 a month on monthly billing, or $489 on annual. Make has no 100,000-credit tier. The slider jumps from 80,000 to 150,000, so you buy 150,000, which is $129 on Core or $199 on Pro, billed monthly. Even buying 50% more capacity than you need, Make Pro costs $733.50 minus $199, about $535 a month less, and Make Core about $605 less. Per unit on monthly billing, Zapier is about $7.34 per 1,000 tasks and Make Pro is about $1.33 per 1,000 credits ($199 divided by 150).

At 1 million a month

Zapier Professional at 1,000,000 tasks is $2,199 a month on annual billing, or $3,299 monthly. Its self-serve plans top out at 2 million tasks, and beyond that it is a sales conversation. Make Core stops at 300,000 credits ($237 a month), so this is Make Pro at $973 a month on monthly billing. $3,299 divided by $973 is about 3.4, so Make is a little over three times cheaper, about $2,330 a month or $27,900 a year. If you lock in Zapier's annual price of $2,199, the gap is about 2.3 times before Make's own annual discount.

Adjusting for what each platform counts

A credit and a task are not the same amount of work, so compare per workflow run as well. Take a typical AI lead-routing flow: a webhook receives a form submission, an OpenAI or Claude step classifies it, a filter or router sends hot leads one way, the lead is created in your CRM, and Slack gets a message. On Zapier the trigger and filter are free, so that is 3 tasks per run. On Make the webhook, the AI step, the CRM step and the Slack step are 1 credit each and the router is free, so it is 4 credits per run.

At the 100,000 tier on monthly billing, a run costs 3 times $7.34 per 1,000 on Zapier, about 2.2 cents. On Make Pro it costs 4 times $1.33 per 1,000, about 0.53 cents. Make is still about 4 times cheaper per run, though not the 5.5 times the headline per-unit numbers suggest.

Two things can close the gap further. The first is polling triggers. A Make scenario that checks an app every 15 minutes uses 4 times 24 times 30, or 2,880, credits a month before any data arrives, and a scenario that polls every minute uses 43,200. Zapier does not charge for polling. Use instant webhook triggers in Make wherever the app supports them. The second is formatting-heavy Zaps. If most of your Zap is Formatter, Filter and Paths steps, which are free on Zapier, the equivalent standalone transform modules in Make consume credits. Mapping functions written inside a module's fields do not count as separate modules.

Overages and running out

Zapier's pay-per-task overage bills extra tasks at 1.25 times your base rate on annual plans and 2.5 times on monthly plans, and stops your Zaps at three times your subscribed tasks. With pay-per-task off, Zaps simply stop at the limit. Make sells extra credits in 1,000 or 10,000 bundles at your plan's rate, can auto-buy 10,000 credits before you run out on Core, Pro and Teams, and otherwise pauses scenarios while queueing incoming webhooks up to your queue limit. For spiky, high-volume workloads, Make's approach is more predictable.

Migrating from Zapier to Make means rebuilding your Zaps as Make scenarios by hand, reconnecting every app account, and running the two side by side until you trust the new version. There is no importer we would rely on. The work is usually smaller than it sounds, because in most accounts a few high-volume Zaps generate most of the bill.

1. Find the Zaps that drive the bill

Open Zapier's task usage and sort your Zaps by tasks consumed over the last month or two. Write down each one's trigger app, its action steps, how many tasks it uses per run, and who depends on it. If three Zaps account for 80% of your tasks, those three are the migration. The long tail of low-volume Zaps can stay where it is for now.

2. Estimate the Make credits before you build

For each Zap on your shortlist, count what it would cost in Make: one credit for the trigger, one for each action, nothing for routers, plus whatever polling would add. Multiply by runs per month and pick a Make tier with headroom. This is the step that tells you whether the migration is worth it. If your Zapier bill is $30 a month, the rebuild time probably costs more than you will save. If it is $489 a month for 100,000 tasks, the arithmetic in the section above suggests savings of $300 or more a month.

3. Check app coverage

Zapier advertises 9,000+ apps and Make 3,000+, so check that every app in your shortlisted Zaps has a Make module with the triggers and actions you use. Where one is missing, Make's HTTP module can usually call the app's API directly, but that means handling authentication and pagination yourself. If a critical app has neither a module nor a usable API, that Zap stays on Zapier.

4. Rebuild, and improve while you are there

Rebuild each Zap as a scenario. The logic transfers directly: Zapier Paths become a Make Router, Looping becomes an Iterator, and Formatter steps usually become mapping functions inside the next module. This is also the moment to merge Zaps that do nearly the same thing into one scenario with a router, and to add the error routes that Zapier made awkward.

5. Reconnect accounts and swap webhook URLs

Every connection has to be authorised again in Make, so collect admin access to each app before you start. Any Zap triggered by a webhook will get a new Make webhook URL, which has to be updated in the form tool, payment processor or app that sends it. Keep a list of every URL you change, because this is where migrations quietly break.

6. Run both in parallel, then cut over

Run the Make scenario alongside the live Zap for a few days, with the Make version writing to a test sheet or test channel rather than your real CRM, and compare outputs run by run. When they match, turn off the Zap and point the Make scenario at the real destination. Keep the old Zap switched off but not deleted for a couple of weeks in case you need to roll back.

7. Downgrade Zapier at the right time

The savings only arrive when you drop your Zapier tier. If you are on annual billing, time the cutover for your renewal date. If you are on monthly billing, downgrade as soon as the high-volume Zaps are off. Plenty of builders keep a small Zapier plan for the low-volume tail and niche apps, which is a perfectly good end state.

Both platforms now sell agents, but they come at it from the automation side. Zapier's strength is reach: its agents and MCP server can act across far more apps. Make's strength is control: its agents live on the same canvas as deterministic scenario logic. Prompt-to-agent platforms start from the other end and let you describe an agent in plain English.

Zapier Agents and Zapier MCP

Zapier Agents are agents you set up with help from Zapier Copilot, give your company knowledge, and let act across Zapier's app library on their own. They are metered in activities, separately from your task pool. Zapier lists 400 activities a month on Free, capped at 10 per run, and 1,500 a month on the Agents Pro plan, capped at 40 per run. Zapier MCP works the other way round: it exposes Zapier actions as tools to an outside AI client such as Claude, ChatGPT or Cursor. It runs on every plan, but tool calls draw from your normal task pool, and Zapier's own example counts one MCP call as two tasks.

Why choose Zapier for AI automation? Because it is the fastest way to give an agent hands across a large number of SaaS apps, with Zapier's SOC 2 Type II controls and nothing to host. The weaknesses are the per-run activity caps, task costs that add up when an agent loops through many tool calls, and the same limitations on complex branching as regular Zaps. Do not use it for high-volume agent loops, for workflows that need self-hosting or strict data residency, or when you need to see and control every step the agent takes.

Make AI Agents, Maia and the Make MCP server

Make AI Agents are built directly on the Make canvas. Make says they are available on all plans, can use Make's own AI provider or your own LLM key, and can call any of its 3,000+ app integrations or your existing scenarios as tools. Maia by Make is a conversational builder that drafts automations and agents from a description. Make's cloud MCP server, launched in 2025, exposes your scenarios to outside AI clients, so Claude or ChatGPT can run a scenario you have already tested. Credit costs for AI steps vary with the model and token count, so test on real inputs and size your plan for the expensive runs, not the average.

Make.com vs prompt-to-agent platforms

Prompt-to-agent platforms let you describe an agent in plain English and handle the reasoning loop, memory and tool selection for you. They suit work that is mostly judgment, such as research, triage, drafting or answering questions. Make suits work that is mostly process, with an AI decision in the middle, because you can see every branch, retry and data mapping, and because a fixed scenario costs the same number of credits every run. A common pattern in the strategies catalogued on this site combines them: an agent decides what should happen, and a Make or n8n workflow does it.

The visual canvas is Make's signature feature and it is genuinely powerful once you learn it. The router module lets you branch workflows based on conditions: if the lead came from Facebook send it to flow A, if from Google send it to flow B. The iterator module lets you loop through arrays of data. The HTTP module lets you call any API directly without a pre-built connector. For AI agent workflows that need to talk to custom systems, that HTTP module alone justifies the switch from Zapier.

Where Make genuinely struggles is the learning curve. The canvas is powerful but not intuitive on day one, and new users often feel overwhelmed by the number of options. If someone on your team who has never touched automation needs to build something this afternoon, Zapier is the right call. Make rewards patience in a way that Zapier does not require.

Make also has a smaller app library than Zapier, 3,000+ apps against 9,000+. For most AI agent use cases this does not matter, because the tools being connected (OpenAI, Claude, Supabase, webhooks, Google Sheets) are all well supported. In edge cases involving niche SaaS tools, Zapier's library advantage is real.

Zapier's real superpower is its app library. It advertises more than 9,000 apps, many maintained by the app companies themselves, which means they stay current. If you need to connect to a niche SaaS tool that has never heard of Make, Zapier probably has it.

The Zap editor is one of the most beginner-friendly pieces of software ever built, and Copilot can now draft a Zap from a plain-English description. For non-technical team members who need to maintain automations on their own, Zapier's simplicity is a genuine feature rather than a limitation.

Zapier has also built out a full AI layer: Agents, Copilot, Zapier MCP, Chatbots, and Canvas for mapping how automations connect across a company. For teams already deep in the Zapier ecosystem, that makes it a credible place to run lightweight agents. It has not caught up with Make for complex agent logic, and the honest weakness is still cost at scale. The free tier covers 100 tasks a month, and as the pricing section shows, a high-volume AI workflow costs roughly three to ten times more on Zapier.

If the reason you are looking past Zapier is data control rather than price, n8n is the third option. Self-hosted n8n keeps workflow data on your own infrastructure and has no per-task bill. It is not automatically safer, though. You take on patching, network exposure and backups, and n8n disclosed several critical remote-code-execution vulnerabilities in 2026 that left unpatched self-hosted instances exposed. Zapier runs as multi-tenant cloud under a SOC 2 Type II report, with security operations handled for you. See n8n vs Zapier for the head-to-head, or Make vs n8n vs Zapier to compare all three.