Make is a visual automation platform with a canvas that shows exactly how data flows through your workflows. More powerful than Zapier for complex scenarios — routers, loops, parallel branches, detailed execution logs. Used by agencies and power users. Free tier: 1,000 operations per month. Core plan: $9 per month.
Make (formerly Integromat) is a visual automation platform that lets you build complex workflows between apps without writing code. Where Zapier presents automations as a linear list of steps, Make uses a canvas — a visual diagram where you can see exactly how data flows between modules, branching paths and conditional routes. Every connection is visible. Every transformation is transparent.
Make is the tool most often recommended when someone says Zapier is not powerful enough for what they need. It handles complex conditional logic, data manipulation, loops, error handling and multi-branch scenarios that become unwieldy in Zapier. The trade-off is a steeper learning curve — Make rewards time investment with significantly more capability.
In terms of scale: Make is the go-to automation platform for agencies, power users and technical operators who build automations professionally. Its visual builder makes complex workflows understandable and maintainable in ways that text-based builders do not.
Visual canvas — Make's scenario builder shows your entire workflow as a diagram. You see every module, every connection, every route. When a workflow has 20 steps and 4 conditional branches, seeing it visually is dramatically easier than scrolling through a list.
Routers — A Router module splits your workflow into multiple parallel branches. The same trigger can simultaneously update a CRM, send an email, create a Jira issue and log to a spreadsheet — all happening in parallel, not sequentially. Zapier can approximate this with Paths but Make's routing is more flexible and visually clearer.
Iterators and aggregators — Make can loop through arrays of data. Get all rows from a spreadsheet and process each one. Send a separate email to each person on a list. Aggregate multiple items back into a single output. This is difficult or impossible in Zapier without significant workarounds.
Data stores — Make has built-in persistent storage. You can save data between scenario runs, look up previous values, track state and build more sophisticated logic without needing an external database for simple storage needs.
Detailed execution logs — Every scenario execution in Make shows you exactly what data entered each module and what came out. Debugging is dramatically easier because you can see precisely where something went wrong and what data caused the problem.
Make is ideal for users who have outgrown Zapier's simplicity, agencies that build automations for clients professionally, technical operators who work with complex multi-step processes, and developers who want automation power without the overhead of writing and maintaining custom integration code.
It is also well-suited for teams that care about understanding their automations — the visual canvas makes it possible for a new team member to look at a Make scenario and understand what it does, which is harder with a text-based workflow builder.
Zapier is simpler to get started with and has more app integrations. For basic linear workflows — trigger → action → action — Zapier is often the better choice. Make's advantage appears when your workflow needs conditional branching, loops over multiple items, parallel execution paths, complex data transformation, or detailed execution monitoring. These things are technically possible in Zapier but become much cleaner and more maintainable in Make.
Make is also significantly cheaper at scale. The operation-based pricing on Make's paid plans is generally less expensive than Zapier's task-based pricing for the same workflow complexity, especially for multi-step scenarios that run frequently.
Yes. The Free plan includes 1,000 operations per month and 2 active scenarios. Operations are Make's equivalent of tasks — each module execution counts as one operation. The Core plan is $9 per month billed annually for 10,000 operations. Pro is $16 per month for 10,000 operations with more features. Teams is $29 per month for multiple users.
Go to make.com and create a free account. You land on the Make dashboard showing your scenarios. Click Create a new scenario to open the visual canvas.
The canvas is a blank workspace. You add modules (circles) and connect them with lines. Each module represents an app action. Data flows left-to-right through the connections. The leftmost module is always the trigger. Everything else is an action or transformation.
Click the + button in the centre. Search for your trigger app. Select the trigger event. Authenticate with your account. Set any filters or conditions. Click OK. You now have the starting point of your scenario.
Click the right edge of your trigger module to add the next step. Search for the app where you want to take action. Select the action type. Map the fields from previous modules using Make's data mapping — click on any field in the action module and select data from earlier in the workflow.
Click the right edge of any module and select Router. The Router splits your workflow into multiple branches. Add a filter to each branch defining when that branch should execute. Now different paths handle different conditions from the same trigger.
Click Run once at the bottom to test with a real data sample. Make shows you exactly what data passed through each module and whether each step succeeded. Fix any issues. When the test passes, click the Active toggle at the top to turn the scenario on.
Label every module. Make lets you rename each module. Use descriptive names — "Get new HubSpot contacts", "Filter: enterprise only", "Create Jira issue" — rather than leaving them as "HubSpot 1", "Filter", "Jira 1". When you come back to a scenario three months later, labels make it immediately understandable.
Use error handling routes. Every module in Make can have an error handler — a separate route that executes if that module fails. Use these for critical steps. If an important action fails, route the error to a Slack alert or a log spreadsheet rather than letting it fail silently.
Use bundles deliberately. Make processes data in "bundles" — each item in an array is processed as a separate bundle. Understanding when your scenario is processing one item versus many is key to avoiding unexpected behaviour with Iterators and Aggregators.
Test with filters set to broad conditions first. When building a new scenario, set your filters loosely so data flows through during testing. Tighten filters only after you have confirmed each step works correctly. Starting with tight filters means you may never get test data through to debug downstream steps.
Make (formerly Integromat) was acquired by Celonis in 2022. According to Make's official website, the platform offers over 1,000 app integrations with a visual scenario builder. Make processes workflows using a module-based architecture where each module is a discrete API call to a connected service.
Make charges per "operation" — each module execution counts as one operation. Unlike Zapier where the trigger itself does not count, in Make the trigger module counts as an operation too. A 5-module scenario counts 5 operations per execution. For workflows with many modules running frequently, this adds up quickly. The offset is that Make's operation prices are generally lower than Zapier's task prices for equivalent functionality.
Make's Router module is a core architectural feature that allows a single data flow to branch into multiple parallel paths. Each branch can have its own filter conditions — only data matching the filter enters that branch. This is what makes Make genuinely better than Zapier for complex conditional workflows: branching is a first-class feature, not an afterthought.
Make's built-in Data Stores provide persistent key-value storage within scenarios. You can write data, read it back in a later scenario run, and build stateful automation logic — tracking whether something has been processed before, storing configuration values, or accumulating data across runs. Capacity depends on the plan tier.