Make

Workflow automation tools tend to sit on a spectrum. At one end are simple trigger-action utilities: when an email arrives, save the attachment. At the other are developer-oriented orchestration frameworks that assume you can write and deploy code. Make, formerly known as Integromat, occupies the middle ground and pushes toward the more capable end of it. It gives you a visual canvas where each app connection is a module, and where the connections between modules can carry branching, looping, filtering, and data reshaping without dropping into a scripting environment. That positioning is the single most useful thing to understand about the product before deciding whether it fits your needs.

The platform is owned by Celonis and, according to the vendor, is used by more than 400,000 organizations across 200+ countries. It describes itself as a visual AI automation platform, reflecting a recent shift in emphasis toward connecting AI models and building autonomous agents alongside conventional app-to-app automation. You can browse it and similar products in our AI workflow automation category.

What the builder actually gives you

The core of Make is a drag-and-drop scenario editor. You place modules on a canvas, wire them together, and each module represents an action inside a connected service. What separates this from lighter automation tools is how much logic you can express visually before hitting a wall.

• Branching and conditional routing. Routers and filters let a single incoming event fan out to different paths based on the data. In practice this means one scenario can handle several outcomes, for example routing high-value leads to a sales channel and everyone else to a nurture list, instead of duplicating logic across many separate automations.
• Loops and iteration. You can iterate over arrays and batches of records, which matters whenever a single trigger produces many items, such as processing every row returned from a spreadsheet or every line in an order.
• Built-in data transformation. Text manipulation, math, date handling, and JSON parsing are available as functions inside the mapping fields. This reduces how often you need a separate step or external code just to reshape data between two apps that expect different formats.
• Webhooks. Scenarios can be triggered by inbound HTTP calls, which lets external systems and custom applications kick off a workflow. This is the escape hatch that keeps Make useful when an app you use is not on the connector list but can send a request.
• Execution monitoring. Every run produces a log you can inspect module by module, so when something fails you can see the exact data that passed through and where it broke. Log retention is tied to your plan.

The vendor reports more than 3,000 pre-built app connectors, including over 400 AI-related integrations. On the AI side, Make integrates with OpenAI (covering models such as ChatGPT, Whisper, and Sora) and other providers, and it has moved into agentic automation: ready-made AI agents and an MCP (Model Context Protocol) server that connects AI models to business actions across the connected apps. If your goal is to have a language model not just generate text but trigger real actions in your stack, that is the capability being marketed. Treat the newer agent features as evolving rather than settled, and prototype them on non-critical workflows first.

Who tends to get the most from it

Make rewards people who have outgrown one-step automations but do not want to maintain code. That includes operations and RevOps staff stitching together a CRM, billing, and messaging tools; marketing teams building lead-routing and enrichment flows; and freelancers or agencies who deliver automation as a service to clients. Developers also use it as a faster path than writing integration glue by hand, with webhooks and HTTP modules covering the gaps.

It is a weaker fit if your requirement is genuinely simple, a single trigger firing a single action, because a lighter tool will get you there with less conceptual overhead. It is also not the right choice if your team's culture is code-first and version-controlled, since a visual canvas is harder to diff, review, and manage in a repository than plain code.

Tradeoffs worth weighing

• The learning curve is real. The flexibility that makes complex scenarios possible also means the interface has more concepts, such as routers, aggregators, iterators, and data stores, than a basic automation tool. Expect an investment before you are productive with branching logic.
• Consumption billing needs monitoring. Make counts each module action as a credit. A scenario that loops over many records can consume credits quickly, so it is easy to underestimate volume. You need to watch usage rather than assume a flat cost.
• Debugging complex flows takes patience. The run logs are thorough, but a scenario with several branches and nested iterations can be time-consuming to trace when data does not arrive in the shape a downstream module expects.
• Visual logic is hard to review at scale. Large scenarios can become sprawling and difficult for a second person to audit, and there is no code-review workflow in the traditional sense.
• No mobile app is documented by the vendor, so building and monitoring is a desktop-oriented experience.

How pricing is structured

Make uses a freemium, credit-based model. Each module action in a scenario, such as adding a spreadsheet row or fetching an email, counts as one credit. The published tiers at the time of writing are:

Paid plans extend log retention and raise the maximum file size handled per operation. Note that this credit-based structure and the plan names differ from older descriptions of Make that referenced "operations" and separate Core, Pro, and Teams tiers; confirm the current tiers on the vendor's pricing page before committing, since consumption pricing can shift. The exact per-tier credit allowances beyond the entry points above are best checked directly, as the vendor lets you scale credit volume within a plan.

Alternatives to consider

Make competes broadly with other visual and no-code automation platforms as well as with simpler trigger-action tools and, at the technical end, self-hosted or code-based workflow engines. The right comparison depends on your priorities: if you need only one-step automations, a lighter product will be easier; if your team prefers code and version control, a developer-oriented engine may suit better; and if you already live inside a particular software suite, that vendor's native automation may reduce integration friction. Rather than name specific competitors we have not verified, we suggest shortlisting two or three tools and rebuilding one of your real workflows in each during a trial. Browse more options in our tools directory or read comparisons on the blog.

Common questions about Make

Is Make the same product as Integromat?

Yes. Make is the current name for the platform previously known as Integromat. It is the same visual automation service, now operated as part of Celonis.

How many apps can Make connect to?

The vendor states more than 3,000 pre-built app connectors, including over 400 AI-related integrations. For services without a native connector, webhooks and generic HTTP modules can bridge the gap.

Does Make have a free plan?

Yes. The Free plan costs nothing and includes up to 1,000 credits per month, two active scenarios, and a 15-minute minimum interval between runs, which is enough to build and test real workflows before upgrading.

What are the AI features in Make?

Make integrates with AI providers such as OpenAI and offers agentic automation, including ready-made AI agents and an MCP server that lets AI models trigger actions across connected apps. This goes beyond generating text to letting a model act inside your workflow.

How does Make charge for usage?

Pricing is credit-based rather than seat-based. Each module action counts as one credit, so cost tracks how much your scenarios actually run rather than how many people use the account.