The best Make alternative for building AI agents
Make (formerly Integromat) gives you a powerful visual canvas for chaining apps together into 'scenarios'. It's more flexible than most node editors — but it's still a canvas, and complex AI logic quickly turns into a maze of modules and routers.
Matagi removes the canvas entirely. You tell a coding agent what you want in plain English, and it builds a code-defined agent that Matagi provisions and wires up automatically — infrastructure, memory, integrations, and model access included.
Make is a visual automation platform built around drag-and-drop 'scenarios'. It offers fine-grained control over branching, iteration, and data mapping, which makes it a favorite for power users building intricate multi-app workflows.
| Matagi | Make | |
|---|---|---|
| How you build | Describe the automation in plain English to Claude, Cursor, or Codex. It writes and deploys the agent for you. | Assemble scenarios on a visual canvas of modules, routers, and filters, mapping data between each. |
| Infrastructure | Servers, databases, memory, inboxes, crawlers, and search are auto-provisioned and wired together for you. | You connect to external apps, but you bring your own backend for anything beyond a workflow run. |
| Integrations | 3,000+ tools out of the box, plus anything with an API or MCP. Credentials are proxied, never written into code. | 2,000+ app modules plus HTTP/JSON. Each connection and data map is configured manually. |
| AI & model access | Proxied access to Claude, GPT, Gemini, and Perplexity built in — or bring your own keys. | AI steps are bolt-ons you configure per workflow; you manage your own model keys. |
| Complexity ceiling | Full code-defined agents — loops, branching, and custom logic with no node-canvas limits. | Powerful for linear and branching flows, but complex logic gets unwieldy on a visual canvas. |
| Pricing model | Flat plan from $49/mo. Runtime, LLM calls, and infrastructure billed at cost with zero markup. | Operation-based pricing that scales with how many module runs your scenarios consume. |
When Make is the better fit
- You enjoy a visual canvas and want granular control over every module and route.
- Your workflows are data-transformation heavy and already mapped out in Make.
- You need a specific Make module that already exists for a niche app.
When Matagi is the better fit
- You want an agent that reasons across steps instead of a fixed scenario graph.
- Your build needs real infrastructure — a database, memory, an inbox, schedules.
- You'd rather describe the goal than maintain a growing web of modules.
/01How is Matagi different from Make?
+
How is Matagi different from Make?
+Make is a visual scenario builder — you assemble modules on a canvas and map data between them. Matagi has no canvas: you describe the automation in plain English to a coding agent and it builds a real agent for you, with infrastructure and integrations provisioned automatically.
/02Can Matagi handle complex, branching logic like Make?
+
Can Matagi handle complex, branching logic like Make?
+Yes, and without a canvas to outgrow. Because Matagi agents are code-defined, they can include loops, branching, and custom logic of any complexity, which is exactly where visual scenarios tend to become hard to maintain.
/03Does Matagi support the integrations I use in Make?
+
Does Matagi support the integrations I use in Make?
+Matagi connects to over 3,000 tools out of the box and anything with an API or MCP. Credentials are proxied and encrypted rather than configured per module, so your keys never live in the generated code.
/04Is Matagi cheaper than Make?
+
Is Matagi cheaper than Make?
+Matagi is a flat $49/mo for solo builders, with runtime, LLM calls, and infrastructure billed at cost. Make charges per operation, so costs rise with the number of module runs your scenarios consume.
See it for yourself
Describe what you want done in plain English. Matagi provisions the infrastructure, wires the integrations, and deploys your agent. Free 7-day trial, no card required.
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