Make is the visual middle ground between Zapier and n8n: a branching scenario canvas where you can now build, run, and watch an AI agent reason in real time on the same surface.
What is the difference between a Make AI Agent and a scenario?
A scenario is Make's deterministic workflow, modules wired with flow control, while a Make AI Agent is built, run, and debugged inside that same canvas and decides at runtime which tools to call, so the scenario is fixed logic and the agent is reasoning logic. The modules are triggers, actions, and searches; the flow control is routers, iterators, aggregators, filters, and error handlers.
An agent calls individual modules directly as tools, and Make auto-creates the backing scenario so you do not wire input and output fields by hand. It can also call full scenarios and subscenarios as tools, and the Reasoning Panel shows its tool calls and decision paths on-canvas. Agents are multi-modal, accepting and producing PDFs, images, and CSVs, and a Library of Agents collects reusable patterns. To build an AI agent in Make, you open a scenario, add an AI Agent, and attach modules or subscenarios as its tools while Make wires the backing scenario for you. The features that exist are Make AI Agents, the Reasoning Panel, Maia, Make Grid, the Make MCP Server and Client, Make Code, module tools, and subscenarios; there is no Make Copilot and no Make Tables or Interfaces, which belong to Zapier.
Does Make.com support Claude and ChatGPT?
Yes, Make ships native LLM modules for Anthropic Claude and OpenAI, covering ChatGPT, DALL-E, Sora, and Whisper, alongside Google Vertex AI for Gemini, Azure OpenAI, Mistral, Hugging Face, Perplexity, OpenRouter, and ElevenLabs, and because every module sits on one canvas, a single scenario can route between models with routers and filters. You pick the model per module rather than committing the whole workflow to one provider.
The Make AI provider and AI Toolkit work on all plans, while custom provider connections require a paid plan, and the HTTP module reaches any model endpoint that lacks a dedicated connector. Maia, Make's conversational scenario builder, generates full scenarios from natural language and is rolling out in closed beta, announced at Waves '25 in October 2025, so treat it as not-yet-GA rather than a shipped feature you can rely on today.
How do Make AI agents handle memory, MCP, and long-running work?
Make AI agents keep within-session conversation history but have no built-in durable long-term memory and no native vector-store node, so any persistent or vector memory is wired yourself through the Data store module, an external database, or a vector database reached over HTTP. First-class native memory is the n8n framing, not the Make one, and it is the single largest gap to plan around when an agent needs to remember across runs.
MCP is bidirectional: the Make MCP Server exposes scenarios as typed tools to external clients such as Claude, ChatGPT, Gemini, and Cursor, and the MCP Client lets Make agents consume external MCP servers, both included on all plans. Long-running work meets hard ceilings: a single scenario execution caps around 40 to 45 minutes, a single module caps around 40 seconds, which is the relevant ceiling for one long-running AI call, and Make Code runs about 30 seconds, up to 300 seconds on Enterprise where it can import third-party libraries. Long agent loops and large batch jobs must be chunked across executions, and the 40-second single-module limit is what bounds any one model call.
Is Make.com cheaper than Zapier for AI workflows?
It depends on the workload, because Make bills per credit, where the unit changed from operations to credits at 1:1 on 27 August 2025, and most module actions cost one credit while AI modules and Make Code meter higher, so whether Make is cheaper than Zapier's per-task model depends on action volume and AI usage rather than a flat rule. Run the numbers on both for your real traffic instead of assuming either wins.
Plans are tiered as Free, Core, Pro, Teams, and Enterprise with a monthly credit allotment, unused credits roll over one month on paid plans, overage credits cost 25% more than in-plan credits as of 6 November 2025, and Make Code meters at 2 credits per second, so the billing model rewards lean scenarios and punishes heavy per-step AI fan-out. Make Grid gives a portfolio-level, observable map of dependencies and data flows once automation sprawl sets in. On billing the three platforms diverge cleanly: Make bills per credit, Zapier bills per task, and n8n bills per successful execution. On hosting they diverge again: Make is cloud-only with an Enterprise on-prem connectivity agent, Zapier is cloud-only with no self-host at all, and n8n is self-hostable on your own infrastructure. On build surface they split a third way: Make gives you a visual scenario canvas, Zapier gives you linear no-code Zaps, and n8n gives you a canvas plus a code escape hatch. By best fit, Make is for visual agent observability, Zapier is for the broadest no-code app coverage, and n8n is for self-hosting and a code escape hatch.
From scenarios to an operating system: where a studio takes over
Make gives the best in-class no-code agent observability through the Reasoning Panel, Replay, and Grid, plus strong deterministic scaffolding, but it does not give durable agent memory, native vector memory, a self-hosted runtime, or export to external tracing and evaluation stacks, and complex multi-agent topologies grow unwieldy in config. In our builds we compose many Make scenarios on a shared context layer rather than letting each one keep its own state. Composing many Make scenarios on a shared context layer and one control plane is the move from scattered automation to an AI operating system, and standing up durable memory, retrieval, and observability around it is the AI automation and AI engineering work a studio owns; if your agents have outgrown a single canvas, that is the point worth booking a build.