# What are the alternatives to Make.com for AI automations?

> The main alternatives to Make.com for AI automations, grouped by approach: Zapier, n8n, enterprise iPaaS, LLM app builders and Claude-built workflows, with when each fits.

- Published: 2026-10-26
- URL: https://teton.so/guides/alternatives-to-make-com-for-ai-automations

The main alternatives to Make.com for AI automations fall into five groups:

- **Zapier**, with its agents, for no-code work across a large app catalog.
- **n8n**, for self-hostable workflows with a built-in AI Agent node.
- **Enterprise iPaaS** such as Workato or Microsoft Power Automate, for governed, IT-owned automation.
- **LLM app builders and agent frameworks** such as Dify or LangGraph, for teams that write code.
- **Workflows that Claude builds and a platform runs**, such as Teton.

The right choice depends less on features than on who builds the automation, who maintains it, and how much of each run needs a model's judgment rather than fixed steps.

A note on sources: most "best Make alternatives" pages are written by a vendor that ranks itself first, and this guide is published by one too. The sections below say what each option is good at and when it is the better choice than Teton.

## Should you leave Make at all?

Make is still a reasonable home for AI steps. A [comparison by Fixedlabs](https://fixedlabs.ai/blog/n8n-vs-zapier-vs-make) says Make is best when AI output needs routing, cleanup or structured handling, which its visual router and data tools do well. The same comparison flags the usual reason people look elsewhere: Make's credit and operation model can surprise teams. An AI scenario that loops over records and calls a model per item uses up operations quickly.

The other common reason comes from enterprise buyers. [CData argues](https://www.cdata.com/blog/make-alternatives-ai-automation) that Make is automation-first and aimed at smaller businesses. In its view, Make isn't built for governed data access or for auditing what an agent did. CData sells one of the products it recommends, so weigh that, but the distinction is real.

If your scenarios work and the bill is predictable, adding a model step inside Make may be enough. Move when the cost model, the governance needs or the maintenance burden stop fitting.

## 1. Zapier and its agents

Zapier is the closest like-for-like swap: hosted, no-code, and a larger catalog than most. Its agents product lets you [create AI agents that act across Zapier's apps](https://help.zapier.com/hc/en-us/articles/24393442652557). Agents act on their own, but you can also call one as a step inside an ordinary Zap. Zapier's help center describes the pattern: [create an agent, give it the Trigger via Zap trigger, then add a Zapier Agents action step to any Zap](https://help.zapier.com/hc/en-us/articles/35859160812685).

Two things to check before you commit:

- **Naming is in flux.** Zapier is moving Agents into AI by Zapier, so older links and answers that point at Zapier Agents may describe a product in migration.
- **Agents have their own quota.** A third-party review lists [400 agent activities a month on the free plan and 1,500 on Pro](https://www.therundown.ai/tools/zapier-agents). That is separate from Zap tasks, and Fixedlabs notes that Zapier's per-task pricing escalates at volume.

**Good for:** non-technical teams who want hosted automation with a model in the loop and don't want to run anything themselves.

## 2. n8n

n8n is the most common pick for people who want more control than Make or Zapier give. Its [AI Agent node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/) connects a chat model to one or more tools, and the agent decides which tools to call. The [AI Agent Tool node](https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolaiagent/) lets one agent call others as tools, for multi-agent setups.

The part that matters most for cost and reliability is that agents sit beside ordinary nodes. n8n's [own tutorial](https://docs.n8n.io/advanced-ai/intro-tutorial/) makes the point that simple tasks, like validating an email address, don't need AI. Practitioners also add guardrails on the Agent node: a maximum iteration count, a structured output format, and an instruction to use a tool only when necessary.

You can use n8n's cloud or run it yourself. A local trial with Docker:

```bash
docker volume create n8n_data
docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
```

Then open `http://localhost:5678`. n8n is source-available under its Sustainable Use License, not OSI open source, so read the license if you plan to embed it in a product.

**The catch:** Fixedlabs notes a smaller native app directory and a steeper learning curve than Zapier or Make. Self-hosting means you also own upgrades, backups and uptime.

**Good for:** technical teams that want visual workflows, real agent nodes, and the option to keep everything on their own infrastructure.

## 3. Enterprise iPaaS: Workato, Power Automate and similar

Workato and Microsoft Power Automate appear on nearly every alternatives list, including [CData's](https://www.cdata.com/blog/make-alternatives-ai-automation) and [Kestra's](https://kestra.io/resources/infrastructure/make-alternatives). They are bought and governed by IT, with the access controls, approvals and audit trails large companies require. Power Automate is the natural choice if your company already runs on Microsoft 365.

**Good for:** organizations where security review and central governance outweigh build speed. They are usually more than a small RevOps team needs.

## 4. LLM app builders and agent frameworks

If the AI part is the product rather than one step in a pipeline, look at tools built around the model. [Sim.ai's comparison](https://www.sim.ai/library/best-make-com-alternatives-ai-agents-2026) calls Dify best for self-hosted LLM applications and retrieval-augmented generation. It calls LangGraph best for developers who want code-level control over agent state. Flowise is a similar visual builder on top of LangChain. Pipedream sits between this group and the workflow tools: hosted workflows where steps can be your own code.

**Good for:** engineers building chat assistants, retrieval apps or long-running agents. The trade-off is that you write and host more yourself. Connecting to a CRM or Slack is your code, not a prebuilt module.

## 5. Have Claude build the workflow, then run it in the cloud

A different approach skips the builder entirely. You describe the job to Claude Code in plain language, iterate until the output is right, and then have a platform run that job on a trigger.

[Teton](https://teton.so) works this way. After you get the job right with Claude, type `/teton`. Claude writes it up as one workflow file: the trigger (a cron schedule with a timezone, a webhook, or manual), the steps, and the credentials it needs. One deploy makes it live, and it runs in the cloud whether your computer is on or not.

What this changes compared with Make:

- **Fixed steps stay fixed.** The steps Claude worked out run the same way each time. Claude is called inside a step only where judgment is needed, like writing a summary or deciding whether a deal looks stalled. That is the same principle n8n recommends, without wiring nodes by hand.
- **Memory between runs.** Each workspace has its own Postgres database, so a job can store snapshots, what it already sent, or where it stopped. In Make or Zapier you would add a Google Sheet or data store for that.
- **Shared definitions.** Context files hold your team's ICP, scoring rules or what counts as a qualified lead. Every workflow reads them on each run, and editing one changes the next run without a deploy.
- **Visibility.** Every run is listed with its result and cost. When a workflow writes to your CRM, each run lists exactly which records it changed. Every version is kept, and rolling back is one click.
- **Credentials** are entered once in the dashboard, never in the chat or the file. They are only sent to the API hosts the workflow declares.

Typical jobs include a morning Slack post of what moved in the HubSpot pipeline, filing form submissions in Notion and pinging the owner, or drafting follow-up emails after sales calls.

**Good for:** RevOps and GTM engineers who already use Claude Code and would rather describe a job than drag modules around. If your team doesn't use Claude, Zapier or n8n is the better fit.

## Comparing costs like for like

Each tool bills in a different unit: tasks in Zapier, operations or credits in Make, executions in n8n, activities for Zapier's agents. Model tokens are often billed separately on top. Starting prices on vendor pages tell you little. Instead, take one real workflow, count the units a single run consumes (including loops and per-item model calls), and multiply by your run volume. Then add token costs.

## How to choose

| | Who builds it | AI model | Remembers between runs | Runs where |
| --- | --- | --- | --- | --- |
| Make | Ops, visually | Model modules in scenarios | Data stores | Make cloud |
| Zapier + agents | Anyone, no code | Agents and AI steps | Tables or added storage | Zapier cloud |
| n8n | Technical ops | AI Agent node beside plain nodes | Add a database | n8n cloud or self-hosted |
| Workato / Power Automate | IT-led teams | Platform AI features | Platform storage | Vendor cloud |
| Dify / LangGraph | Engineers | Core of the app | You build it | Self-hosted or vendor |
| Teton | You and Claude, in plain language | Claude only in steps that need judgment | Postgres per workspace | Cloud |

## Moving existing scenarios off Make

There is no one-click importer between these tools, so treat it as a rebuild:

1. List your scenarios and rank them by cost and how often they break. Move the worst first.
2. Export each scenario's blueprint from Make as a reference for the exact steps, filters and field mappings.
3. Rebuild it in the new tool and run both side by side for a week, comparing outputs.
4. Turn off the Make scenario only when the outputs match.

## FAQ

### Is n8n open source?

Not in the OSI sense. It is source-available under n8n's Sustainable Use License, which allows self-hosting for internal use. Check the license terms if you plan to resell or embed it.

### What is the cheapest alternative to Make for AI workflows?

It depends on your volume and on how many units one run consumes. Self-hosted n8n removes per-execution fees but adds server and maintenance cost. Count the units for one real workflow, plus model tokens, before comparing plans.

### Can I keep Make and just add AI steps?

Yes. If your scenarios work and costs are predictable, a model step inside Make is often the least disruptive option. Move only the scenarios that are expensive, fragile or need memory between runs.

### Is Zapier Agents still available?

Zapier is moving Agents into AI by Zapier. Check Zapier's help center for the current name and setup before following older tutorials.

---

- [All guides](https://teton.so/guides)
- [Teton](https://teton.so): the cloud for Claude Code. Build a workflow with Claude once and Teton runs it on a schedule or a webhook.