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Make vs OpenClaw
A plain-English comparison to help you choose between them.
A hosted workflow platform against an open agent you run yourself: Make draws deterministic scenarios across hundreds of apps, OpenClaw is a free, MIT-licensed autonomous agent living on your own machine. Pick Make when the work is defined and repeatable, branching, iteration and error paths built visually, priced by credits, operated by the vendor. Pick OpenClaw when you want the agent itself, model-agnostic across hosted Claude, GPT or local models through Ollama, reachable through WhatsApp, Telegram and Slack, paying only the model usage on your own keys, and carrying the security responsibility that comes with a model holding command and file access on its host.
Side by side
- Summary
Make is visual automation with engineering sensibilities: scenarios built on a canvas where branching, loops, error handling and data transformation are first-class, connecting thousands of apps at credit-based pricing that undercuts the per-task platforms at volume.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at credit-based pricing
- Reusable AI agents inside scenarios, reasoning visible
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Per Make's Help Center, "effective august 27th, 2025, we're replacing operations with credits as our billing unit," with existing operations converting 1:1. Standard modules stay at 1 credit, but native AI modules consume credits variably: per one 2026 review, "A workflow with AI Agents can consume 43-50 credits per execution (Small model), versus the few credits of a classic workflow." Extra credits cost 25% more than in-plan (Help Center, updated 6 Nov 2025), for both manual and auto-purchase. The sticker price is not the bill; consider calling AI APIs directly via HTTP for cost control.
- Summary
OpenClaw is a free, open-source agent that runs on your own machine and takes actions rather than only producing text.
- Best for
- A private personal agent that runs on your own machine
- Model-agnostic: hosted Claude and GPT, or local models via Ollama
- Reachable through WhatsApp, Telegram and Slack as a local service
- Cost
- Free
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- It runs on your own machine, so data stays as local as the model you route to: fully local via Ollama, or shared with the hosted provider you choose. Installing it grants the model the ability to execute commands and read files on the host, so run it with least privilege, keep API keys and integration tokens narrow, and scope what it may touch before connecting messaging channels.
Common questions
Determinism or autonomy: which does the job need?
OpenClaw's own positioning answers cleanly: where you want defined, repeatable flows, a workflow tool is the better fit, and it names that boundary rather than blurring it. An autonomous agent decides its own steps, which is the point for open-ended tasks and the risk for processes that must run identically. Auditability points to the canvas; judgement points to the agent.
What does owning the agent actually cost?
The software is free and MIT-licensed; the costs are model usage flowing through your own API keys, which a busy agent generates faster than a fixed plan would, and the operational duty of a persistent local service. The security obligation is explicit: installing it grants a model command and file access on the machine, so least-privilege scoping is the setup, not an afterthought.
Who should choose neither and go hosted-agent instead?
Anyone wanting delegation without a server, which OpenClaw itself concedes: if running and maintaining a local service is not something you want to own, hosted agents do that work for you. And a non-technical user should not start here at all, since an agent with host access is not a sensible first AI tool. Make remains the no-maintenance middle for defined flows.
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Tool facts last checked July 2026