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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 thousands 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.
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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.
AI has become native: agents live inside the scenario builder as reusable, shareable components, with their reasoning and tool calls visible step by step, and a real-time visual map keeps a growing automation estate comprehensible.
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It rewards builders who think in flows: more capable than the simplest platforms, more approachable than code, with credit billing that still compounds at serious scale.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at credit-based pricing
- Reusable AI agents inside scenarios, reasoning visible
- Data transformation between connected apps
- A visual map of the whole automation estate
- 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. It is MIT-licensed and model-agnostic, working with hosted models such as Claude and GPT or local models through Ollama, so the intelligence layer is yours to choose. The software itself costs nothing; you pay only for the model usage it consumes through your own API keys.
OpenClaw runs as a persistent local service and can be reached through messaging apps including WhatsApp, Telegram and Slack, so the agent is available wherever you already type. It has a large and rapidly growing integration surface and a community registry of reusable skills. Created by Peter Steinberger, it is a young project, launched in 2025 and renamed OpenClaw in early 2026.
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Installing it grants a language model the ability to execute commands and access files on the machine it runs on. That access is the source of both its usefulness and its risk, so it should be set up with deliberate permission scoping, and it is not a sensible first AI tool for a non-technical user.
- 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
- Free, MIT-licensed software; you pay only your own model usage
- A large integration surface and community registry of reusable skills
- 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.
Pricing
- Make
Free·from $9·from $16·from $29·Custom
Prices as of August 2026.
- Free
- Free
- Core
- from $9per monthbilled annually; $10.59 per month if billed monthly
- Pro
- from $16per monthbilled annually; $18.82 per month if billed monthly
- Teams
- from $29per monthbilled annually; $34.12 per month if billed monthly
- Enterprise
- Price on applicationno list price published
- OpenClaw
- Free
By area
Where each one pulls ahead, area by area.
| Area | Make | OpenClaw |
|---|---|---|
| By job | ||
| AI agents & automation | Make iterates over a dataset and transforms records between connected systems the same way on every run | OpenClaw — when the team wants the agent capability without a hosted platform, as a persistent local service on infrastructure it controls |
| Founders & entrepreneurs | Make's visual scenarios connect a growing app stack with the logic and branching drawn into them, so the path a signup takes is on the canvas rather than decided per run | OpenClaw is MIT-licensed and free, so a founder pays for the model usage and nothing else, provided they are comfortable on a command line |
| Operations | Make deploys reusable agents across the team's own scenarios, so what gets shared is the thing the team built rather than something taken from a registry | OpenClaw runs sensitive work on local models through Ollama while heavier reasoning goes to hosted Claude or GPT, so the routing follows the data's sensitivity |
| By task | ||
| Automation & agents | Make's visual complexity becomes invisible complexity, so a scenario nobody documented is a bus-factor risk worth writing down before anyone depends on it | OpenClaw is reached through WhatsApp, Telegram and Slack rather than through a builder you open, so the agent sits where the conversation already is |
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 August 2026