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OpenClaw vs Zapier vs Make: When to Use What

An honest comparison of automation platforms. Simple triggers? Use Zapier. Visual flows? Use Make. Complex, AI-powered workflows that need judgment? That's where OpenClaw wins.

We get this question constantly: "Why should I use OpenClaw when I already have Zapier?"

The honest answer: sometimes you shouldn't. Zapier and Make are excellent tools for what they're designed to do. The problem is that most teams hit their limits fast—and don't realise there's a different category of tool for the workflows that don't fit.

Here's our honest breakdown of when to use what.

Zapier: Best for Simple, Linear Triggers

What it's great at: "When X happens, do Y." New row in Google Sheets → send Slack message. New form submission → create CRM contact. Email received → save attachment to Dropbox.

Strengths:

  • Massive app library (6,000+ integrations)
  • Zero learning curve for basic zaps
  • Reliable for simple trigger-action patterns
  • Good for non-technical users setting up basic automations

Where it breaks down:

  • Branching logic gets messy: Multi-path workflows become hard to manage
  • No real AI reasoning: You can add an "AI step" but it's a single prompt—it can't reason across multiple steps or maintain context
  • Pricing scales poorly: Complex workflows with many steps get expensive fast
  • No local/on-prem option: Your data always flows through Zapier's cloud
  • Debugging is painful: When a complex zap fails, finding the issue is tedious

Best for: Marketing teams, simple CRM workflows, basic notifications, one-to-one data syncing.

Make (formerly Integromat): Best for Visual, Multi-Step Flows

What it's great at: Complex visual workflows with branching, loops, and data transformation. Make's visual builder is genuinely excellent for workflows that involve multiple conditional paths.

Strengths:

  • Superior visual workflow builder
  • Better handling of complex data transformations
  • More affordable than Zapier for complex workflows
  • Good error handling and retry logic
  • Supports iteration and aggregation natively

Where it breaks down:

  • Still fundamentally rule-based: Every path must be explicitly defined
  • AI capabilities are bolted on: You can call an AI API, but the workflow itself doesn't "think"
  • Maintenance overhead increases with complexity: A 30-node Make scenario requires a developer to maintain
  • Same cloud-only limitation: No self-hosted option for sensitive data
  • Visual builder becomes unwieldy: Past a certain complexity, the canvas becomes spaghetti

Best for: Operations teams with technical workflow designers, ETL processes, complex data pipelines, multi-step processes with known branching logic.

OpenClaw: Best for AI-Powered Workflows That Need Judgment

What it's great at: Workflows where the automation needs to think—read context, make decisions, handle ambiguity, and take multi-step actions that can't be defined as a static flowchart.

Strengths:

  • AI-native: Agents reason about tasks, not just execute predefined steps
  • Natural language configuration: Describe what you want in plain English
  • Handles ambiguity: The agent decides how to handle edge cases instead of failing
  • Open source: Self-host it. Your data stays on your infrastructure
  • Compound actions: One agent can interact with multiple systems in a single reasoning chain
  • Audit trails built in: Every agent action is logged with full context
  • Scales without spaghetti: Adding complexity doesn't mean adding visual nodes

Where it's not the best fit:

  • Simple trigger-action workflows: If "when X happens, do Y" is all you need, Zapier is simpler
  • Non-AI data pipelines: Pure ETL? Make or a dedicated tool is more appropriate
  • Teams that want zero learning curve: There's a ramp-up to understanding agent-based automation

Best for: Engineering teams, complex operational workflows, anything involving email/document triage, reporting, multi-system coordination, and workflows that change frequently.

The Decision Framework

Ask three questions about your workflow:

1. Does it require judgment?

If the workflow needs to interpret something—classify an email, decide priority, extract meaning from unstructured text, or choose between multiple actions based on context—you need AI-native automation. OpenClaw.

If it's purely "data arrives → transform → send elsewhere," Zapier or Make.

2. How many conditional paths exist?

If it's linear (A → B → C), Zapier. If it has known branches (A → B or C, depending on field value), Make. If the branching is dynamic or context-dependent ("handle this however makes sense based on the content"), OpenClaw.

3. Where does your data need to stay?

If data sovereignty matters—regulated industries, sensitive customer data, intellectual property—you need self-hosted. OpenClaw is open source. Zapier and Make are cloud-only.

Real-World Examples

Use Zapier: New Stripe payment → update Google Sheet → send receipt email. Simple, linear, no judgment needed. Zapier handles this perfectly.

Use Make: Incoming webhook → validate data → branch based on customer tier → update CRM → trigger different email sequences → aggregate weekly stats. Multi-step with known branching. Make's visual builder is ideal.

Use OpenClaw: Incoming support email → read and understand the issue → check if it's a known bug (search Jira) → if yes, link to existing ticket and draft response → if no, create new ticket with appropriate priority and assign to right team → draft personalised response acknowledging the issue → flag if it seems like a potential escalation. This requires reading comprehension, search, judgment calls, and dynamic action selection. That's an AI agent's job.

The Hybrid Approach

Most teams we work with don't replace Zapier or Make entirely. They use all three:

  • Zapier for simple notifications and data syncing (the "glue" automations)
  • Make for complex but deterministic data pipelines
  • OpenClaw for anything that needs AI reasoning, handles unstructured data, or requires judgment

The key insight is that these tools operate at different levels of abstraction. Zapier and Make automate procedures. OpenClaw automates decisions.

Cost Comparison

| | Zapier | Make | OpenClaw | |---|--------|------|----------| | Simple workflow (5 steps) | $20-50/mo | $10-30/mo | Overkill | | Complex workflow (20+ steps) | $100-300/mo | $50-150/mo | More efficient | | AI-powered workflow | Not possible natively | Not possible natively | Built for this | | Self-hosted | No | No | Yes (free) | | Per-workflow scaling | Gets expensive | Moderate | Flat (compute-based) |

For teams running 10+ automations with AI components, OpenClaw is typically 40-60% cheaper than cobbling together Zapier + AI API calls.

Making the Switch

If you're currently hitting Zapier or Make's limits—workflows that are too complex, too fragile, or require too much manual intervention—OpenClaw is worth evaluating.

We offer a free workflow audit where we look at your current automations and identify which ones would benefit from AI-native automation. No pressure to switch everything—just an honest assessment of where each tool fits best.

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