Make vs Zapier in 2025: A Practical Comparison for AI Workflows

Zapier built the automation category. Make (formerly Integromat) built a more powerful alternative that attracted the people who ran into Zapier’s limits. In 2025, the competition is tighter than ever — and the choice matters more because both are being pushed hard into AI workflow territory.

Here’s what actually matters when choosing between them.

The interface difference

Zapier is linear: trigger → steps → action. You can branch with paths, but the mental model stays sequential. This is its biggest strength and its biggest limitation.

Make uses a visual canvas where you can see every connection. Routers branch into parallel paths, data transformers slot in between any two nodes, and error handlers are first-class citizens. It’s more complex to learn, but more accurately represents what’s actually happening in a multi-step workflow.

For AI workflows — where you’re often transforming data, making conditional decisions based on LLM outputs, and handling errors gracefully — Make’s model is a better fit.

Pricing at scale

Zapier prices on tasks (each action in a zap = one task). Make prices on operations (each module execution = one operation, but watching for triggers is free).

At low volumes, Zapier’s pricing is often competitive or cheaper. At high volumes, Make tends to be significantly cheaper — especially for workflows that run frequently or have many steps.

Real example: a workflow that checks for new rows in a spreadsheet every 15 minutes, enriches data via an API call, and sends a Slack message. In Zapier, this costs 3 tasks per run × number of rows. In Make, watching the sheet is free; the 2 operations per row are billable. At 1,000 rows/day, the cost difference is meaningful.

AI capabilities in 2025

Both platforms have added native AI modules, but they work differently:

Zapier AI: Has an “AI by Zapier” step and native ChatGPT/OpenAI integration. Straightforward for simple prompt-in, text-out scenarios. Handles most common AI use cases without custom HTTP calls.

Make AI: Better suited for complex AI pipelines. The HTTP module handles any API (Claude, Gemini, Perplexity) with full control over headers and body. Data store integration makes it easier to build workflows that maintain state across runs — essential for multi-turn AI interactions.

For RAG pipelines, multi-step reasoning, or workflows where the AI output determines the next branch, Make has a meaningful edge in flexibility.

Where Zapier still wins

Zapier has more native integrations — over 7,000 apps vs Make’s ~1,500. If you need to connect to an obscure SaaS tool quickly, Zapier is more likely to have a ready-made connector.

Zapier’s interface is also genuinely easier for non-technical users. If your automations will be built or maintained by operations people without a technical background, the learning curve difference matters.

Zapier’s AI-powered Zap builder (describe what you want, it builds the skeleton) is also more mature than Make’s equivalent.

The honest verdict

Choose Zapier if: you need broad app coverage, your team isn’t technical, and you’re running straightforward trigger-action automations at moderate volume.

Choose Make if: you’re building AI-heavy workflows, processing data at scale, need parallel execution or complex branching, or are running cost-sensitive high-volume automations.

For most teams building serious AI-powered workflows in 2025, Make is the better technical choice. The learning curve pays back quickly once you’re building anything beyond simple two-step zaps.

One more option worth knowing

Neither Zapier nor Make is ideal for every use case. For teams who need self-hosting, maximum flexibility, or integration with internal systems, n8n is worth evaluating alongside both. It adds infrastructure overhead but removes both platforms’ pricing ceilings entirely.