Nicole Replogle's May 15, 2026 analysis finds that differences in billing units and runtime behavior make Zapier

Nicole Replogle’s May 15, 2026 analysis finds that differences in billing units and runtime behavior make Zapier typically less expensive than Make for AI‑powered automations, even though Make offers lower entry pricing.

Nicole Replogle published a detailed comparison on May 15, 2026 that concludes Zapier typically yields a lower total cost for AI‑powered automations than Make, despite Make’s cheaper entry plans. The piece matters because the two platforms bill and meter automation work very differently, and those differences can quickly change the economics of a project as it runs and scales.

Both platforms offer free plans and low‑tier paid plans (Zapier from $19.99/month; Make from $12/month) and provide more than basic trigger‑and‑action functionality. Zapier lists 9,000+ integrations versus Make’s roughly 3,000+, and Zapier includes a built‑in form builder on every plan while Make does not. Each product also includes built‑in databases, and both expose features beyond simple triggers and actions that influence how workflows are designed and priced.

Make’s headline pricing is expressed in credits per step, and Replogle emphasizes how that model plays out in practice. Every trigger, search module and action can consume a credit; polling schedules can burn credits even when no new data appears; and errors and test runs may still draw credits. Some modules — examples include heavy code execution or certain AI actions — can cost multiple credits, so a 10,000‑credits/month plan is easy to exceed depending on architecture and operational patterns.

Zapier, by contrast, charges for tasks tied to completed work actions only. Many internal operations that improve robustness — filters, formatting operations and paths — do not consume task counts the way analogous Make steps consume credits. Replogle also notes that Zapier Tables and Zapier Forms do not count their triggers or actions toward task usage, which can materially reduce running costs for high‑volume data workflows.

Taken together, the analysis argues that Zapier’s predictable per‑outcome billing and larger connector library generally lower total cost of ownership once builders factor in trigger frequency, maintenance and real‑world usage patterns. Make’s lower entry price can be attractive and sufficient when your stack is fully supported and automations remain very small, but the credit model becomes more expensive with scale or frequent polling.

For builders the practical takeaway is straightforward: estimate trigger frequency, polling schedules, debugging and test runs, and any AI or heavy‑code modules before choosing a platform. If you need broad connector coverage, enterprise governance and predictable per‑outcome billing, Zapier may stretch dollars further; if you want the cheapest starting plan for compact scenarios, Make can still be the right fit.