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Comparison

FlowFn vs Make

Make has a beautiful, branchy visual canvas — and a steep learning curve. FlowFn lets you describe a workflow in English and have the AI draft it, then refine it in the same builder, with forms, dashboards, and AI models built in.

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TL;DR

The short version

  • FlowFn lets you describe a workflow in English and have the AI build it; Make’s strength is its deep visual operator graph.
  • FlowFn includes forms, dashboards, and AI in one product; Make leans on its scenario canvas plus external tools.
  • Pick Make for visual depth and complex branching, FlowFn for fast time-to-first-workflow with AI.
Feature comparison

How FlowFn stacks up against Make

Powerful visual scenario canvas with deep operator vocabulary.

Updated May 2, 2026

Reflects publicly documented features as of this date; competitor capabilities can change.

Feature

FlowFn

Make

Visual workflow builder

Included

Included

AI workflow generator (describe → build)

Native — describe in plain English

Available via Make’s AI features

AI models in-line

70+ models, bring your own keys

Via individual app modules (OpenAI, etc.)

Built-in forms

Native form builder, embeddable

Not available

Live data visualizer / dashboards

Native dashboards

Not available

Learning curve to first live workflow

Minutes — AI drafts the steps

Steeper — operator-graph model to learn

Free plan

Included

Included

Self-host option

Not available

Not available

Pricing

How Make and FlowFn price differently

Make prices on operations — each module call inside a scenario counts as one or more operations, and complex iterators or routers can multiply the count quickly. Plans start with a per-month operations bucket; AI module calls consume operations on top of any underlying model API costs (Make doesn't ship its own model billing). FlowFn rolls runs and AI into a single credit pool, so the cost of a workflow is closer to the cost of the work itself, not the cost of routing it through operators. If you build long branchy scenarios on Make, you can hit your op budget weeks before the end of the month; FlowFn's run-level metering tends to be steadier for the same volume of automation.

Why teams switch

What FlowFn does that Make doesn’t

From idea to live workflow in minutes

AI Workflow Builder drafts the scenario from a plain-English description. You refine it in the visual builder — no operator vocabulary to learn first.

AI as a first-class citizen

70+ models, BYO keys, used like any other step. No need to wire individual app modules together to get a single AI call into your workflow.

No tab tax

Forms, dashboards, and workflows in one product — capture, automate, and report without standing up additional tools.

Honest take

When Make is the better choice

No tool is right for every team. Here’s when we’d send you to a competitor.

  • You need very long, deeply branched scenarios with niche operators.
  • Your team has already invested in Make’s scenario discipline and team training.
  • You rely on a Make-specific module that hasn’t shipped on FlowFn yet.
Migration

Migrating from Make to FlowFn

A practical playbook for moving an existing workflow over without losing data or downtime.

  1. Export your scenario list from Make’s dashboard and prioritise by run frequency.
  2. For each scenario, sketch the trigger and the top-level chain — ignoring purely cosmetic operators.
  3. Use FlowFn’s AI Workflow Builder to draft the equivalent: paste the scenario description and let the AI propose triggers and steps.
  4. Map operators 1:1 where possible — Filter → Conditions, Router → Branches, Iterator → Loops, Aggregator → an aggregation step (or a code step) to combine items.
  5. Reconnect credentials, run a few cycles in parallel with Make, then disable the Make scenario.
Free plan — no credit card needed

Try FlowFn free — see the difference yourself

Free forever plan. No credit card required. Build your first AI workflow in minutes.

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