Make
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.
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.
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Model
- Hosted service
- Checked
- August 2026
Prices, plans and model versions change fast: this is a mid-2026 snapshot; check the tool's official site for the latest.
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
Less suited to
Credit billing still compounds: data-heavy scenarios that iterate over large sets consume credits quickly, and the arithmetic needs doing before scale. The cheapest at volume is still not cheap at extreme volume.
Its power also assumes a builder: teams wanting the absolute simplest trigger-action setup will move faster on the simpler platforms.
Costs & data, in short
Core suits most SMB automation; Pro adds execution-log search/priority; Teams for multi-builder teams. Enterprise for SSO/compliance.
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.
Plans
| 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 |
Prices as of August 2026. Prices and plans change regularly. Check with the provider before you buy.
In practice
How Make is used, area by area.
Jobs
AI agents & automation
The second automation platform
The second automation platform, reached when the first one runs out of logic. Most teams arrive here after a flow they already depend on grows a condition, then a loop, then a branch that has to fail gracefully, and the simpler platform starts charging heavily for structure it was never designed to hold.
The canvas is the reason to move. Branching, iteration and error paths are drawn rather than worked around, credit-based pricing keeps a high-volume process affordable where per-task billing had begun to hurt, and reusable AI agents live inside the scenario with their reasoning visible step by step, so a flow that makes decisions can still be inspected. A visual map of the whole estate keeps a growing set of automations comprehensible rather than mysterious.
Builders gain most from it: the person who enjoys constructing the machine, has more than one process worth real structure, and has started noticing the per-task arithmetic.
Example tasks
- Rebuild a flow that outgrew a simpler platform's branching limits
- Add error handling and retries to an automation the business depends on
- Iterate over a dataset and transform records between connected systems
- Embed a reusable AI agent inside a scenario with its steps visible
- Map the whole automation estate before it becomes undocumented
Limits
One-step automations are slower to build here than on the simpler platforms, and a team that only needs a few of those will move faster elsewhere.
Credit billing still compounds at extreme volume, so data-heavy scenarios iterating over large sets need the arithmetic done before they scale. Visual complexity also becomes invisible complexity: a scenario nobody documented is a single point of failure worth writing down while it is still fresh.
Compares
| vs | Pick Make when | Pick the other when |
|---|---|---|
| GumloopFull comparison → | Make is the second automation platform, reached when the first runs out of logic: a flow you already depend on grows a condition, then a loop, then a branch that has to fail gracefully | the core steps are AI operations rather than integrations |
| BardeenFull comparison → | Make keeps a growing set of automations comprehensible by mapping the whole estate on one canvas, though visual complexity becomes invisible complexity when nobody documents a scenario | the agents should traverse sites themselves, finding the specific information asked for and summarising it |
| ZapierFull comparison → | Make suits a second-stage estate, where a handful of processes are already automated and the next ones are more complex than trigger and action | nobody on the team will maintain infrastructure and the automation needs to exist soon |
| n8nFull comparison → | Make is the reasonable choice when one person owns automation as part of their job and would rather see every scenario laid out than listed | data residency, compliance or unit cost is the deciding factor rather than convenience |
| LindyFull comparison → | Make draws the branching, iteration and error paths rather than working around them, so the shape of the process is on the canvas instead of in the agent's head | the work you want removed is a whole job rather than a single step, and some variation in how it gets done is acceptable |
| CrewAIFull comparison → | Make embeds a reusable AI agent inside the scenario with its steps visible, so a flow that makes decisions can still be inspected by whoever inherits it | collaboration between agents is the actual problem and you have the engineering capacity to express it in code |
| LangChainFull comparison → | Make adds error handling and retries to an automation the business already depends on, which is a configuration job rather than a build | the system is complex enough that control and observability matter more than speed to a first version |
| Relevance AIFull comparison → | Make is for the builder who enjoys constructing the machine and has started noticing the per-task arithmetic | the unit of work is a function rather than a task, and the ambition is several agents covering it rather than one automation running through it |
| OpenClawFull comparison → | Make iterates over a dataset and transforms records between connected systems the same way on every run | the team wants the agent capability without a hosted platform, as a persistent local service on infrastructure it controls |
Founders & entrepreneurs
Make gives founders serious automation at startup prices
Make gives founders serious automation at startup prices. Its visual scenarios connect a growing app stack with logic and branching that simpler tools charge enterprise rates for, and a generous free tier means the automation habit starts before the budget exists. It answers to a founder whose ops automation needs real logic and whose budget line reads zero. The trade is the learning curve and the meter: if nobody on the team enjoys building flows, the simplest platforms get the basics running faster, Make's depth pays only once automations grow real logic, and credit counts climb as data volumes grow with the business, so watch them as you scale.
Example tasks
- Wire signups, payments and notifications across the stack
- Automate onboarding with branching per customer type
- Transform data between tools instead of re-entering it
- Add AI steps that classify and draft inside flows
- Scale early automations without per-task cost shock
Limits
If nobody on the team enjoys building flows, the simpler platforms get the basics running faster; Make's depth pays when automations grow real logic. Watch credit counts as data volumes grow with the business.
Compares
| vs | Pick Make when | Pick the other when |
|---|---|---|
| ZapierFull comparison → | Make gives a founder real logic and branching at startup prices, with a generous free tier that starts the automation habit before the budget exists | speed of setup and the widest app catalogue matter more, wiring the young company together in an afternoon |
| BardeenFull comparison → | Make's depth pays only once automations grow real logic, and if nobody on the team enjoys building flows the simpler platforms get the basics running faster | the growth work is hours in the browser doing what a robot should, prospect lists and competitor pages included |
| n8nFull comparison → | Make automates the onboarding with a branch per customer type and transforms the data between tools instead of anyone re-entering it | you can run a server and refuse to rent your automations forever |
| ManusFull comparison → | Make scales an early automation without a per-task cost shock, which is the difference between a habit that survives growth and one that gets switched off | a founder task is a whole job you would hand to a capable assistant |
| Claude CoworkFull comparison → | Make wires the signups, the payments and the notifications across the stack, which is the plumbing a young company runs on rather than the thinking | you are covering several roles at once and the bottleneck is execution rather than ideas |
| Relevance AIFull comparison → | Make adds the AI step that classifies and drafts inside a flow the business already runs, rather than standing an agent up beside it | headcount is the constraint and parts of the work are delegable to agents |
| OpenClawFull comparison → | 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 | you want a personal agent you own rather than a subscription seat |
Operations
Make suits operations teams automating beyond simple triggers
Make suits operations teams automating beyond simple triggers. Its visual scenarios handle branching, iteration and error paths across thousands of apps, priced by credits rather than seats, which keeps sophisticated multi-step automation affordable where per-task platforms would not, and a real-time visual map keeps a growing automation estate comprehensible. It rewards operations whose automations carry real logic and whose volume makes per-operation maths win. Two cautions travel with the power: mission-critical pipelines with hard guarantees eventually belong in engineered systems rather than visual scenarios, and a fifty-module scenario nobody documented is production infrastructure with a bus factor of one, so document what runs before it becomes tribal knowledge.
Example tasks
- Build multi-path scenarios for real operational logic
- Iterate over datasets with loops and explicit error paths
- Sync and transform records across systems
- Deploy reusable agents across the team's scenarios
- Keep the automation estate visible on the live map
Limits
Mission-critical pipelines with hard guarantees eventually belong in engineered systems; visual automation is operational convenience, not infrastructure. Error handling exists and needs actually configuring.
Compares
| vs | Pick Make when | Pick the other when |
|---|---|---|
| ZapierFull comparison → | Make handles the operational flows that carry real branching, iteration and error paths, priced by credits so sophisticated multi-step automation stays affordable at volume | departmental automation should ship in an afternoon, faster to deploy across the widest tool set |
| Claude CoworkFull comparison → | Make keeps the whole automation estate visible on a live map, so an operations team can see what runs rather than remember it | the operational work is recurring assembly rather than one-off analysis |
| GensparkFull comparison → | Make syncs and transforms records across systems, which is plumbing between the tools an operations function already runs rather than work done inside one workspace | an operations function wants one workspace to run research, spreadsheets, browsing and reporting rather than wiring several tools together |
| OpenClawFull comparison → | 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 | operations wants an agent that acts across the team's tools while staying on infrastructure the team owns |
| ManusFull comparison → | Make is operational convenience rather than infrastructure: mission-critical pipelines with hard guarantees eventually belong in engineered systems, and its error handling exists but has to be configured | the work is varied and judgement-shaped, complementing the stack that runs the defined processes rather than competing with it |
Tasks
Automation & agents
Make is the visual power tool of mainstream automation
Make is the visual power tool of mainstream automation. Scenarios draw as flowcharts with branching, iteration and error paths that simpler platforms hide or charge heavily for, credit-based pricing keeps complex high-volume automation affordable, and AI agents now live inside the scenario builder as reusable components with their reasoning visible step by step. It rewards people who enjoy building the machine, where the automation has real logic in it and per-task maths matters at volume. The power has a learning curve, so one-trigger-one-action needs are faster on the simpler platforms, credit billing still compounds at extreme volume, and visual complexity becomes invisible complexity, so a scenario nobody documented is a bus-factor risk worth writing down.
Example tasks
- Build branching, multi-path scenarios visually
- Iterate over datasets and handle errors explicitly
- Connect thousands of apps with transformation between them
- Add AI steps for classification, drafting and decisions inside flows
- Deploy shareable agents with step-by-step reasoning visible
Limits
The visual power has a learning curve, and one-trigger-one-action needs are faster elsewhere: buying Make for simple zaps is buying a lathe to sharpen pencils.
Compares
| vs | Pick Make when | Pick the other when |
|---|---|---|
| ZapierFull comparison → | Make builds branching, multi-path scenarios visually, iterates over a dataset and handles its errors explicitly rather than letting a failed run disappear quietly | the apps must simply connect, nobody will maintain infrastructure, and speed beats cost per task |
| n8nFull comparison → | Make connects thousands of applications with real transformation between them, so the data arriving at the second app is already in the shape that app needs | you can run a server, volume makes metered pricing hurt, and code steps are a feature rather than an escape hatch |
| GumloopFull comparison → | Make keeps AI inside the scenario as reusable components whose reasoning stays visible step by step, so a flow that makes decisions can still be inspected by whoever inherits it | AI operations are the centre of the canvas rather than steps within it, and the run covers a whole batch |
| BardeenFull comparison → | Make's visual power carries a learning curve, and buying it for simple one-trigger-one-action work is buying a lathe to sharpen pencils | the work is visible in front of you, scraping pages into sheets from inside the tab you are already looking at |
| ManusFull comparison → | Make's credit-based pricing keeps complex high-volume automation affordable, which is the arithmetic that decides whether a process runs constantly or occasionally | you want to hand over a whole task rather than build the automation that performs it |
| LindyFull comparison → | Make is the visual power tool of mainstream automation, where the process is drawn as a flowchart instead of described in sentences | you want to hand over a role rather than a task: the recurring, judgement-adjacent loop of triaging mail, coordinating meetings and chasing follow-ups |
| CrewAIFull comparison → | Make draws the branching, iteration and error paths that simpler platforms hide or charge heavily for, without any of it being written as code | you are engineering a multi-agent system, not configuring an automation product |
| LangChainFull comparison → | Make adds the AI step for classification, drafting and decisions inside a flow that already exists, rather than making the agent the thing being built | you are coding agent systems and want maximum control with maximum ecosystem |
| Relevance AIFull comparison → | Make rewards the person who enjoys building the machine, where the satisfaction and the skill are both in the construction | whole business functions need agent coverage and nobody is writing code |
| OpenClawFull comparison → | 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 | the agent should be reached through WhatsApp, Telegram or Slack rather than through a builder somebody has to open |
| GensparkFull comparison → | Make's credit billing compounds at extreme volume, so a scenario's cost wants sizing against how often it will actually fire | the step you need is a real outbound phone call, which no app-to-app connector performs |
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Alternatives
Same category, different strengths.
Appears in these stacks
Curated combinations this tool is part of.
Common questions
Is Make free?
There's a free tier to start; paid plans add capacity and features.
Where does Make fit best?
Make fits best in Founders & entrepreneurs and Operations; see its practice notes for how.
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Last checked: August 2026