Make.com Review: Is It Actually Cheaper Than Zapier For Scaling Agencies?

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This Make.com review answers the question many agencies ask before migrating from Zapier: is the cost saving real? The answer is potentially yes, but headline pricing does not tell the whole story. Make uses a credit-based model, while Zapier primarily bills around successful tasks, so comparing the two requires looking at how an actual workflow runs. Module execution, polling frequency, AI usage and workflow architecture can all materially change the economics. For agencies building complex, high-volume automations, Make can offer compelling value, but the advantage needs to be tested against the workflows you actually run. This Make.com review breaks down the pricing, credit consumption, AI capabilities and hidden costs agencies should understand before migrating client automations.

Updated: September 2026. All pricing verified. Credit consumption rates sourced from verified independent testing and Make’s official documentation.

Before diving into the infrastructure differences, you can calculate your own project volumes by setting up a free Make.com account here to follow along with our cost-modeling framework.

Make.com at a Glance

The August 2025 Credit Switch: What Actually Changed

On August 27, 2025, Make renamed ‘Operations’ to ‘Credits.’ For standard automations, the cost is identical — one module execution still costs one credit. The naming change is cosmetic for most workflows. The material change is in AI-related features: AI modules, Make Code (JavaScript/Python execution), and advanced data processing now consume credits variably rather than at a flat 1-credit-per-module rate.

Make AI Agents and Maia

Make has expanded beyond adding AI modules inside traditional scenarios. Make AI Agents allows users to build agent-based automations that can reason through tasks and use connected tools, while Maia brings conversational automation building directly into Make. Users can describe an automation or AI agent in plain language and watch Maia build the workflow visually inside the Scenario Builder.

This is particularly relevant for agencies with less technical team members because it reduces some of the learning curve traditionally associated with Make’s visual canvas. Maia is available across Make plans, with paid plans providing ongoing access, while Free users receive limited trial access. AI Agent usage can consume Make credits based on operations and, when using Make’s AI Provider, token consumption.

Make also now offers an MCP Server, allowing compatible AI clients to connect with Make scenarios. Together, Maia, AI Agents and MCP make Make a broader AI automation platform than it was when this review was originally written.

Make Code (JS/Python): Bills at 2 credits per second of execution time. A JavaScript module that runs for 3 seconds consumes 6 credits per run. At 2,000 runs/month, that single module costs 12,000 credits — already exceeding the Core plan’s 10,000 credit base. Teams running custom code modules need to audit execution time and credit consumption before assuming Core covers their workflow.

AI credit consumption now depends on the connection type. Third-party AI apps such as OpenAI, Anthropic Claude and Google Gemini generally consume 1 Make credit per operation when connected using your own provider account, while token charges are paid directly to the AI provider. Make’s built-in AI features, including Make AI Agents and Make AI Toolkit when using Make’s AI Provider, can consume credits dynamically based on the model and the number of input and output tokens processed. Agencies should therefore check both the AI feature and connection type before estimating workflow cost.

Make.com Review: Pricing Plans and What Each Actually Delivers

Free Plan: Real Utility, Real Limits

1,000 credits/month, 2 active scenarios, 15-minute minimum scheduling interval. The 15-minute interval kills time-sensitive workflows. A lead notification scenario that needs to fire within minutes of a form submission is useless on the free plan. The 2 active scenario cap means you cannot test a full client workflow stack without deactivating something else. Use the free plan to learn the visual canvas and test credit consumption on a sample workflow — not to run production automations.

Core Plan ($9/month Annual): The Right Entry Point

10,000 credits, unlimited scenarios, 1-minute intervals, webhook support. This is where a Make.com review verdict changes from ‘interesting’ to ‘compelling.’ At $9/month, Core delivers 10,000 credits versus Zapier Starter’s 750 tasks at $19.99/month — a 1,233% more credit volume for 55% less money. A standard 4-step lead routing workflow (form → CRM → Slack → email) running 2,500 times/month consumes 10,000 credits precisely. Core handles it. Zapier Starter can’t — it forces you to the $49/month Professional tier for the same volume.

Core gaps: No priority execution, no custom variables, no full-text execution log search. For agencies debugging complex client workflows, the inability to search execution logs by content is a real operational friction. Pro at $16/month adds those three features without changing the credit count.

Pro Plan ($16/month Annual): The Agency Debug Tier

Same 10,000 credits as Core. Three added features: priority execution, custom variables, full execution log search. Priority execution matters when Make’s infrastructure is under load — your scenarios queue ahead of Core and free tier runs. For client-facing workflows where a 30-minute delay causes a visible failure, priority execution is worth the $7/month upgrade. Full execution log search is the feature that saves hours of debugging time — filter logs by scenario name, module, or data value without scrolling through thousands of individual runs.

Teams Plan ($38/month at 10K Credits): The Collaboration Tier

Teams starts at $38 per month at the 10,000-credit level. It includes the capabilities of Pro while adding team-oriented features such as teams and team roles, along with the ability to create and share scenario templates. This makes Teams the more relevant Make plan for agencies where multiple people need structured access to automation assets and workflows.

Unlike the previous per-seat assumption, agencies should not multiply the Teams subscription price by the number of users. The more important cost variable is automation usage: as workflow volume increases, the required credit allowance can increase the subscription cost. Agencies should therefore choose Teams for its collaboration and governance capabilities, then size the credit allowance around actual scenario consumption.

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Stop wrestling with messy webhook parameters, API rate limits, and broken variable fields. We have packaged our internal Production-Ready Agency Lead Enrichment Engine into a plug-and-play architectural blueprint.

This verified configuration bridges Make.com, Clay, and Notion perfectly:

• Instantly Captures: Structural webform submissions from any standard inbound framework.
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• CRM Structuring: Hands off pristine data rows straight into your central Notion Workspace database without duplicate errors.

What is Included in Your Vault Pass:

1. Raw .json Configuration File: Ready for instant import directly into Make.com.
2. Universal Schema Document: Explicit setup instructions for matching column configurations.
3. Troubleshooting Matrix: Field-by-field optimization map to avoid common API validation errors.

Make.com Review: Head-to-Head Cost vs Zapier

The comparison that answers the agency migration question.

The Polling Tax: The Hidden Cost That Breaks the Math

Polling is when Make checks a trigger source for new data on a schedule rather than receiving data through an instant trigger. Make charges 1 credit each time a polling trigger runs, even when the check returns no new data. A scenario polling every minute therefore runs 1,440 trigger checks per day, or approximately 43,200 in a 30-day month. At that frequency, the trigger checks alone would exceed a 10,000-credit monthly allowance before downstream workflow actions are counted.

Where the source application supports an instant trigger or webhook, use it instead of frequent polling. Instant triggers respond when new data arrives rather than repeatedly checking the source on a schedule, which can substantially reduce unnecessary trigger executions.

You can build webhooks natively inside the Make.com Core Plan.

Why it matters for agencies: Not every application offers an instant trigger, so some workflows will still require scheduled polling. Five scenarios polling every minute would generate approximately 216,000 trigger checks in a 30-day month, before counting downstream actions when new data is found. That does not automatically mean an agency needs the Teams plan, but it does mean the required credit allowance can increase substantially. Trigger frequency should therefore be part of every Make cost model.

The fix: Match polling frequency to the actual business requirement rather than automatically using the shortest available interval. A polling trigger running every 15 minutes executes approximately 2,880 times in a 30-day month, compared with 43,200 times at one-minute intervals — a reduction of about 93% in trigger-check credits. Audit every polling scenario and use the slowest interval that still meets the workflow’s operational requirement.

The Visual Canvas: Make.com’s Genuine Technical Advantage

The visual builder is one of Make.com’s strongest advantages. Scenarios are laid out as flowcharts on a visual canvas, making it easier to see how data moves through complex automations. Routers create multiple paths, iterators split arrays into individual bundles, aggregators bring multiple bundles back together, and error handlers give teams control over what happens when a module fails. For agencies building complex multi-step workflows, having this logic visible on one canvas can make scenarios easier to design, troubleshoot and explain.

The branching advantage: Make’s routers and filters do not consume credits themselves; credit usage comes primarily from the modules that actually execute within the scenario. This can make complex conditional workflows efficient because branches that do not run do not generate downstream module executions. Zapier follows a broadly similar principle with Paths and Filters: those conditional steps do not consume tasks, while successful action steps within the path that actually runs do. The real Make advantage here is therefore less about uniquely avoiding charges for unused branches and more about the flexibility and visibility of building complex branching logic on its visual canvas.

The learning curve: Make gives users more control over complex workflow logic, but that flexibility comes with a steeper learning curve. Concepts such as bundles, mapping, routers, iterators and aggregators can take time to understand, particularly for users coming from simpler linear automation builders. Make Academy provides free training, while newer AI-assisted tools such as Maia can help users build and modify automations through natural-language instructions. Agencies should still expect complex production scenarios to require a working understanding of how data moves through Make.

AI on Make.com: How Credit Consumption Actually Works

Make AI Provider vs Your Own AI Provider

Make supports several ways to add AI to a scenario, and the credit model depends on how the AI connection is configured. Third-party AI apps such as OpenAI, Anthropic Claude and Google Gemini can use a custom provider connection on paid Make plans. In this configuration, Make generally charges 1 credit per operation and the AI provider bills token usage separately.

Make also offers built-in AI capabilities such as Make AI Agents and Make AI Toolkit using Make’s AI Provider. These can use dynamic credit consumption based on the selected model and the number of input and output tokens processed. Make revised this calculation in August 2026, with separate credit rates for input and output tokens, so fixed estimates such as “3–8 credits per AI call” are not reliable across models and prompts.

Agencies running AI-heavy workflows should compare the available connection options rather than automatically routing every request through an HTTP module. Using your own AI provider connection can make Make credit consumption more predictable, while Make’s managed AI Provider reduces the need to maintain separate provider accounts and API connections. The better option depends on model choice, token volume, workflow complexity and the agency’s existing AI provider setup.

Make Code: JS and Python in the Workflow

2 credits per second of execution time. A JavaScript data transformation function that runs in 500ms costs 1 credit (rounds up to nearest second). A Python script processing a large JSON payload that runs in 4 seconds costs 8 credits per execution. Profile every Make Code module’s execution time during development — add a test log at the end that records execution time, then calculate monthly credit cost at your expected run frequency before deploying to production.

Make.com for Agencies: Where It Wins and Where It Doesn’t

Where Make.com Wins for Agencies

• Complex visual workflows: Make’s canvas, routers, filters, iterators and aggregators give agencies considerable control over multi-step automation logic while keeping the workflow visible in one place.
• Reusable workflow architecture: Scenarios can be exported as blueprints and reused across accounts, while Teams adds the ability to create and share scenario templates. This is useful for agencies deploying similar automation architectures across multiple clients.
• Data-heavy automation: Iterators, aggregators, mapping tools and built-in data transformation capabilities make Make particularly well suited to workflows that process arrays, multiple records or structured data.
• Conditional client workflows: Routers and filters allow agencies to create different workflow paths based on lead type, client tier, campaign, response data or other conditions without splitting every variation into a completely separate automation.
• Event-driven automation: Where an application supports an instant trigger or webhook, scenarios can respond to incoming events without repeatedly polling the source. This can make credit consumption considerably more efficient than frequently scheduled polling workflows.

Where Make.com Loses for Agencies

• Steeper learning curve: Make’s flexibility comes with greater technical complexity. Routers, iterators, aggregators, bundles and data mapping give experienced users more control, but can make complex scenarios harder for non-technical team members or clients to maintain.
• Smaller native integration catalog than Zapier: Make supports 3,000+ apps, but Zapier’s catalog now exceeds 9,000. For agencies working with niche or industry-specific software, that difference can matter. Check the complete client application stack before migrating and assess whether missing native integrations can be handled reliably through APIs, HTTP modules or webhooks.
• Polling-heavy workflows: Make charges credits when polling triggers check for new data, even when nothing new is returned. Workflows that depend heavily on frequent polling can therefore consume credits faster than expected and should be modelled before migration.
• More responsibility for workflow architecture: Make gives users considerable control over routing, error handling and data transformation. That flexibility is valuable, but poorly designed scenarios can also consume unnecessary credits or become difficult to troubleshoot. Agencies need stronger internal automation discipline as their Make environment grows.

Data Portability and Switching Cost

• Scenario blueprints: Make allows scenarios to be exported as JSON blueprints and imported into another Make account. Blueprints preserve the scenario structure, modules, settings and mapped values, making them useful for backup, replication and multi-client deployments.
• Reusable templates: Teams users can create and share scenario templates, giving agencies another way to standardise recurring automation architectures across clients and internal teams.
• No direct cross-platform portability: Make blueprints are designed for Make and cannot simply be imported into Zapier, n8n or another automation platform. Moving away from Make therefore usually means rebuilding and testing workflows on the destination platform.
• Connections require attention: Imported blueprints do not automatically transfer access to another account’s app connections. Connections need to be configured or re-authorised in the destination environment before the imported scenario can run correctly.
• Complexity increases switching cost: The more a scenario relies on routers, iterators, aggregators, custom functions and application-specific mappings, the more work is likely to be involved in recreating it elsewhere. Agencies should therefore treat workflow documentation and blueprint backups as part of their automation governance.

Make.com Review Verdict: Make or Zapier?

TSA Final Verdict: Make.com Review Bottom Line

Make.com is a strong choice for agencies that need more control over complex automation without moving to a self-hosted platform. Its visual canvas, routers, iterators, aggregators, reusable blueprints and data-mapping capabilities make it particularly well suited to multi-step workflows that would become difficult to manage in a simpler linear automation builder.

Cost remains one of Make’s attractions, but agencies should avoid assuming that a Make credit and a Zapier task are directly interchangeable. Actual economics depend on how many modules execute, how frequently scenarios poll for data, how AI is configured and how efficiently the workflow is architected. Make can offer compelling economics at scale, but the saving should be demonstrated on the agency’s real workflows rather than assumed from headline plan allowances.

Zapier remains the stronger option in some situations. Its larger native integration catalog can matter when clients use niche applications, and its simpler workflow experience may be easier for non-technical teams and client handoffs. Agencies with a substantial existing Zapier estate should also include migration, testing and retraining costs when evaluating a switch.

Make has also become more interesting as an AI automation platform. Make AI Agents, Maia and MCP Server extend the platform beyond conventional scenario building, while different AI connection options give agencies more flexibility over how AI usage is billed. That makes Make increasingly relevant for agencies building both traditional workflow automation and AI-driven processes.

The pre-migration checklist:

• Audit every trigger type — identify instant and polling triggers and calculate how frequently polling scenarios will execute.
• Map the complete workflow — count the modules that actually execute during a typical run rather than comparing headline credits with Zapier tasks.
• Audit AI usage — identify whether each AI step uses Make’s AI Provider or your own provider connection and model the resulting credit and token costs.
• Check every required integration — confirm that critical client applications are available natively or can be connected reliably through APIs, HTTP modules or webhooks.
• Assess collaboration requirements — determine whether your agency needs Teams features such as team roles and shared scenario templates.
• Run a controlled migration test — move one representative high-volume or technically complex workflow first, measure actual credit consumption and operational effort, and use those results to model a wider migration.

Start with the Free plan if you are evaluating Make for the first time. Its 1,000 monthly credits are enough to explore the visual builder and test a representative workflow before committing to a paid plan. For an agency considering migration from Zapier, the goal of that test should not simply be to prove that Make is cheaper. It should establish whether Make delivers the right combination of cost, maintainability, integration coverage and workflow flexibility for your actual automation stack.