Make Review 2026: Pricing, AI Agents, Credits & Pros/Cons

Make is a visual automation and AI-agent platform for connecting applications, moving data, orchestrating multi-step workflows and adding AI reasoning without building every integration from scratch. Its 2026 platform combines the familiar scenario canvas with Make AI Agents, Maia, APIs, Model Context Protocol tools and more than 3,000 pre-built apps.

The defining characteristic is visibility. Traditional Make scenarios show every trigger, action, router, filter and transformation on a visual canvas. The new AI Agents are built into that same environment, so teams can combine deterministic workflow logic with adaptive decision-making while still seeing which tools an agent used and how it reasoned. That makes Make especially attractive to operations, marketing, revenue and technical teams that want automation they can inspect rather than a black-box agent.

What is Make?

Make is a cloud automation platform that lets users design workflows visually. A workflow in Make is called a scenario. Scenarios connect modules representing triggers, actions, searches, transformations, routers and other logic. A typical scenario might collect a form submission, enrich the contact with external data, create or update a CRM record, notify a Slack channel and send a personalized email.

Make has expanded this model to AI. Teams can connect OpenAI, Anthropic, Gemini, Perplexity and hundreds of other AI applications, or build Make AI Agents that decide which tools to call based on the work they receive. The result is a platform that supports both predictable automation and judgment-heavy workflows.

Who is Make best for?

Make features reviewed

Visual Scenario Builder

The scenario builder is Make's core advantage. Users add application modules to a visual canvas, map fields between them and connect the modules into a sequence. Routers can create different paths, filters control when a path runs and iterators or aggregators handle lists of data.

This visual structure makes complex workflows easier to inspect than a long block of code. It also creates a learning curve: advanced scenarios can become large, and users need to understand bundles, mapping, iterators, aggregators and error handling to build efficiently.

Make AI Agents

Make AI Agents are built directly inside the scenario canvas. They can reason over unstructured input, select tools and take actions across connected applications. Make's current product emphasizes transparency: the Reasoning panel shows decisions step by step rather than hiding the agent behind a single opaque output.

Teams can also combine agents with deterministic logic. A scenario can use an AI Agent for judgment, then pass the result through a router, approval step or fixed business rule. Make recommends using ordinary automation when a process simply needs to execute and using an agent when the process requires thinking or interpretation.

Manual approvals and guardrails

AI agents can be surrounded by rules, approval points and stop conditions. This is important for workflows that update customer records, send external messages or take financially meaningful actions. The visual canvas lets teams see where the agent is allowed to decide and where deterministic control takes over.

Maia by Make

Maia is Make's natural-language automation assistant. It helps users build and troubleshoot workflows and AI-agent configurations conversationally. The goal is to reduce the amount of platform-specific knowledge required to get from an idea to a functioning scenario.

3,000+ app integrations

Make advertises more than 3,000 pre-built applications. Current examples include OpenAI, HubSpot, Salesforce, Slack, Google Sheets, monday.com, NetSuite, Canva, Perplexity and many other business systems. The ecosystem also includes hundreds of AI applications.

When a dedicated connector is unavailable, Make can connect through HTTP, webhooks, APIs or MCP. That breadth reduces the need to build one-off integration code for every system.

Make MCP Server

Make's MCP Server turns scenarios into callable tools for MCP-compatible clients such as ChatGPT, Claude and other AI interfaces. Make currently says those tools can reach workflows across more than 3,000 apps and 30,000 actions.

A team can therefore build a controlled scenario once and expose it to several AI assistants rather than giving every assistant direct credentials to every business system.

MCP Client

Make also supports the reverse direction: scenarios can call tools exposed by remote MCP servers. This lets Make orchestrate external AI tools and data sources inside the same workflow canvas.

Routers, filters and data transformation

Routers split a scenario into multiple paths, while filters determine which records follow which path. Data transformation tools can clean, restructure or aggregate data between systems. These capabilities are useful when a business process involves exceptions rather than a single straight line.

Make API

Core and higher plans include access to the Make API. This lets developers and internal platforms manage scenarios or interact with Make programmatically. API access is useful for companies that want Make to function as an automation layer behind another application.

Execution logs and observability

Make records scenario runs so teams can inspect what happened when an automation succeeds or fails. Pro adds full-text execution-log search and priority scenario execution. Enterprise provides broader analytics and governance capabilities, including Make Grid for visualizing an organization's automation landscape.

Make pricing overview

Make has Free, Core, Pro, Teams and Enterprise plans. Free includes 1,000 credits per month. At the currently displayed 10,000-credit tier, Core is $12 per month, Pro $21 and Teams $38. Enterprise is custom-priced. Make says annual billing saves at least 15 percent.

How Make credits work

A Make credit generally represents an executed module action. If one scenario run performs five module actions, it can consume roughly five credits. Loops can multiply usage because a module may execute once for every item processed. AI-agent workflows can also consume credits according to the modules and tools they invoke.

This is the most important budgeting concept. Ten thousand credits do not equal ten thousand full workflow runs unless every workflow consists of a single billable module action.

Make rating breakdown

This is a research-based SearchSagar editorial score rather than a hands-on benchmark. Make scores highly for integration breadth, visual control, AI-agent transparency and security. Usability is slightly lower because advanced scenario design has a learning curve, while pricing requires users to understand how module actions multiply credit consumption.

What do Make users say?

G2's correctly matched seller profile for Make, listed under the former Integromat identity, currently shows a 4.6 out of 5 rating from more than 350 reviews. Reviewers frequently praise the breadth of integrations, workflow flexibility, visual builder and ability to automate complicated processes. Negative feedback often centers on the learning curve and the effort required to understand usage and debugging on advanced scenarios.

The review base is large enough to be useful, but automation fit is highly process-specific. Teams should test Make with one representative workflow that includes the same volume, branches and integrations expected in production.

Security and compliance

Make's current security materials list GDPR, SOC 2 Type II and SOC 3. The platform uses encryption in transit and at rest, access controls and regular security testing. Enterprise environments receive additional controls and support, and Make states its broader information-security program is aligned with ISO 27001.

Cancellation and refunds

Self-serve Make subscriptions can be changed or canceled through account billing. Paid access continues according to the current billing term. Make does not advertise a universal blanket money-back guarantee for ordinary subscription usage; refund or credit eligibility depends on applicable agreements, order terms and law.

Make alternatives to consider

Relevant approved SearchSagar alternatives include Zapier, n8n and Gumloop. Zapier offers a larger application ecosystem and a broad AI-orchestration platform with Zaps, Tables, Forms and MCP. n8n is attractive to technical teams that want code, self-hosting and workflow-execution pricing. Gumloop is a strong option for AI-first agent automation with unlimited seats on Pro.

Related Make articles

• Make Pricing 2026
• Best Make Alternatives
• Make product page

Final verdict: is Make worth it?

Make is worth considering when automation needs to be powerful without becoming invisible. The combination of a visual scenario builder, AI Agents, MCP and thousands of integrations gives teams a way to automate routine processes and agentic workflows in the same environment.

The main buyer challenge is estimating credits correctly. A $12 Core plan can be excellent value for efficient scenarios, while an unnecessarily complex workflow can burn through credits much faster than expected. The strongest Make users treat scenario design as an optimization problem: remove unnecessary modules, filter early, batch where possible and reserve agents for work that actually requires judgment.