Make

What is Make?

Make is a web-based workflow automation platform for connecting applications and coordinating multi-step business processes. Its main workspace is a visual scenario builder: users place modules on a canvas, connect them, map data between steps, and add filters, routers, transformations, and actions.

The platform is designed for practical automation rather than primarily conversational use. A scenario might receive a webhook, look up or transform records, route the data according to conditions, call an API, update a CRM, and send a notification. AI can be added as one part of that process, or used within configurable agents that have instructions, tools, scenarios, knowledge files, and MCP connections.

What Make actually does

Visual, multi-step scenarios

Make supports workflows that are more involved than a single app-to-app connection. Triggers can come from application events, schedules, forms, emails, mailhooks, webhooks, or API requests. Subsequent modules can filter, aggregate, transform, enrich, route, and deliver information to other systems.

This visual structure makes the path of data easier to inspect than a collection of disconnected scripts, while still allowing low-code techniques such as HTTP requests, custom applications, and API calls. Users can test scenarios, inspect execution details, monitor errors, and review credit usage.

App integration and custom connectivity

Make provides access to more than 3,000 app integrations across business, productivity, communication, marketing, development, data, and AI services. Examples in the available ecosystem include Google Workspace, Microsoft services, Salesforce, HubSpot, Slack, Notion, Airtable, Shopify, Zendesk, Zoom, OpenAI, and Anthropic.

When a ready-made connector is insufficient, users can connect services through HTTP, webhooks, the REST API, or custom apps. This is an important distinction from simpler automation products: Make is intended to accommodate custom data flows and unusual integration requirements, although that flexibility increases configuration and maintenance work.

AI workflows and agents

AI is an extension of Make's automation model rather than the product's sole interface. AI steps can classify or summarize content, generate text or structured data, enrich records, and support research, reporting, or customer-service processes. The exact model availability depends on the feature and connection, with Make's AI Provider, OpenAI, Anthropic, and other provider connections available in different contexts.

Make also supports configurable AI agents. An agent can be given instructions, tools, scenarios, supported knowledge files, and MCP servers. This allows an organization to place an AI decision or interaction inside a broader process instead of treating the agent as a standalone chatbot. For readers comparing agent-building products, Botpress, Dify, and Langflow are adjacent tools with a more explicit focus on agent or workflow construction.

How people use Make

Make is useful when a process crosses several systems or requires conditional logic. Common examples include synchronizing CRM, ecommerce, marketing, support, and finance data; processing form submissions and inbound emails; routing notifications; enriching records; and producing reports from information collected across multiple applications.

A team might use a webhook to receive a new order, check inventory or customer information, send selected data to another service, and notify staff only when a condition is met. A marketing workflow could collect leads, enrich them, classify them with an AI step, update a CRM, and send different follow-up messages. An operations team could use scheduled scenarios to reconcile records or create recurring reports.

Make can also expose scenarios through its MCP Server or connect external MCP tools to agents. This is relevant for teams experimenting with AI clients that need controlled access to business workflows, although the usefulness and risk depend on how tools, permissions, data, and provider connections are configured.

Who is Make for?

• Operations and business teams: Automating repeatable processes that involve several systems and conditional steps.
• Marketers and sales teams: Moving leads, campaign data, customer records, and notifications between platforms.
• Customer-support teams: Routing requests, enriching tickets, connecting support systems, and generating AI-assisted classifications or summaries.
• Developers and IT professionals: Combining APIs, webhooks, custom apps, and visual orchestration without building every integration from scratch.
• Agencies and consultants: Creating reusable scenarios for different clients and business processes.
• Enterprises: Managing shared automation with features such as SSO, role permissions, audit logs, analytics, governance controls, and selected enterprise security options.

It is less suitable for someone seeking a general-purpose conversational assistant, a dedicated media-generation application, or a very simple automation that needs no branching or data transformation. Users who want a more straightforward trigger-and-action experience may find Make's visual flexibility unnecessary.

Pricing and access

Make has a free plan with no stated time limit and up to 1,000 credits per month. It includes the visual workflow builder, access to more than 3,000 apps, routers and filters, and a 15-minute minimum interval between scheduled runs.

Paid access begins with the Core plan at $12 per month for 10,000 credits when billed monthly. Pro is listed at $21 per month and Teams at $38 per month for the same monthly credit allocation when billed monthly; annual billing provides savings. Enterprise pricing is customized. Additional credits can incur extra charges.

The credit system is central to evaluating cost. Each module action generally counts as one credit, so a scenario with many steps or frequent executions can consume credits quickly. Plans also differ in execution, data-transfer, scheduling, collaboration, and administration limits. As a result, the advertised starting price is not a reliable estimate for every workflow, particularly at high volume.

Platforms, integrations, and workflow fit

Make is primarily a web application with REST API, webhook, third-party app, and MCP connectivity. It supports file, text, JSON, CSV, PDF, API, and application-event inputs, and can produce updated records, notifications, API requests, transformed data, reports, AI-generated text, structured data, and other workflow actions.

The service requires an account, and collaboration features support shared workspaces and administrative controls. The research states that Make's native Chrome app and browser extension were retired on August 31, 2026, so the web platform and its API-based connections are the relevant access methods. Users comparing workflow automation options can also look at n8n or Zapier Agents, but Make's defining emphasis is its visual control over complex scenarios, routing, and data transformation.

Limitations and privacy considerations

Make's main practical limitation is complexity. Building reliable scenarios requires careful field mapping, testing, error handling, monitoring, and maintenance. Credit-based billing can make costs difficult to forecast, and AI steps may introduce additional provider or token costs. Feature availability also varies by plan.

Privacy depends on both Make and the services connected to a scenario. Make processes account, usage, configuration, and customer data to operate and secure the service. Its published security materials describe encryption, access controls, audit logging, GDPR-oriented safeguards, SOC 2 Type II, SOC 3, ISO 27001, enterprise SSO, role-based access, and data-region options. However, prompts, retrieved knowledge, files, and other content used in AI workflows may be sent to the selected AI provider, so the provider's retention and training terms must be reviewed separately.

Make's privacy materials state that Google Workspace APIs are not used to develop, improve, or train generalized AI or machine-learning models. That statement does not automatically apply to every external AI provider connected to Make. Organizations handling sensitive information should limit permissions, review each provider connection, and avoid assuming that a workflow's data has one uniform retention policy across all services.

Is Make a good fit?

Make is a strong fit when a team needs visual, multi-step automation across many applications and wants branching logic, transformations, webhooks, custom integrations, AI steps, or enterprise administration. It is particularly useful when the process is too structured or interconnected for a basic integration and not worth implementing entirely as custom software.

It is a weaker fit for users who want predictable unlimited usage, a standalone AI assistant, or a minimal setup. The platform's flexibility comes with a learning curve, operational responsibility, and usage-based cost management. Teams should estimate credits from realistic execution volumes and test the complete data path before committing to a large deployment.