Lindy and Make both aim to simplify automation, but they approach it from opposite directions. Lindy was built around AI agents that interpret plain-language instructions and execute tasks automatically. Make uses a visual workflow builder that lets users design processes step by step, recently adding an AI beta for natural-language automation.
Both tools now blend automation and intelligence, but their experiences remain distinct. Lindy focuses on hands-free setup through AI reasoning. Make emphasizes manual control and workflow precision. These differences shape how each handles pricing, integrations, ease of use, customization, and scalability.
What is Lindy?
Lindy is an AI-powered automation platform that turns plain-language instructions into action. Users describe what they want, and Lindy’s AI agents handle the logic behind the scenes. It’s built for teams that want to automate without coding or managing complex systems.
Each workflow runs as an intelligent agent that understands context, adapts to change, and completes multi-step tasks automatically. Lindy connects with thousands of popular tools for communication, scheduling, and operations, all within a managed cloud environment.
Strengths
- AI agents interpret goals and handle logic automatically.
- Connects with 3,000+ business apps through AI-driven integrations.
- Adapts workflows as conditions change.
- Includes a built-in Code skill for inline scripting.
- Fully cloud-based with no hosting or maintenance required.
Drawbacks
- Less customizable than open-source or logic-heavy platforms.
- Limited control over data storage and hosting.
- Credit-based pricing with fixed monthly limits; workflows pause when credits run out.
- Fewer options for manual branching or advanced logic.
Dive deeper: Lindy Review (2025): Can AI Agents Really Run Your Workflows?
What is Make?
Make is a visual automation platform for users who prefer full control. It uses a drag-and-drop editor where each step represents an app, data source, or logic path. In April 2025, the platform introduced Make AI Agents in beta, adding natural-language automation to its visual builder. The feature is still in testing, blending Make’s manual precision with early AI-powered setup.
Strengths
- Visual builder with detailed workflow mapping and branching.
- Built-in Code module for advanced data handling.
- 3,000+ integrations with strong API and webhook support.
- Predictable pricing based on operations.
- Early AI beta adds basic natural-language automation for testing.
Drawbacks
- Steeper learning curve for new or non-technical users.
- Manual setup and ongoing maintenance required.
- Credit-based pricing has fixed limits with no overages, which means workflows can pause mid-month if the credit balance runs out.
- AI features are in beta and less integrated than in Lindy.
- Unused credits do not roll over between billing cycles.
Dive deeper: Make Review (2025): Is it the Best Workflow Automation Tool for Your Business?
Lindy vs Make: Feature Comparison
Lindy and Make share the same goal of automating work but take very different paths to get there. Lindy relies on AI agents that act through natural language, while Make focuses on visual workflow design with logic-based controls. Make’s AI beta narrows the gap by adding early conversational automation to its existing builder.
| Factor | Lindy | Make |
|---|---|---|
| Pricing | Credit-based pricing with fixed monthly limits; workflows pause when credits are used up. | Credit-based pricing with fixed monthly limits; workflows pause when credits are used up. |
| Integrations | 3,000+ integrations including popular business apps through AI-driven connections. | 3,000+ pre-built integrations with detailed configuration options. |
| Ease of use | Plain-language automation designed for quick setup. | Visual drag-and-drop builder that offers more manual control. |
| Customization | Centered on AI-driven automation with guided flexibility through Code and API actions. | Highly customizable with filters, branching logic, and scripting. |
| Scalability | Cloud-based platform with fixed plan limits that expand through upgrades. | Cloud-based platform with fixed plan limits that expand through upgrades. |
Pricing comparison
Lindy and Make both use fixed, plan-based pricing, meaning costs stay consistent month to month with no surprise overages. The difference lies in how each platform measures usage. Lindy’s model is built on credits, which represent units of automated work, and each task draws from the available balance. Credit consumption varies slightly based on task complexity and the resources an automation uses. Make, by contrast, measures usage through operations, though certain AI modules and data-heavy actions now consume credits dynamically rather than at a flat rate. Both systems have hard plan limits that define monthly capacity, giving users clear control over automation volume and cost.
Make pricing tiers
| Plan | Monthly pricing | Annual (per month) | Credits included |
|---|---|---|---|
| Free | $0 | $0 | 1,000 |
| Core | $10.59 | $9 | 10,000 |
| Pro | $18.82 | $16 | 10,000 |
| Teams | $34.12 | $29 | 10,000 |
| Enterprise | Custom | Custom | Custom |
Lindy pricing tiers
| Plan | Monthly pricing | Annual pricing | Included credits |
|---|---|---|---|
| Free | $0 | $0 | 400 |
| Pro | $149.99 | $99.99 | 10,000 |
| Business | $299.99 | $199.99 | 10,000 |
| Enterprise | Custom | Custom | Custom |
How usage is measured
Lindy and Make measure activity differently, which affects how plan limits are reached. Both can run the same workflow, but they calculate usage in distinct ways. Lindy uses credits that are deducted based on how intensive each task is, while Make counts every workflow step as one operation.
| Example workflow: Email follow-up → Spreadsheet update → Slack message | Lindy (credits) | Make (credits) |
|---|---|---|
| Steps in workflow | 3 | 3 |
| Usage per run | Varies by task complexity (simple tasks use fewer credits, complex tasks use more) | Varies by task complexity (simple tasks use fewer credits, complex tasks use more) |
| Runs per month | 100 | 100 |
| Total usage | Fixed credit cap; consumption varies by workflow complexity | Fixed credit cap; consumption varies by workflow complexity |
Both platforms perform the same number of steps, but Lindy’s credit usage depends on how intensive each action is. Make’s operation count increases linearly with workflow steps, which makes its usage limits easier to predict.
Integrations: Side-by-side comparison
Integration depth shapes how each platform connects apps and data across a user’s workspace. Lindy focuses on simplicity, using AI agents that automatically link common business tools through natural language. Make takes a more hands-on approach with a large library of pre-built integrations, letting users configure every step through a visual editor or API.
The main difference lies in flexibility. Lindy automates the setup process, while Make gives users full control to build, test, and refine connections as workflows grow more complex.
| Category | Lindy | Make |
|---|---|---|
| Number of integrations | 3,000+ | 3,000+ |
| Built-in code module | Yes. Includes a built-in Code skill that lets users write and run code directly within workflows. | Yes. Make includes a native Code module supporting JavaScript for inline logic and data handling. |
| API or webhook support | Yes. Lindy supports custom HTTP requests through the “HTTP Request” skill. | Yes. Make offers full API access, custom webhooks, and HTTP modules for bidirectional communication. |
| Ease of setup | AI agents create and manage integrations automatically. | Manual setup through drag-and-drop mapping and configuration. |
| Support and extensions | Guided AI setup with centralized platform support. | Active ecosystem with extensive templates and API documentation. |
Ease of use: Side-by-side comparison
How simple a platform is to learn and use often shapes how quickly users can build their first automation. Lindy takes a conversational approach, letting users describe what they want to automate while its AI agents handle the details in the background. Make uses a visual editor that gives users a clear view of each step, offering flexibility but requiring more setup time.
The main difference lies in the level of control. Lindy focuses on speed and accessibility, while Make caters to users who prefer to plan and refine workflows manually.
| Category | Lindy | Make |
|---|---|---|
| Interface | Conversational interface powered by AI agents that handle logic behind the scenes. | Visual drag-and-drop editor with modular workflow design. |
| Learning curve | Low; built for non-technical users. | Moderate; requires some familiarity with workflow logic. |
| Setup time | Fast, with AI handling most configuration. | Longer, especially for complex or multi-step workflows. |
| Monitoring | AI agents track progress automatically. | Manual run history and scenario monitoring. |
Customization and flexibility: Side-by-side comparison
Customization determines how much control users have over how automations run and adapt over time. Lindy relies on AI agents to interpret goals and handle logic automatically, limiting the need for manual setup but offering fewer options for detailed configuration. Make, on the other hand, gives users full visibility and control, allowing for complex logic, branching paths, and custom scripting.
The core distinction is between automation guided by AI reasoning and automation built by user-defined logic. Lindy prioritizes accessibility and simplicity, while Make provides the depth needed for advanced workflows and technical teams.
| Category | Lindy | Make |
|---|---|---|
| Workflow logic | AI builds and manages logic automatically. | Manual control with filters, conditions, and branching. |
| Code and scripting | Supported through the built-in Code skill for inline scripting and logic. | Supports custom scripting and advanced expressions. |
| Extensibility | Extensible through HTTP Request actions, webhooks, and AI workflows, though customization is guided rather than open-source. | Highly extensible through APIs, webhooks, and custom modules. |
| Data ownership | Managed within Lindy’s cloud environment. | User-managed, with options for on-platform or API-based control. |
Scalability and reliability: Side-by-side comparison
Scalability determines how well each platform supports growing workloads, while reliability reflects how consistently automations perform at higher volumes. Both Lindy and Make use fixed, plan-based models that set clear limits on monthly activity. Lindy’s plans are defined by credits, while Make’s are based on operations. When users outgrow those limits, scaling occurs by upgrading to a higher plan rather than through automatic expansion.
Both platforms deliver stable performance within their limits. Lindy’s managed cloud handles system reliability, while Make offers flexibility through detailed workflow monitoring and resource control.
| Category | Lindy | Make |
|---|---|---|
| Hosting | Fully managed cloud environment. | Cloud-based platform with adjustable usage limits. |
| Scaling | Plan-based scaling by credit limit; higher tiers expand capacity. | Plan-based scaling by operation limit; higher tiers expand capacity. |
| Reliability | Consistent performance managed by Lindy’s infrastructure. | Reliable performance with manual optimization options. |
| Maintenance | No setup or maintenance required. | Requires occasional workflow monitoring and adjustments. |
Bottom line: Which platform is right for you?
Lindy is best for teams that want automation handled through natural language, without setup or maintenance. Its AI agents interpret context, manage workflows, and adapt to changing conditions automatically.
Make is a strong choice for those who prefer to design and control every step of their automations. Its visual editor, branching logic, and advanced configuration options allow teams to build highly detailed workflows that grow with their needs. The platform’s new AI beta adds natural-language automation but still maintains Make’s foundation of structure and precision.
Your decision comes down to how you want automation to work for you. Lindy prioritizes accessibility and hands-free setup powered by AI reasoning, while Make emphasizes flexibility, visibility, and technical depth.
