7 Best Alternatives to Make (formerly Integromat) in 2026

Why look beyond Make (formerly Integromat)

Make, originally Integromat, offers a visual, no-code/low-code platform for automating workflows and integrating applications. Its strength lies in its ability to handle complex, multi-step scenarios, often involving conditional logic and data manipulation across hundreds of connected services. The platform is recognized for its granular control over data flow and its capacity to build highly customized automation solutions. However, organizations may seek alternatives for several reasons.

One common driver is the need for simpler, more accessible automation. While Make excels at complexity, its visual interface can present a steeper learning curve for users new to workflow design, especially compared to platforms emphasizing quick, straightforward integrations. Performance and scalability requirements can also lead teams to explore other options. For very high-volume transactions or extremely low-latency requirements, some users might find that other iPaaS solutions offer more optimized execution environments. Additionally, specific industry compliance needs, deeper native integrations with particular enterprise systems, or a desire for more advanced AI-driven automation capabilities could prompt a search for platforms with different core competencies or feature sets. Pricing models, which often scale with operations and data volume, can also be a factor for organizations managing tight budgets or unpredictable usage patterns.

Top alternatives ranked

1. 1. Zapier — Automate workflows with thousands of apps, no code required

   Zapier is a widely recognized iPaaS platform that specializes in connecting web applications to automate repetitive tasks. It provides a user-friendly interface for creating "Zaps," which are automated workflows triggered by events in one application and performing actions in another. With integrations for over 6,000 applications, Zapier offers extensive connectivity, often exceeding the number of direct integrations available on other platforms. Its primary appeal lies in its simplicity and accessibility, making it suitable for individuals and small to medium-sized businesses looking to automate straightforward tasks without requiring deep technical knowledge.

   Unlike Make, which often caters to more intricate, multi-branching scenarios, Zapier emphasizes ease of setup and a quicker path to automation for common business processes like lead management, content distribution, and data entry. It supports conditional logic and multi-step Zaps, but its visual builder is generally less complex than Make's, focusing on a linear flow. For users prioritizing rapid deployment and broad app compatibility without the need for highly customized data manipulation or complex branching logic, Zapier presents a robust alternative. It also offers features like Paths for conditional logic and Formatter for basic data transformation, expanding its utility beyond simple triggers and actions.

   • Best for: Quick, straightforward integrations; extensive app compatibility; small to medium-sized businesses; non-technical users.

   Read more about Zapier on modelroost. Official site: zapier.com.

2. 2. Workato — Enterprise-grade automation and integration for complex business processes

   Workato is an enterprise automation platform designed for complex integrations and workflow orchestration across an organization's entire tech stack. It combines iPaaS capabilities with Robotic Process Automation (RPA), API management, and AI/ML features to deliver comprehensive automation solutions. Workato is particularly strong in handling mission-critical business processes, offering robust governance, security, and scalability features tailored for large enterprises. Its platform supports both IT and business users, enabling collaboration on automation projects while maintaining control and compliance.

   Compared to Make, Workato provides a more extensive suite of enterprise-focused features, including advanced error handling, version control, role-based access, and a deeper focus on security and compliance standards required by large organizations. While both platforms offer visual builders, Workato's "recipes" can incorporate more sophisticated logic, custom code, and integration with on-premise systems through its agents. It also emphasizes real-time integration and event-driven architectures, making it suitable for scenarios requiring high data velocity and immediate synchronization across diverse systems like ERP, CRM, HRIS, and custom applications. For organizations with demanding integration requirements, stringent security policies, and a need for centralized automation governance, Workato is a compelling alternative.

   • Best for: Enterprise-level integrations; complex business process automation; IT-business collaboration; robust security and governance; real-time data synchronization.

   Read more about Workato on modelroost. Official site: workato.com.

3. 3. Tray.io — Low-code automation platform for sophisticated integrations

   Tray.io is a low-code automation platform that targets businesses needing to build sophisticated integrations and automated workflows without relying solely on developers. It offers a visual workflow builder that allows users to connect applications, manipulate data, and implement complex logic. Tray.io distinguishes itself with its focus on enabling business users and analysts to create powerful automations, often integrating with sales, marketing, and support tools, while still providing the flexibility for technical users to extend functionality with custom code or advanced API calls.

   While Make offers extensive customization, Tray.io often provides a more structured approach to building complex workflows, with an emphasis on reusability and maintainability, particularly for larger teams. Its platform includes features such as robust error handling, version control, and a comprehensive connector library, designed to support a wider range of technical and non-technical users. Tray.io excels in scenarios where businesses need to integrate disparate SaaS applications, automate data onboarding, enrich customer profiles, or streamline operational processes across departments. It strikes a balance between ease of use for citizen integrators and the power required for more technical automation challenges, making it a strong alternative for teams that have outgrown simpler tools but aren't ready for full enterprise iPaaS complexity.

   • Best for: Marketing, sales, and support automation; low-code development; cross-functional team collaboration; sophisticated SaaS integrations.

   Read more about Tray.io on modelroost. Official site: tray.io.

4. 4. OpenAI — API-first platform for integrating advanced AI capabilities

   OpenAI provides an API-first platform offering access to a suite of advanced AI models, including large language models (LLMs) like GPT-4o, for various tasks such as natural language understanding, generation, code interpretation, and multimodal processing. While not an iPaaS in the traditional sense, OpenAI's offerings serve as a powerful component within automation workflows, enabling developers to inject intelligence into their applications and integrations. Its models can be used to process unstructured data, generate dynamic content, summarize information, or classify inputs, which are critical steps in many modern automation pipelines.

   Unlike Make, which focuses on connecting applications and orchestrating data flow, OpenAI provides the cognitive layer. Developers can integrate OpenAI's APIs into their existing automation platforms (including Make itself, or alternatives like Zapier and Workato) to enhance decision-making, personalization, and data transformation capabilities. For example, an automation workflow might use OpenAI to extract key entities from an email before routing it to the correct department, or to generate personalized responses based on customer inquiries. For organizations looking to imbue their automated processes with cutting-edge AI, OpenAI offers the foundational models and tools to achieve this, making it an essential complementary or foundational technology rather than a direct, like-for-like replacement for an iPaaS.

   • Best for: Adding advanced AI capabilities (NLP, generation, reasoning) to existing workflows; developers building AI-powered applications; augmenting data processing and decision-making.

   Read more about OpenAI on modelroost. Official site: platform.openai.com.

5. 5. Hugging Face — Open-source platform for ML models and datasets

   Hugging Face is a platform and community for machine learning, primarily known for its extensive repository of open-source models, datasets, and tools for natural language processing, computer vision, and audio tasks. It offers a hub for sharing, discovering, and deploying pre-trained models, including many large language models (LLMs) and diffusion models. While not an iPaaS, Hugging Face serves as a critical resource for developers and organizations aiming to integrate custom or open-source AI models into their automation workflows, providing flexibility and control over the AI component.

   Similar to OpenAI, Hugging Face provides the AI intelligence layer rather than the integration orchestration. However, its emphasis on open-source models and community contributions allows for greater customization, fine-tuning, and often more cost-effective deployment options compared to proprietary API services. Developers can leverage Hugging Face's Transformers library, inference endpoints, or Spaces to host and run models that perform specific tasks within an automation pipeline, such as sentiment analysis, text summarization, or image classification. For organizations with in-house ML expertise or a preference for open-source solutions, Hugging Face enables the creation of highly specialized AI components that can be integrated into any workflow automation platform, offering a powerful alternative for custom AI-driven automation.

   • Best for: Integrating open-source ML models into workflows; custom AI development and fine-tuning; ML research and collaboration; cost-effective AI deployment.

   Read more about Hugging Face on modelroost. Official site: huggingface.co.

6. 6. GitHub Copilot — AI pair programmer for accelerating software development

   GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI, designed to help developers write code faster and more efficiently. It provides real-time code suggestions, completes lines and functions, and can even generate entire code blocks based on natural language prompts or existing code context. While not an automation platform for business processes, Copilot is an automation tool for the software development lifecycle itself, significantly enhancing developer productivity. For organizations that build custom applications or develop intricate scripts to extend their automation platforms (like Make's custom app development), Copilot can accelerate this crucial development work.

   Unlike Make, which automates tasks between applications, Copilot automates the act of writing code within an Integrated Development Environment (IDE). This makes it an indirect but powerful alternative for teams that frequently develop custom connectors, API wrappers, or complex data transformation scripts to augment their iPaaS solutions. By streamlining the coding process, Copilot can reduce the time and effort required to build bespoke automation components, making advanced integrations more accessible and faster to implement. For development teams that are integral to an organization's automation strategy, Copilot represents a significant productivity enhancement, indirectly impacting the overall efficiency of automation initiatives.

   • Best for: Accelerating custom code development for integrations; improving developer productivity; learning new programming patterns; reducing boilerplate code.

   Read more about GitHub Copilot on modelroost. Official site: docs.github.com/en/copilot.

7. 7. PyTorch — Open-source machine learning framework for deep learning

   PyTorch is an open-source machine learning framework developed by Meta AI, widely used for deep learning research and production deployments. It is known for its flexibility, Pythonic interface, and dynamic computational graph, which makes it popular for rapid prototyping and complex model development. While PyTorch itself is a foundational tool for building AI models rather than an integration platform, it serves as an essential component for organizations that develop highly specialized, custom AI capabilities to embed within their automation workflows.

   The relationship between PyTorch and Make (or other iPaaS solutions) is one of enablement. An organization might use PyTorch to train a custom neural network for a specific task, such as advanced image recognition, predictive analytics, or highly nuanced natural language understanding. This custom model can then be deployed as an API endpoint, which an iPaaS like Make can call within a workflow. For instance, a Make scenario could trigger a PyTorch-powered model to analyze incoming documents, extract specific information, and then route the data based on the model's output. For teams with strong data science and machine learning capabilities, PyTorch provides the tools to build unique AI components that can differentiate their automation solutions, offering a deep level of customization beyond what off-the-shelf AI services typically provide.

   • Best for: Developing custom deep learning models for integration; ML research and prototyping; advanced AI-driven data processing; organizations with in-house ML expertise.

   Read more about PyTorch on modelroost. Official site: pytorch.org.

Side-by-side

How to pick

Selecting the right alternative to Make (formerly Integromat) depends heavily on your specific automation needs, technical capabilities, and organizational scale. Consider these factors:

1. For simple, broad integrations: Zapier. If your primary goal is to connect a wide array of popular SaaS applications and automate straightforward, event-driven tasks without deep technical expertise, Zapier is likely the most suitable choice. Its extensive app directory and user-friendly interface make it ideal for quick wins and broad adoption across non-technical teams.

2. For enterprise-grade, complex automation: Workato. When dealing with mission-critical business processes, requiring robust security, compliance, advanced error handling, and integration with on-premise systems, Workato stands out. It's built for large organizations that need centralized governance, IT-business collaboration, and capabilities beyond basic iPaaS to orchestrate complex operations across the entire enterprise.

3. For sophisticated low-code integrations: Tray.io. If you need to build intricate workflows that go beyond Zapier's simplicity but don't require Workato's full enterprise complexity, Tray.io offers a strong middle ground. It's excellent for teams that want low-code flexibility to build sophisticated integrations, particularly in sales, marketing, and support, with an emphasis on reusability and maintainability.

4. For adding advanced AI capabilities: OpenAI or Hugging Face. These are not direct iPaaS alternatives but rather powerful complements. If your automation strategy requires injecting advanced AI for tasks like natural language processing, content generation, or custom model inference, consider integrating OpenAI's API for cutting-edge proprietary models or Hugging Face for open-source flexibility and custom model deployment. You would typically use these in conjunction with an iPaaS.

5. For accelerating custom development: GitHub Copilot or PyTorch. If your organization frequently develops custom connectors, scripts, or unique AI models to extend your automation platform's capabilities, GitHub Copilot can significantly boost developer productivity in coding these components. For building highly specialized, custom deep learning models from the ground up to integrate into your workflows, PyTorch is the go-to framework for ML researchers and data scientists.

6. Consider your technical expertise:

   • No-code/Low-code: Zapier, Tray.io, Make (if comfortable with its visual complexity).
   • Developer-centric/API-first: OpenAI, Hugging Face (for integration), GitHub Copilot (for development).
   • Deep ML expertise: PyTorch (for custom model development).

7. Evaluate scalability and cost: Understand the pricing models, which often involve operations, tasks, or API calls. Project your usage to compare total cost of ownership across platforms. Enterprise solutions like Workato typically come with a higher price point but offer unparalleled features and support for large-scale deployments.