This guide compares the top 15 automation and AI agent platforms to help you move beyond basic logic workflows. It evaluates solutions based on ease of use, reliability, and how well they handle complex AI reasoning versus simple data transfer. You will find the right tool for your specific use case by comparing practical criteria like debugging capabilities, team collaboration, and building speed.

Top 6 Shortlist of Make Alternatives

1. Vellum - Best for building reliable AI agents by simply describing what you want done.
2. Retool AI - Best for building internal tools and dashboards that incorporate AI logic.
3. Relevance AI - Best for B2B teams building autonomous AI workforces without code.
4. Stack AI - Best for visually connecting LLMs to databases for specific enterprise workflows.
5. Flowise - Best for developers who want a self-hosted, open-source visual AI builder.
6. Zapier - Best for simple, linear automations connecting thousands of apps without code.

I kept seeing Team and Ops leaders struggle to make the leap from basic automations to actual AI agents that can reason. When I looked for resources, most guides just listed generic automation tools without addressing the specific needs of building reliable AI workflows. They rarely touched on the difficulty of fixing an LLM that hallucinates or the challenge of collaborating on a prompt chain with a team.

I talk to teams daily who hit a wall with traditional builders—their prototypes work once but break in production because they lack proper testing tools. I wrote this guide to show you what is actually required to build agents that work at scale. I want to help you move beyond just connecting APIs to engineering reliable intelligence that drives real business value.

What is an AI Agent?

An AI agent is a software program that uses a Large Language Model (LLM) as its reasoning engine to perceive its environment, make decisions, and execute tasks to achieve a goal. Unlike traditional automation that follows a rigid script, an AI agent can handle ambiguity, determine the best path forward dynamically, and correct its own errors.

What are Make alternatives?

Make alternatives are software platforms that allow users to build automated workflows, integrate applications, and deploy AI agents without extensive coding. These tools range from general automation builders to dedicated AI development environments designed for higher complexity, improved reliability, and advanced reasoning capabilities.

Why use Make alternatives?

While Make is powerful for logic-based workflows, dedicated alternatives often offer better environments for handling the probabilistic nature of AI.

• Enhanced Reliability: Built-in testing ensures consistent outputs and prevents hallucinations.
• Faster Iteration: Integrated playgrounds allow immediate testing of prompts and logic without deploying.
• Natural Language Building: Build workflows using plain English rather than complex JSON mapping.
• Better Debugging: Trace exactly where the LLM reasoning failed, not just where the API connection dropped.
• Team Collaboration: Version control allows multiple builders to work safely on the same agent.
• Scalability: Handle high-volume requests without hitting rigid rate limits or unexpected cost spikes.
• Seamless Deployment: One-click deployment to production endpoints or user-facing apps.
• Advanced Context: Connect vector databases and long-term memory more intuitively than standard webhooks.
• Cost Efficiency: 46% of businesses report cost savings as a primary driver for AI adoption [1].

Who needs Make alternatives?

• Team Leaders: Who need a single place to standardize and scale automations across their team, with clear ownership, collaboration, and guardrails.
• Operations Leaders: Who need to automate complex processes that require decision-making, not just data transfer.
• Product Managers: Who need to prototype AI features quickly without waiting for engineering resources.
• Customer Support Heads: Who want to build intelligent chatbots that resolve issues rather than just routing tickets.
• Growth Marketers: Who need to personalize outreach at scale using dynamic content generation.
• Developers: Who want to offload the "plumbing" of integrations to focus on core product logic.

What Makes an Ideal Make Alternative?

• Intuitive Interface: The platform should allow non-engineers to visualize and build workflows without coding.
• Robust Evaluation: It must include tools to quantitatively test AI outputs against expected results.
• Native Integrations: It should connect easily to your existing tech stack (CRM, database, Slack).
• Low Latency: Agents should respond quickly enough for real-time user interaction.
• Transparent Pricing: Costs should be predictable as you scale usage and complexity.

Key Trends Shaping Make Alternatives

• Shift to Agentic AI: By 2028, at least 15% of day-to-day work decisions will be made autonomously by agentic AI, up from 0% in 2024 [2].
• Democratization of Development: 80% of technology products will be built by non-technology professionals by 2024 [3].
• Focus on Safety: As adoption grows, 55% of organizations are implementing "human in the loop" protocols to manage AI risk [4].
• Rise of Composite AI: Organizations are increasingly combining different AI techniques (like knowledge graphs and LLMs) to improve accuracy [5].

How to Evaluate Make Alternatives

To find the right tool, you must look beyond feature lists and evaluate the builder experience.

How we chose the best Make alternatives

We evaluated these platforms based on five core criteria: reliability, ease of use for non-engineers, integration depth, collaboration features, and debugging capabilities. We prioritized tools that allow for rigorous testing of AI outputs, as this is the biggest failure point in production. We also considered the "time-to-hello-world"—how fast a new user can get a basic agent running.

Expected Trade-offs

• Power vs. Simplicity: Tools that are easiest to start with often lack the depth for complex logic.
• Cost vs. Control: Fully managed platforms cost more but reduce infrastructure headaches compared to self-hosted options.
• General vs. Specialized: General automation platforms connect to more apps, but specialized AI builders handle LLM context and hallucinations better.

Now let's dive into the specific tools. Each platform has unique strengths depending on your team's technical skills, budget, and automation complexity.

Our review process

We evaluated 30 platforms and scored them against common buyer needs for automation and AI app building. Here’s the simple framework we used to keep rankings structured and fair, weights add up to 100%.

We scored every platform based on the following criteria:

• Core Automation Capabilities (25%)
• AI Readiness (20%)
• Ecosystem & Extensibility (15%)
• Reliability & Performance (10%)
• Deployment & Governance (10%)
• Usability (10%)
• Customer Support & Resources (10%)

The tools in the Top 15 have made it on the list based on our review criteria.

15 Best Make Alternatives: Reviewed & Compared

1. Vellum AI

Vellum AI is an AI agent builder designed to help teams automate work easily and quickly. Anyone can create AI agents by simply describing the task they want done. No code, no workflow wiring, no AI expertise required. Vellum handles the underlying complexity of building and integrating agents into systems, so teams can go from idea to working agent that meaningfully automates work in minutes.

Best For: Teams who want to automate work by simply describing what they need done

Pros:

• Prompt-based agent creation that anyone can use
• Visual builder + TypeScript/Python SDK (custom nodes, exportable code)
• Shareable AI Apps for cross-org reuse and rapid rollout
• Shared canvas for seamless cross-functional collaboration
• Flexible deploys (cloud, VPC, on-prem)
• Strong docs, templates, and responsive support

Cons:

• Some advanced SDK features still require engineering support
• As a rapidly evolving platform, new features may require occasional relearning for teams

Score: 100

Pricing: Free tier; paid plans starting at $25/month; enterprise plans available

2. Retool AI

Retool AI combines a visual interface builder with AI workflows. It allows teams to build internal tools that connect to databases and APIs, then layer AI logic on top to process that data. It is more interface-focused than Make.

Best For: Engineering teams building internal dashboards with AI capabilities

Pros:

• Excellent drag-and-drop UI builder for front-end interfaces
• Pre-built blocks for connecting to Postgres, REST APIs, and GraphQL
• Integrated vector database for RAG (Retrieval-Augmented Generation) workflows
• Securely handles sensitive business data within your VPC

Cons:

• Requires knowledge of SQL and JavaScript for complex logic
• Can become expensive as you scale user seats

Score: 99

Pricing: Free tier available; paid plans starting at $10/mo; enterprise plans available

3. Relevance AI

Relevance AI is a no-code platform focused specifically on building multi-agent workforces. Unlike Make's linear automation, Relevance allows users to create "agents" that can loop, reason, and handle complex tasks autonomously.

Best For: B2B teams building autonomous AI workforces without code

Pros:

• Visual builder designed specifically for agent chains and loops
• "B2B Tools" feature allows agents to browse the web and scrape data
• Easy to train agents on specific knowledge bases (PDFs, websites)
• Templates available for sales outreach and research tasks

Cons:

• Steeper learning curve for understanding agent behavior vs. linear workflows
• Debugging complex agent loops can be difficult

Score: 98

Pricing: Free tier; paid plans start at $29/month; enterprise plans available

4. Stack AI

Stack AI is a visual interface for building Large Language Model (LLM) applications. It focuses heavily on the "backend" logic of AI, allowing users to connect different models (OpenAI, Anthropic) to data sources and prompts in a node-based view.

Best For: Rapidly prototyping LLM logic and backend workflows

Pros:

• Intuitive node-based canvas similar to Make but optimized for AI
• Instant swapping between different LLM providers to test performance
• One-click API deployment for integrating into other apps
• Good handling of document parsing and vector search

Cons:

• Interface can get cluttered with complex, multi-step workflows
• Pricing scales quickly with high usage volume

Score: 97

Pricing: Free tier; enterprise plans available

5. Flowise

Flowise is an open-source drag-and-drop tool built on top of LangChain. It is a great option for technical teams who want a visual interface but prefer to self-host their infrastructure rather than pay SaaS fees.

Best For: Developers and technical teams who prefer open-source and self-hosting

Pros:

• Completely free to use if you self-host
• Visualizes complex LangChain concepts (chains, agents, memory)
• Huge library of community-contributed nodes and integrations
• Full control over your data and infrastructure

Cons:

• Requires technical setup (Docker/Node.js) to install and maintain
• UI is less polished than paid SaaS competitors

Score: 96

Pricing: Free tier; paid plans start at $35/month; enterprise plans available

6. Zapier

Zapier is the most well-known alternative to Make. It prioritizes ease of use and a massive library of integrations over complex logic. It is ideal for simple "if this, then that" automations but lacks the granular control of Make or Vellum.

Best For: Simple, linear integrations between popular SaaS apps

Pros:

• Massive library of 6,000+ app integrations
• Very easy to use; no logic mapping required for basic tasks
• "Zaps" are reliable and rarely break once set up
• "Tables" feature allows for basic database functionality

Cons:

• Significantly more expensive than Make for high-volume tasks
• Limited ability to handle complex branching or data transformation

Score: 95

Pricing: Free tier; paid plans starting at $19.99/mo; enterprise pricing available

7. n8n

n8n is a "fair-code" workflow automation tool that offers a node-based visual editor similar to Make. It is highly popular among developers because it allows for custom JavaScript execution and can be self-hosted for data privacy.

Best For: Technical teams who want complex logic and self-hosting options

Pros:

• Can be self-hosted on your own servers (great for GDPR/privacy)
• Powerful data transformation capabilities using JavaScript
• Visual workflow editor is flexible and handles complex branching well
• Active community and template library

Cons:

• Requires technical knowledge to set up and maintain (if self-hosting)
• Less intuitive for non-technical business users than Vellum or Zapier

Score: 94

Pricing: Paid plans starting at $24/mo; enterprise plans available

8. Gumloop

Gumloop is a newer automation platform that combines traditional workflow automation with AI agents. It is particularly strong at web automation and scraping tasks, allowing users to categorize and extract data from the web easily.

Best For: Automating web scraping and data categorization workflows

Pros:

• "Chrome Extension" integration allows for easy recording of web tasks
• Visual canvas is clean and modern
• Strong focus on AI-driven data extraction and formatting
• Good templates for marketing and research use cases

Cons:

• Fewer native integrations than Zapier or Make
• Still a younger platform, so some advanced features are in development

Score: 93

Pricing: Free tier; paid plans start at $37/month; enterprise plans available

9. Lindy

Lindy positions itself as an "AI Employee" platform. Instead of building workflows, you hire a "Lindy" to handle a specific job function (like an Executive Assistant or Recruiter). It uses a chat-based interface to set up tasks.

Best For: Individuals wanting an out-of-the-box "AI assistant" experience

Pros:

• Very fast setup; pre-configured "personas" are ready to use
• Handles triggers like emails and calendar events naturally
• Chat interface makes it feel like delegating to a human
• Can learn from feedback over time

Cons:

• Less control over the specific logic steps than a workflow builder
• Can be difficult to debug if the "employee" misunderstands a task

Score: 92

Pricing: Free tier; paid plans start at $39/month; enterprise plans available

10. Microsoft Copilot Studio

Microsoft Copilot Studio is a low-code tool for building AI agents. It is deeply integrated into the Microsoft 365 ecosystem, making it a strong choice for organizations already using Teams, SharePoint, and Dynamics.

Best For: Enterprises heavily invested in the Microsoft ecosystem

Pros:

• Native integration with Microsoft 365 data (Graph API)
• Familiar interface for Power Platform users
• Deploys directly to Microsoft Teams or internal SharePoint sites
• Strong security and compliance features

Cons:

• Expensive licensing structure
• Difficult to use with non-Microsoft data sources compared to agnostic tools

Score: 91

Pricing: From $30/user/mo; pay-as-you-go pricing available

11. MindStudio

MindStudio is a visual builder for creating AI applications that can be deployed as standalone web apps. It focuses on the "application" layer, allowing you to build tools that your team or customers interact with directly.

Best For: Non-technical creators building AI-powered web apps

Pros:

• Agnostic to LLM providers (switch between GPT-4, Claude, Llama)
• Built-in monetization tools for selling your AI apps
• Easy to upload custom data sources (PDFs, CSVs) for context
• Deploys as a clean, user-friendly web interface

Cons:

• More focused on "apps" than background automation workflows
• Limited ability to trigger actions in external software

Score: 90

Pricing: Free tier; paid plans start at $20/month; enterprise plans available

12. Tray.ai

Tray.ai is an enterprise automation platform. It is significantly more powerful and complex than Make, designed for IT and engineering teams at large companies to build sophisticated integrations and data pipelines.

Best For: Large enterprises needing secure, scalable API integrations

Pros:

• Extremely scalable and secure (SOC 2, HIPAA compliant)
• "Merlin AI" helps build workflows using natural language
• Universal connector allows integration with any REST API
• Detailed error logging and version control

Cons:

• High price point; not suitable for SMBs or individuals
• Steep learning curve for non-engineers

Score: 89

Pricing: Enterprise plans only

13. Dust

Dust is an AI platform focused on breaking down data silos. It connects to your company's internal data (Notion, Slack, Google Drive, GitHub) and creates custom AI assistants that can answer questions based on that collective knowledge.

Best For: Teams wanting a unified search/chat interface for internal knowledge

Pros:

• Excellent connectors for Notion, Slack, and Google Drive
• "Programmable" assistants allow for custom instructions
• Collaborative workspace where teams can share assistants
• Strong focus on data privacy and permissions

Cons:

• Not a general-purpose workflow automation tool (doesn't "do" tasks as well as it "answers" them)
• Limited output options beyond chat

Score: 88

Pricing: 14 day free trial; paid plans start at $29/month; enterprise plans available

14. Salesforce Agentforce

Agentforce is Salesforce's platform for building autonomous AI agents within the CRM. It allows agents to take action on customer data, such as resolving support tickets or qualifying sales leads, without human intervention.

Best For: Sales and Support teams living inside Salesforce

Pros:

• Direct access to all Salesforce CRM data without integration headaches
• "Atlas Reasoning Engine" helps agents plan and execute complex tasks
• High security standards for handling customer data
• Seamless handoff to human agents when necessary

Cons:

• Extremely expensive consumption-based pricing
• Only useful if your organization uses Salesforce

Score: 87

Pricing: Free trial; Starting at $500 per 100K credits

15. CrewAI

CrewAI is a framework for orchestrating role-playing AI agents. While it started as a code-only library, it now offers enterprise features. It excels at breaking down a complex objective into sub-tasks assigned to specific "agents" (e.g., a Researcher, a Writer, and an Editor).

Best For: Developers building complex multi-agent systems

Pros:

• Structured approach to multi-agent collaboration
• Agents can be assigned specific roles, goals, and backstories
• Works well with local LLMs via Ollama
• Highly flexible for complex, multi-step reasoning tasks

Cons:

• Primarily code-first (Python), though UI is improving
• Can be overkill for simple linear automations

Score: 86

Pricing: Free tier; enterprise plans available

15 best Make alternatives comparison table

After reviewing all these options, one platform consistently stands out for teams who want to move fast without sacrificing reliability.

Why Vellum

This section breaks down why Vellum is the top choice for teams looking to move beyond standard automation tools like Make.

Why Vellum stands out

Vellum AI is an AI agent builder designed to help teams automate work easily and quickly. Anyone can create AI agents by simply describing the task they want done. No code, no workflow wiring, no AI expertise required. Vellum handles the underlying complexity of building and integrating agents into systems, so teams can go from idea to working agent that meaningfully automates work in minutes.

While tools like Make require you to manually map data between hundreds of modules, Vellum allows you to focus on the outcome. You describe the goal, and the platform handles the logic. This shift from "wiring" to "describing" makes it the fastest path to production for non-technical teams.

When Vellum is the best fit

Vellum is the ideal solution if you find yourself in these specific scenarios:

• You want to automate work but don't code: You understand your business process perfectly but get stuck trying to map JSON arrays or API endpoints in Make.
• You need to build fast: You need a working solution in hours, not weeks of testing and debugging complex node graphs.
• You need reliability, not just a demo: You need an agent that handles edge cases gracefully without breaking your entire workflow every time an input changes slightly.
• You need to collaborate: Multiple team members need to work on, test, and improve the same agent without overwriting each other's work.
• You want to connect to existing tools: You need your agent to read from your CRM, post to Slack, or draft emails without setting up complex custom webhooks.
• You need to share your tool: You want to deploy your agent as a simple form or app for your team to use, rather than just a backend API.

How Vellum compares

• Vellum vs. Make: Vellum uses natural language to build logic, whereas Make requires complex visual wiring and logic mapping.
• Vellum vs. Zapier: Vellum offers deep testing and reliability tools for AI responses, while Zapier is better suited for simple, linear "if this, then that" tasks.
• Vellum vs. n8n: Vellum is accessible to anyone on the team, whereas n8n requires technical knowledge and self-hosting management.
• Vellum vs. Custom Code: Vellum provides a pre-built infrastructure that lets you ship in minutes, eliminating the maintenance burden of custom Python scripts.

What you can ship in the first 30 days

Proof you can show stakeholders

• 90% faster build time: Teams move from idea to working prototype in minutes using natural language, compared to days of configuring modules in Make.
• Zero engineering dependency: Operations and product teams can build and maintain their own agents without waiting for developer resources.
• Measurable reliability: Unlike standard automation tools, Vellum provides visibility into exactly how and why an agent made a decision, reducing error rates.
• Immediate ROI: Low monthly cost (starting at $25/mo) combined with high time savings on repetitive operational tasks delivers payback in the first month.

Ready to replace Make with Vellum?

Stop wrestling with complex node graphs and error logs. Start building agents by simply describing what you want to get done.

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FAQs

1. What is the fastest way to build an AI agent without coding?

Vellum is currently the fastest option for non-coders. Its "describe-and-go" interface allows you to build fully functional agents by typing natural language instructions, removing the need to drag-and-drop complex logic nodes.

2. How do I know if my AI agent is working correctly?

Vellum includes built-in testing and evaluation tools. You can run your agent against test cases to verify it doesn't hallucinate or break before you deploy it to your team.

3. Can I connect my existing tools to an AI agent?

Yes. Vellum supports integrations with common business tools. You can connect your agents to your data sources and communication platforms without needing to write custom API code.

4. What is the difference between Make and Vellum?

Make is a general automation tool that requires manual wiring of logic steps. Vellum is an AI-first agent builder where the logic is handled by the AI based on your natural language descriptions, making it much easier for complex reasoning tasks.

5. How much does it cost to build AI agents?

Vellum offers a free tier to get started, with paid plans starting at $25/month. This is often more predictable than usage-based pricing models that spike unexpectedly.

6. Do I need technical skills to use an agent builder?

With Vellum, you do not. The platform is designed specifically to abstract away the technical complexity, allowing business users to build powerful tools using plain English.

7. How do I prevent my AI agent from making mistakes?

Vellum allows you to set up "guardrails" and test your agent against specific scenarios. This ensures the agent adheres to your guidelines and doesn't produce unwanted outputs.

8. Can multiple team members collaborate on agents?

Yes. Vellum provides a shared workspace where teams can collaborate, view version history, and manage edits, solving the "single-player" problem found in many other automation tools.

9. What happens if my agent builder vendor shuts down?

Vellum is a venture-backed, stable platform designed for long-term use. Unlike smaller, experimental tools, it provides the infrastructure stability required for business-critical workflows.

10. How long does it take to deploy a production agent?

With Vellum, you can deploy a production-ready agent in minutes. Once you are happy with the performance in the playground, deployment is a single click.

11. Which agent builder is best for operational teams?

Vellum is the best fit for operational teams because it combines the power of AI with an interface that doesn't require engineering support, allowing ops leaders to solve their own bottlenecks immediately.

Extra Resources

• Best n8n alternatives of 2026→‍
• Best Zapier alternatives of 2026→‍
• Gumloop vs n8n vs Vellum →‍
• 2026 Guide to Top 20 AI Agent Builder Platforms →‍
• Beginner’s Guide to Building AI Agents →

Citations

[1] IBM. (2024). Global AI Adoption Index 2024.

[2] Gartner. (2024). Top Strategic Technology Trends for 2024.

[3] Gartner. (2023). Gartner Says 80% of Technology Products and Services Will Be Built by Those Who Are Not Technology Professionals.

[4] Deloitte. (2024). State of AI in the Enterprise, 6th Edition.

[5] Gartner. (2024). Hype Cycle for Artificial Intelligence, 2024.