Relevance AI vs Make: AI Agents or Visual Automation?
Contents
Relevance AI and Make both connect AI to business software, but they approach automation from opposite directions.
Relevance AI is designed around agents and multi-agent Workforces. Make is designed around visual scenarios, with AI Agents now available inside the Scenario Builder.
The short answer: choose Relevance AI when agents are the product you want to build; choose Make when you want a controlled visual automation and may add an agent to selected steps.
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Relevance AI vs Make at a Glance
| Area | Relevance AI | Make |
|---|---|---|
| Core experience | Build agents and Workforces | Build visual scenarios |
| Best for | Agent-led business processes | Multi-app workflow automation |
| AI decisions | Central to the platform | Available through AI Agent modules |
| Predictable routing | Supported through tools and workflow design | A core strength |
| Multi-agent structure | Native Workforces | Agents can be used within scenarios |
| Current AI-agent maturity | Established product focus | New version is in open beta |
Agent-First vs Scenario-First
In Relevance AI, you create an agent, assign instructions, add knowledge and tools, and decide how it is triggered. Several agents can work together in a Workforce.
In Make, you place modules on a visual canvas and connect them into a scenario. The new Make AI Agents app lets an agent use modules, scenarios, and MCP tools. It can reason about which tool to run while the surrounding scenario handles triggers, routing, and downstream steps.
Relevance AI makes the agent the main unit. Make makes the scenario the main unit.
Ease of Building
Relevance AI is approachable when the workflow can be described as a job: research this account, qualify this lead, update the CRM, or answer this support request.
Make is approachable when the workflow can be described as a sequence: when a form is submitted, look up the contact, transform the data, call an AI model, update the CRM, and send a notification.
Both become complicated when the underlying process is unclear. A visual canvas does not remove the need for good rules, sample data, error handling, and human approval.
AI Agent Capabilities
Relevance AI supports individual agents, Workforces, tools, knowledge, triggers, scheduling, escalations, and different interaction modes. Its marketplace provides agents that can be cloned and adapted.
Make's new AI Agents experience supports instructions, knowledge, tools, reasoning, modules, scenarios, and MCP tools in the Scenario Builder. Make labels the new version open beta, which means features and pricing may change.
Choose Relevance AI when multi-agent collaboration is a requirement now. Choose Make when an agent needs access to the scenarios and app connections your team already uses.
Integrations
Relevance AI's official pricing documentation currently lists more than 2,000 apps and over 1,000 triggers. It provides documented integrations for tools such as HubSpot, Apollo, Slack, and Google Sheets.
Make has a broad app ecosystem built around modules and scenarios. It is strong for connecting many SaaS products and mapping data visually between steps.
Integration count alone is not enough. Check whether the exact trigger and action you need are available, whether they require a higher plan, and whether an API fallback exists.
Pricing Models
Relevance AI charges through plan Actions plus Vendor Credits used for models and tools. Pro currently begins at $19 per month annually or $29 monthly.
Make uses credits for module operations, with AI features potentially adding token-based usage depending on the provider and connection. Its AI Agent usage can include a base operation charge plus AI token usage.
Because the units differ, calculate the cost of one complete workflow on both platforms. Read the Relevance AI pricing breakdown before estimating agent volume.
Relevance AI Is Better When
- you want digital workers rather than isolated AI steps
- several specialized agents need to collaborate
- sales, support, marketing, or operations teams will own the workflow
- marketplace agents provide a useful starting point
- the system should decide which tools to use based on context
Make Is Better When
- most steps follow a predictable route
- your team already has Make scenarios and connections
- visual data mapping and branching are important
- AI should enhance a larger conventional automation
- you want one canvas for triggers, modules, tools, and an agent
Verdict
Relevance AI is the better fit for building an AI workforce. Make is the better fit for building a visual automation system that can include AI agents.
For lead generation, Relevance AI can divide prospect search, research, qualification, CRM updates, and message drafting across agents. Make can map the same process as a scenario and use an agent only where flexible judgment is required.
The correct choice depends on which part of that process you want to control explicitly.
Read the Relevance AI review, compare other Relevance AI alternatives, or try Relevance AI.
Frequently Asked Questions About Relevance AI vs Make
Does Make have AI agents?
Yes. Make AI Agents can use instructions, knowledge, modules, scenarios, and MCP tools. The current new version is in open beta.
Is Relevance AI a workflow automation tool?
Relevance AI automates workflows, but it is positioned around agents and Workforces rather than conventional trigger-and-action scenarios.
Which is better for beginners?
Relevance AI may be easier when the user thinks in roles and tasks. Make may be easier when the user can map a clear sequence of app actions. Both require testing as workflows grow.
Can Make replace Relevance AI?
Make can cover many of the same business processes, especially when the workflow is predictable. Relevance AI has a clearer native model for several specialized agents collaborating as a Workforce.
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