Make vs Relevance AI

Make (formerly Integromat) is a visual automation builder where you connect apps and services into "scenarios" using a drag-and-drop canvas, with branching logic, error handling, and data transformation tools that go beyond simple trigger-action automations. Built-in modules for OpenAI, Anthropic's Claude, Google Gemini, and other AI providers make it straightforward to add an AI step into a workflow — for example, summarizing incoming form submissions before routing them to a CRM. The free tier includes a meaningful number of monthly operations, generous enough for small personal automations; paid plans starting around $9/mo raise operation limits and unlock more frequent scenario execution. The visual builder is more powerful than simpler tools but has a steeper learning curve, and debugging complex multi-branch scenarios can take some practice.

Relevance AI frames automation around an "AI workforce": instead of a single agent, you assemble a team of agents, each with a defined role (e.g., researcher, outreach writer, support responder), tools they're allowed to use, and goals they work toward — with a manager-style orchestration layer coordinating handoffs between them. A library of pre-built agent templates covers common business functions like outbound sales prospecting, customer support triage, and market research, which can be customized to your stack and connected to CRMs and other business tools. The free tier supports a limited number of agent runs for testing; paid plans starting around $19/mo raise usage limits and unlock more agents, seats, and integrations. Getting real value requires some upfront investment in defining roles and tools clearly — vaguely-configured multi-agent teams tend to produce inconsistent results, and overall output quality is still bounded by the underlying LLM each agent uses.

• + Powerful visual workflow builder
• + Deep customization with branching logic
• + Built-in AI modules (OpenAI, Claude, Gemini)
• + Generous free operation allowance

• – Steeper learning curve than simpler tools
• – Complex scenarios can be hard to debug
• – Operations-based pricing scales with usage

• + Build multi-agent teams with defined roles and tools
• + Library of pre-built templates for sales/support/research
• + Integrates with CRMs and common business tools
• + Usable free tier for testing

• – Effective multi-agent workflows take time to configure
• – More advanced features and seats behind paid tiers
• – Output quality depends on underlying LLM and setup