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Make vs Relevance AI
A plain-English comparison to help you choose between them.
Visual workflows or a managed agent workforce. Pick Make when the process is a defined flow you can draw: branching, loops, error handling and data transformation across hundreds of connected apps, priced by credits so sophisticated automation stays affordable at volume. Pick Relevance AI when the unit of work is a function rather than a flow: autonomous agents and multi-agent teams covering research, outreach and operations, configured without code, with bring-your-own-key economics keeping usage visible.
Side by side
- Summary
Make is visual automation with engineering sensibilities: scenarios built on a canvas where branching, loops, error handling and data transformation are first-class, connecting thousands of apps at operation-level pricing that undercuts the per-task platforms at volume.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at operation-level pricing
- Reusable AI agents inside scenarios, reasoning visible
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Per Make's Help Center, "effective august 27th, 2025, we're replacing operations with credits as our billing unit," with existing operations converting 1:1. Standard modules stay at 1 credit, but native AI modules consume credits variably: per one 2026 review, "A workflow with AI Agents can consume 43-50 credits per execution (Small model), versus the few credits of a classic workflow." Extra credits cost 25% more than in-plan (Help Center, updated 6 Nov 2025), for both manual and auto-purchase. The sticker price is not the bill; consider calling AI APIs directly via HTTP for cost control.
- Summary
Relevance AI is a no-code platform for building an AI workforce: autonomous agents assembled from tools, triggers and instructions, deployed against real work such as outreach, research and operations tasks.
- Best for
- Building autonomous agents without code
- Multi-agent teams coordinating on real work
- Bring-your-own-key model cost control
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- Agents act across connected business tools; treat credential grants as the security boundary they are.
Common questions
Make has AI agents in scenarios now, so when is Relevance AI still the answer?
When the agent is the product rather than a step. Make's agents live inside the scenario builder as reusable components with reasoning visible, which suits judgement moments within an otherwise defined flow. Relevance AI builds the workforce itself: agents assembled from tools, triggers and instructions, coordinating as teams on whole functions. A flow with smart steps is Make; staff you configure is Relevance AI.
What should you budget for before scaling either?
Make bills by credits, which undercut per-task rivals at volume but still compound on data-heavy scenarios iterating over large sets, so the arithmetic belongs in the plan. Relevance AI's consumption climbs with autonomy: always-on agents draw steadily, and bring-your-own-key transparency exists precisely so you can watch it. Meter a workflow by volume; model a fleet before it runs unattended.
What kind of person runs each well?
Make rewards a builder who thinks in flows: someone who owns automation as part of their job and wants the whole estate visible on a canvas. Relevance AI rewards a manager's habits applied to software: narrow scopes first, supervision before trust, autonomy earned by evidence. Neither is a set-and-forget purchase; they simply demand different disciplines.
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Tool facts last checked July 2026