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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 thousands 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.
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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 credit-based pricing that undercuts the per-task platforms at volume.
AI has become native: agents live inside the scenario builder as reusable, shareable components, with their reasoning and tool calls visible step by step, and a real-time visual map keeps a growing automation estate comprehensible.
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It rewards builders who think in flows: more capable than the simplest platforms, more approachable than code, with credit billing that still compounds at serious scale.
- Best for
- Branching, loops and error handling built visually
- High-volume automation at credit-based pricing
- Reusable AI agents inside scenarios, reasoning visible
- Data transformation between connected apps
- A visual map of the whole automation estate
- 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. Multi-agent teams coordinate on bigger jobs.
Its cost posture is unusually transparent: usage is metered in vendor credits passed through at wholesale with no markup, which keeps the economics visible while agents scale.
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As with every agent platform, always-on autonomy is earned: usage climbs with ambition, and supervision precedes trust.
- Best for
- Building autonomous agents without code
- Multi-agent teams coordinating on real work
- Wholesale vendor-credit cost control
- Usage transparency while agents scale
- Sales, research and ops agents in production
- 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.
Pricing
- Make
Free·from $9·from $16·from $29·Custom
Prices as of August 2026.
- Free
- Free
- Core
- from $9per monthbilled annually; $10.59 per month if billed monthly
- Pro
- from $16per monthbilled annually; $18.82 per month if billed monthly
- Teams
- from $29per monthbilled annually; $34.12 per month if billed monthly
- Enterprise
- Price on applicationno list price published
- Relevance AI
Free·$19·$234·Custom
Prices as of August 2026.
- Free
- Free
- Pro
- $19per monthbilled annually; $29 per month if billed monthly
- Team
- $234per monthbilled annually; $349 per month if billed monthly
- Enterprise
- Price on applicationno list price published
By area
Where each one pulls ahead, area by area.
| Area | Make | Relevance AI |
|---|---|---|
| By job | ||
| AI agents & automation | Make is for the builder who enjoys constructing the machine and has started noticing the per-task arithmetic | Relevance AI is built on the idea that a business process needs several agents cooperating rather than one long flow running through it |
| Founders & entrepreneurs | Make adds the AI step that classifies and drafts inside a flow the business already runs, rather than standing an agent up beside it | Relevance AI builds an outreach agent that researches before it writes and qualifies leads with judgement in the loop |
| By task | ||
| Automation & agents | Make rewards the person who enjoys building the machine, where the satisfaction and the skill are both in the construction | Relevance AI puts a no-code surface in front of real business processes and widens an agent's scope only as supervised results hold |
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 August 2026