Why This Comparison Exists
I'm a quality and brand compliance manager at a B2B SaaS company. That means I review the tools before they reach our revenue team—roughly 25 to 30 of them every year—and I've rejected my fair share. Over four years of evaluating sales tech, I've learned that the "best" tool depends entirely on what it's replacing and who's going to maintain it.
Lately, the question in our Slack has been Relevance AI vs Make. It keeps coming up. Teams hear about agent-native prospecting workflows, they hear about Make as the flexible automation alternative, and suddenly everyone's building spreadsheets comparing features they don't fully understand. I ran our official evaluation. Here's what actually separated them.
The comparison framework I used has three dimensions:
- Focus: general-purpose automation vs. AI-powered sales prospecting
- Data: visitor identification and B2B intent data
- Workflow: how sales engagement features fit into an agent-native prospecting stack
These are the dimensions that matter when you're choosing between a platform that will run your outbound motion and a generalist tool that could run anything.
Dimension 1: General Automation vs. Sales-Specific Focus
Make is a genuinely impressive general-purpose automation platform. You can connect thousands of apps and build nearly anything. Need a workflow that posts to Slack, creates a Notion page, and emails a customer? Make can do that in an afternoon. That flexibility is its brand—and the brand is accurate.
Relevance AI is the opposite kind of product. It's built specifically for sales prospecting: AI SDR and BD agents, cold email, LinkedIn automation, enrichment, intent data. It's opinionated. It assumes you're trying to identify accounts, enrich contact data, and run outreach, because that's what it's designed to do.
Here's the counter-intuitive part, and it's worth sitting with for a second. Make's flexibility was a liability in our test. We built a basic prospecting workflow with it—enrichment, email sequencing, reply tracking—and the assembly time was significant. Every connector was a potential breakage point. We spent more time debugging API rate limits and data mapping mismatches than we ever spent reviewing Relevance AI's native workflow.
One reviewer described it better than I could: "Make can do anything, which means you become the integrator." The quality of your prospecting with Make is entirely on you. With Relevance AI, the structure is embedded. That's not always better—it means you're committing to someone else's opinion about how prospecting should work. But if that opinion matches your GTM motion, it saves you weeks of plumbing.
The conclusion on dimension one is not subtle: for sales prospecting specifically, Relevance AI's focus beats Make's flexibility. The generalist tool wins at general things. The specialist wins at the specific job.
Dimension 2: Visitor Identification and B2B Intent Data
This is where the two products diverge most sharply. "Identify website visitors" isn't a Make feature. It's not even a category Make competes in. Make can route data between tools, but it doesn't collect or resolve intent signals by itself. You'd need to bolt on a separate visitor identification service, plus an enrichment provider, and then wire all the schemas together.
We actually did this. When I implemented our evaluation protocol in 2023, I insisted that we test tools the way a real customer would. For Make, that meant building the full stack. For Relevance AI, it meant turning on the platform. The difference was stark.
Seeing our evaluation side by side made me realize why dedicated intent platforms win for this use case. With Make, we had three tools to manage:
- A visitor identification service (setup: one to two weeks)
- An enrichment provider (contract negotiation plus API integration)
- Custom mapping between their data models and our CRM
Relevance AI shipped enrichment, intent data, and visitor identification as core platform capabilities. I won't pretend setup was instantaneous—we still had to connect our CRM, set up tracking, and configure which ICP signals mattered. But it was days, not weeks. The data quality was also noticeably cleaner. With Make, our reconciliation headaches came from mismatched company records between tools. With Relevance AI, the data layer and the workflow layer share the same foundation.
I should note a caveat here (which, honestly, applies to every intent data vendor we've tested): intent signals are only as good as your traffic volume. If you're a seed-stage startup pulling a few hundred visitors a month, no platform can resolve intent from a trickle. If you're generating real site traffic, this dimension is where Relevance AI pulls decisively ahead. It's a B2B intent data platform in a way Make isn't—and by design, Make doesn't pretend to be one.
Dimension 3: Sales Engagement Features in an Agent-Native Workflow
This is the question I see people searching for most: how do sales engagement platform features fit into an agent-native prospecting workflow? Let's define terms first, because "agent-native" gets thrown around loosely.
A traditional prospecting workflow looks like this: identify a list, enrich contacts, write emails, send, track replies, follow up. You're the orchestrator, and the sales engagement platform gives you sequencing, templates, and analytics. It's a tool that helps you do the work.
An agent-native workflow changes the relationship. You describe the outcome in natural language—"find fintech companies that are actively hiring for revenue leadership and showing intent around AI sales tools"—and an AI agent executes: researching accounts, enriching contacts, drafting outreach, and sending it. The sales engagement features (sequences, deliverability, reply tracking, cadence management) are embedded in the agent's execution loop rather than layered on top. Relevance AI was built for this model. Make was not.
Make's paradigm is mechanical assembly. You drag modules onto a canvas and tell them what to do. It's powerful—or rather, it can be powerful—but it treats prospecting as a series of steps, not a goal-driven process. The distinction sounds abstract until you watch a Make workflow send an awkward follow-up three days after a prospect already replied, because no module has any awareness of conversation context. We saw that exact failure in our test. (Which, honestly, said more about our wiring than Make's intent, but it's the reality of building a stack yourself.)
It took me the better part of 18 months and roughly 30 vendor demos to understand this fully: better automation tools don't fix bad prospecting logic—they just execute the bad logic faster. An agent-native approach at least forces a higher-level conversation about outcomes. When the system understands the goal ("find accounts that match our ICP"), it can make better micro-decisions than a static workflow script.
Relevance AI reviews I've read—and I read a lot of them for this evaluation—consistently circle back to the same theme: users describe what they want, and the agent figures out the execution. That's a fundamentally different review signature than what you see from Make users, who mostly describe how they assembled integrations. Both approaches can work. They represent different levels of abstraction, and the right one depends on how much engineering time you're willing to invest.
Pricing, Reviews, and the Catch
No honest comparison skips pricing, so let's address it. Make's entry-level plans are affordable—single-digit to low double-digit monthly costs, depending on operations. Relevance AI is usage-based and will almost certainly be more expensive out of the gate.
But price-per-seat comparisons miss the total cost picture. On Make, you're paying for the automation layer, then adding a visitor identification tool, enrichment provider, and email infrastructure on top. The engineering time to wire and maintain those tools is a real cost—often the biggest one—and it doesn't show up on any invoice. When I totaled everything for our evaluation, the "cheap" Make stack wasn't dramatically cheaper than Relevance AI's usage-based pricing. And it required ongoing maintenance. Roughly one in four tools we've tested over the years failed on this total-cost basis, and Make's prospecting stack came uncomfortably close to that line.
If you're searching for "reviews voor Relevance AI" in the Dutch-speaking market, or looking at English-language review communities, you'll see a consistent pattern. Strong praise around the agent workflows and intent data, frustration around the learning curve and occasional predictability of AI agents. That matches our experience, and honestly, I'd rather read those honest notes than a vendor page with zero caveats.
One thing my quality background makes me picky about: review freshness. The AI sales tooling market changes fast. A review from six months ago may describe features that have shifted significantly. This assessment was accurate as of early 2026, and I'd recommend verifying current pricing and capabilities before making your call.
Which One Should You Pick?
Here's my scenario-based verdict, because "just pick one" is bad decision-making for a tool that'll sit in your critical path.
Make makes sense if: you already run a working stack of specialist sales tools, you have engineering support to maintain integrations, and you want a flexible automation layer for internal processes. Make is a great general-purpose tool. It is not a bad product; it's just not a specialist. And if you don't want a platform imposing workflow opinions on your sales team, Make's neutrality is an advantage.
Relevance AI makes sense if: you want outbound prospecting to run like a managed operation rather than a home-built assembly line. If identifying website visitors, using B2B intent data, and running AI SDR agents inside an agent-native workflow are your priorities, Relevance AI's focus translates to less setup, cleaner data, and fewer things to babysit.
The vendor who is honest about their boundaries earns trust. Make's boundaries are clear: it automates, but you bring the strategy and the domain logic. A specialist like Relevance AI is more reliable for prospecting in my experience, because it doesn't offload the quality burden to you.
A tool that claims to do everything is usually a tool that does nothing exceptionally well. Pick the one that's honest about its strengths—and the one that matches the problem you're actually solving.
