Make vs Relevance AI
Both tools are listed under No-Code Marketing Workflow Automation, and both have been fact-checked by a named editor. This page lines up their structured data and quotes each review’s limitations and pricing notes; it does not pick a winner.
Make
Visual workflow orchestration platform connecting APIs, databases, and AI agents through a node-based integration engine.
Pricing: Free plan · $0/mo · Enterprise plan · Autonomy: L3 · Conditional automation · Moat: 3.6/5.0 (Proprietary moat)
Target fit: B2B SaaS, DTC / eCommerce (Web SaaS)
Relevance AI
No-code enterprise orchestration platform enabling teams to build, deploy, and monitor collaborative multi-agent autonomous workforces across operational.
Pricing: Custom / enterprise pricing · Autonomy: L3 · Conditional automation · Moat: 4.2/5.0 (Proprietary moat)
Target fit: B2B SaaS, DTC / eCommerce, Enterprise Brands (Web SaaS)
How Make and Relevance AI differ
- Pricing model: Make is freemium, Relevance AI is enterprise / quote.
- Make has a free plan; none is recorded for Relevance AI.
- Make was founded earlier (2016) than Relevance AI (2020).
- Headquarters: Make in Prague, Praha 8, Relevance AI in San Francisco, California.
Computed from the fields below. Only editor-verified ratings and vendor-sourced prices are compared.
Side-by-side facts
| Field | Make | Relevance AI |
|---|---|---|
| Subtrack | No-Code Marketing Workflow Automation | No-Code Marketing Workflow Automation |
| Pricing | Free plan · $0/mo · Enterprise plan | Custom / enterprise pricing |
| Pricing model | Freemium | Enterprise / quote |
| Free access | Free plan | No free plan disclosed |
| Enterprise plan | Yes | Yes |
| AI autonomy level | L3 · Conditional automation | L3 · Conditional automation |
| Moat tier | Proprietary moat | Proprietary moat |
| Delivery | Web SaaS | Web SaaS |
| Audience | B2B SaaS, DTC / eCommerce | B2B SaaS, DTC / eCommerce, Enterprise Brands |
| Headquarters | Prague, Praha 8 | San Francisco, California |
| Fact-checked | Julie Brot, Oct 14, 2026 | Jagmeet Sine, Oct 12, 2026 |
Make vs Relevance AI: TCO & constraint stress test
Test how both tools behave under your team size, monthly budget ceiling, CRM ecosystem, and governance guardrails.
Make
Conditional Fit · Contract & Stack Caveats- Opaque Enterprise Quoting vs. SMB/Mid-Market Budget: Make does not disclose a public self-serve floor (Free plan · $0/mo · Enterprise plan). Buyers with a $800/mo cap face high sales-qualification friction.
- Published Self-Serve Entry Floor: Make publishes a transparent entry point (Free plan · $0/mo · Enterprise plan) with a free tier for initial validation before committing.
- Tier Fit for 5-Seat Scale: Entry / Starter ($12/mo): Verified $12/mo published rate on vendor pricing schedule.
- Primary Operational Bottleneck: High-Throughput Operational Throttling: Complex multi-branch scenarios experience execution queue latency and downstream API rate-limiting when handling high-frequency webhook bursts, requiring manual batching logic.
Walk-Away Rule: Walk away if your workflow requires uncapped enterprise volume on the Free plan · $0/mo · Enterprise plan entry plan without upgrading tiers.
Negotiation Lever: Validate exact quota thresholds (seats, credits, or API calls) included in Make's Free plan · $0/mo · Enterprise plan tier before converting from monthly to annual billing.
Relevance AI
High Deal-Risk · Constraint Mismatch- Opaque Enterprise Quoting vs. SMB/Mid-Market Budget: Relevance AI does not disclose a public self-serve floor (Custom / enterprise pricing). Buyers with a $800/mo cap face high sales-qualification friction.
- Sales-Gated Contract Terms: Commercial procurement operates through bespoke contractual tiers rather than self-service usage bands. Enterprise licensing requires direct sales engagement to tailor quotes based on organizational scale, agent concurrency requirements, and dedicated workspace provisioning.
- Primary Operational Bottleneck: Multi-Agent Cascading Latency: Chained reasoning cycles across multiple cooperating agents introduce cumulative API round-trip overhead, making real-time interactive user-facing workflows impractical compared to deterministic scripts.
Walk-Away Rule: Walk away if you require transparent self-serve monthly billing or your budget ceiling is under $1,000/mo (Relevance AI requires sales-gated custom quoting).
Negotiation Lever: Demand a 30- to 60-day paid pilot opt-out clause in the Relevance AI order form before signing a 12-month enterprise commitment.
Limitations
Make
Operational considerations and platform boundaries include: High-Throughput Operational Throttling: Complex multi-branch scenarios experience execution queue latency and downstream API rate-limiting when handling high-frequency webhook bursts, requiring manual batching logic.Full limitations in the Make review
Relevance AI
Operational considerations and platform boundaries include: Multi-Agent Cascading Latency: Chained reasoning cycles across multiple cooperating agents introduce cumulative API round-trip overhead, making real-time interactive user-facing workflows impractical compared to deterministic scripts.Full limitations in the Relevance AI review
Pricing notes
Make: Free plan · $0/mo · Enterprise plan
Commercial procurement follows a tiered usage framework anchored by operations consumed across scenario runs. Evaluation commences on an entry-level tier at $0 per month, which supplies foundational execution credits and connector access for exploratory workflows.Pricing details in the Make review
Relevance AI: Custom / enterprise pricing
Commercial procurement operates through bespoke contractual tiers rather than self-service usage bands. Enterprise licensing requires direct sales engagement to tailor quotes based on organizational scale, agent concurrency requirements, and dedicated workspace provisioning.Pricing details in the Relevance AI review
Frequently asked questions: Make vs Relevance AI
How does Make pricing compare to Relevance AI in 2026?
Make is listed at Free plan · $0/mo · Enterprise plan (freemium, free plan), whereas Relevance AI is listed at Custom / enterprise pricing (enterprise / quote, no free plan disclosed).
How do Make and Relevance AI compare on AI autonomy and moat depth?
On the MarTechRadar L1–L5 scale, Make is rated L3 · Conditional automation with a Proprietary moat moat (3.6/5.0), while Relevance AI is rated L3 · Conditional automation with a Proprietary moat moat (4.2/5.0).
Which teams are Make and Relevance AI built for?
Make is a Web SaaS product in No-Code Marketing Workflow Automation designed for B2B SaaS, DTC / eCommerce. Relevance AI is a Web SaaS product in No-Code Marketing Workflow Automation designed for B2B SaaS, DTC / eCommerce, Enterprise Brands.
What are the main limitations of Make vs Relevance AI?
For Make: Operational considerations and platform boundaries include: High-Throughput Operational Throttling: Complex multi-branch scenarios experience execution queue latency and downstream API rate-limiting when handling high-frequency webhook bursts, requiring manual batching logic. For Relevance AI: Operational considerations and platform boundaries include: Multi-Agent Cascading Latency: Chained reasoning cycles across multiple cooperating agents introduce cumulative API round-trip overhead, making real-time interactive user-facing workflows impractical compared to deterministic scripts.
Excerpts are quoted verbatim from each fact-checked review. Last updated Oct 14, 2026.