🔁 Workflow Automation
Make vs Relevance AI: Which Should You Choose?
Both are workflow automation agents, but they are built for different people. Here is the honest split.
Make
by Make (Celonis)
A visual automation canvas with AI agents, priced per operation rather than per task.
Relevance AI
by Relevance AI
Build a workforce of specialised AI agents that work together, with no engineering required.
Side by side
Make vs Relevance AI at a glance
| 🧩 Make | 🤝 Relevance AI | |
|---|---|---|
| Editor score | 4.1★★★★★ | 4.1★★★★★ |
| Vendor | Make (Celonis) | Relevance AI |
| Autonomy | Semi-autonomous | Semi-autonomous |
| Deployment | Cloud | Cloud, API |
| Starting price | Free tier | Free tier |
| Pricing model | Subscription tiers priced in credits (formerly 'operations') | Subscription tiers with monthly credit allowances |
| Free tier | Yes — 1,000 credits per month | Yes — a small monthly credit allowance |
| Integrations | 12 native | 8 native |
| Best for | Ops teams wanting more power than Zapier | Go-to-market and RevOps teams |
Decision
Which one should you pick?
Choose Make if…
- Ops teams wanting more power than Zapier. The sweet spot. Branching and iteration without needing to run infrastructure.
- Teams with multi-step, high-volume workflows. Per-operation billing usually beats per-task pricing once workflows have real depth.
- Visual thinkers. The canvas makes complex logic legible in a way linear builders never manage.
Skip it if: Operation accounting is unintuitive is a dealbreaker for you.
Choose Relevance AI if…
- Go-to-market and RevOps teams. The core audience. Lead research, qualification and personalised outreach is where the product is deepest.
- Ops teams wanting agents without engineers. The visual builder is capable enough to cover most internal processes without writing code.
- Developers building custom agent products. Usable via API, but a code-first framework will give you more control for less money.
Skip it if: Credit-based pricing is hard to predict is a dealbreaker for you.
Trade-offs
Strengths and weaknesses
Make
- Best visual model for complex branching logic
- Per-operation pricing suits multi-step workflows
- Strong error handling
- Operation accounting is unintuitive
- Big scenarios get visually messy
- Agent layer less mature than the automation core
Relevance AI
- Best no-code route to genuine multi-agent workflows
- Excellent fit for sales and GTM specifically
- Approval gates make customer-facing use defensible
- Credit-based pricing is hard to predict
- Multi-agent debugging is opaque
- Fewer native integrations than general automation platforms
FAQ
Make vs Relevance AI FAQ
Is Make better than Relevance AI?
On our scoring Make edges it at 4.1/5 against 4.1/5, but the gap is smaller than the difference in who they suit. Make is the better choice for ops teams wanting more power than zapier; Relevance AI is stronger for go-to-market and revops teams.
Which is cheaper, Make or Relevance AI?
Make: Free tier — subscription tiers priced in credits (formerly 'operations'). Relevance AI: Free tier — subscription tiers with monthly credit allowances. Compare the pricing models rather than the headline numbers; consumption-based plans can overtake a higher flat fee quickly once usage is real.
Can I use Make and Relevance AI together?
Yes, and plenty of teams do. They share 8 integrations, so both can sit on the same data without duplicated plumbing. The usual pattern is to run each on the work it is strongest at rather than forcing one to cover everything.
What are the main differences between Make and Relevance AI?
Three things matter most. Autonomy: Make is semi-autonomous while Relevance AI is semi-autonomous. Deployment: Make runs cloud, Relevance AI runs cloud/api. Pricing model: subscription tiers priced in credits (formerly 'operations') versus subscription tiers with monthly credit allowances.