Automation & agents · 2026 comparison

Gumloop vs Make

Both are automation & agents tools. Here's how Gumloop and Make compare on pricing, fit, and use case — and where a one-bundle alternative replaces both.

Gumloop

Agentic AI

$97 to $497/month

AI workflow builder.

Best for: Data and growth engineers who want to wire LLMs into scraping, enrichment, and one-off data pipelines. Gumloop is better if you want the canvas, not a product.

Wysera vs Gumloop

Make

Automation

$0 to $35+/month

No-code automation platform.

Best for: Technical operators, growth engineers, and ops teams who need a flexible canvas for custom data flows, branching scenarios, and integrations Wysera doesn't touch. Make is the right call when the canvas is the value.

Wysera vs Make

At a glance

Gumloop
Make
Category
Agentic AI
Automation
Starting price
$97 to $497/month
$0 to $35+/month
Positioning
AI workflow builder
No-code automation platform

On entry price, Make starts lower — but weigh total cost at your team size, since per-seat and per-contact pricing can flip the answer as you grow.

How Gumloop and Make compare

Gumloop is a visual workflow builder for stitching LLM calls, web scraping, and data tools into automations. You design the flow. Wysera ships finished agents: 11 PostWyse agents for marketing, 22+ OpsWyse agents for revenue and operations, all with proper UIs, brand kit awareness, and confirm-before-execute UX on every action. You skip the canvas and run the product.

Make (formerly Integromat) is the visual automation platform with a deep scenario builder, branching, and operations-based pricing. It's beloved by technical operators who want more flexibility than Zapier. Wysera attacks the cost from the other side: fewer tools to glue because PostWyse and OpsWyse replace 4 to 6 marketing and revenue SaaS tools in the average SMB stack.

Which should you choose?

Pick Gumloop if you fit its sweet spot: Data and growth engineers who want to wire LLMs into scraping, enrichment, and one-off data pipelines. Gumloop is better if you want the canvas, not a product. Pick Make if you're closer to Technical operators, growth engineers, and ops teams who need a flexible canvas for custom data flows, branching scenarios, and integrations Wysera doesn't touch. Make is the right call when the canvas is the value. If your real problem is paying for too many overlapping tools, neither single choice solves it — that's the case for consolidation.

The third option

Or replace both with one $299/month bundle

If you're comparing Gumloop and Make to cut cost or tool sprawl, Wysera is the consolidation play: marketing (PostWyse), CRM and operations (OpsWyse), and an agent (Wyse) that drafts and executes across both — replacing several automation & agents and adjacent tools at once, with a confirm-before-execute step.

Frequently asked

Is Gumloop or Make better?

Neither is universally better — they fit different teams. Gumloop is best for Data and growth engineers who want to wire LLMs into scraping, enrichment, and one-off data pipelines. Gumloop is better if you want the canvas, not a product. Make is best for Technical operators, growth engineers, and ops teams who need a flexible canvas for custom data flows, branching scenarios, and integrations Wysera doesn't touch. Make is the right call when the canvas is the value. If you're consolidating a wider stack rather than picking one automation & agents tool, a bundle like Wysera replaces both plus the tools around them.

Gumloop vs Make: which is cheaper?

Make starts lower ($0 to $35+/month) than Gumloop ($97 to $497/month). Compare on total cost at your team size, not just entry price — per-seat and per-contact pricing can flip the answer as you grow.

What's a good alternative to both Gumloop and Make?

If you're weighing Gumloop against Make mainly to cut cost or tool sprawl, Wysera is the consolidation option: one $299/month bundle covering marketing (PostWyse), CRM and operations (OpsWyse), and an agent that works across both — replacing several automation & agents and adjacent tools at once.

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