Activepieces vs Make
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Activepieces | Make |
|---|---|---|
| Pricing | Free tier: 10 active flows; paid $5/active flow/month | Free tier: 1,000 operations/month; paid plans start at $9/month |
| AI Features | AI agents with custom instructions, AI agent library (Lead Scorer, Deal Closer) | No native AI agents; relies on integrations like OpenAI |
| Deployment | Cloud + self-host (Docker, Helm, any cloud) | Cloud only (no self-hosting) |
| Integrations | 700+ apps including Gmail, OpenAI, Slack, Notion, HubSpot | 500+ connectors including Slack, Google Sheets, Salesforce, HubSpot |
| Target User | Teams wanting AI automation; enterprises needing self-hosted compliance | Marketers, ops, developers needing complex multi-step workflows |
| Best For | AI-first automation, cost-conscious, self-hosted enterprises | Complex integrations, high customization, no-code logic |
Choose Activepieces if AI agents, self-hosting, and cost predictability (flat $5/flow) are critical, especially for teams migrating from Zapier/Make with AI use cases. Pick Make if you need deep data transformations, routers, and a mature visual builder for complex non-AI workflows, and you're comfortable with operation-based pricing.
Open-source AI-first automation platform for teams building workflows and agents visually
Visit WebsiteVisual automation platform to build complex workflows and AI agents across 3,000+ apps
Visit WebsiteWho should pick which
- Enterprise compliance teamPick: Activepieces
Needs self-hosting for data control, AI agents for lead scoring/deal closing, and enterprise SSO (SAML/SCIM) – all built-in.
- Marketing ops managerPick: Make
Requires complex multi-step workflows with data transformations, routers, and many SaaS integrations; Make's visual builder excels at this.
- Solo founder building AI workflowsPick: Activepieces
Free tier supports 10 active flows, no coding, and native AI agents for sales/support automation – cost-effective for a lean operation.
- Developer building custom integrationsPick: Make
Needs sub-scenarios, iterators, and robust error handling for complex API orchestration without AI overhead.
- Cost-conscious team migrating from ZapierPick: Activepieces
Predictable per-flow pricing and self-hosting can cut costs vs. operation-based models; AI features add value.
Frequently Asked Questions
Activepieces vs Make: which should you choose?
Choose Activepieces if AI agents, self-hosting, and cost predictability (flat $5/flow) are critical, especially for teams migrating from Zapier/Make with AI use cases. Pick Make if you need deep data transformations, routers, and a mature visual builder for complex non-AI workflows, and you're comfortable with operation-based pricing.
Can I self-host Activepieces?
Yes, Activepieces supports self-hosting via Docker, Helm, or any cloud provider, giving full data control.
Does Make offer native AI agents?
No, Make does not have built-in AI agents; AI functionality requires integrating third-party AI services like OpenAI.
Which platform has more integrations?
Activepieces claims 700+ integrations (including OpenAI, Gmail, Slack, Notion, HubSpot), while Make has 500+ connectors.
Is Activepieces cheaper than Make?
For high-volume automation, Activepieces' flat $5/flow pricing is often cheaper than Make's per-operation scaling, which can increase costs significantly.
Can Make handle complex logic like routers and filters?
Yes, Make provides routers, filters, aggregators, iterators, and sub-scenarios for complex workflow design.
Does Activepieces support enterprise SSO?
Yes, it supports SAML 2.0 and Google SSO along with SCIM provisioning and advanced RBAC.
Which platform is better for non-technical users?
Both offer drag-and-drop builders. Activepieces is simpler for AI workflows; Make requires understanding of logic concepts for complex scenarios.
Can I automate lead scoring with Activepieces?
Yes, Activepieces includes an AI agent library with a Lead Scorer agent, enabling AI-driven lead scoring without coding.
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Last reviewed: May 12, 2026