Introduction
Choosing the right workflow automation platform can make the difference between seamless business operations and constant technical headaches. With n8n and Make (formerly Integromat) emerging as top alternatives to Zapier, decision-makers face a critical choice between technical flexibility and user-friendly simplicity.
This blog compares both platforms based on features, real-world capabilities, pricing models, integration support, customization depth, and the strategic role of tools like Peliqan in building sustainable automation systems.
Platform Overview: Technical Power vs Visual Simplicity
n8n: Developer-Centric Automation
n8n positions itself as a developer-friendly automation platform with a fair-code licensing model. It allows unlimited self-hosting, full access to the source code, and strong customization via JavaScript and Python. Organizations that prioritize data sovereignty, infrastructure control, or complex business logic will find n8n particularly attractive.
Make: Business-User-Friendly Experience
Make, by contrast, is designed for business users. It emphasizes ease of use, offering a mind-map-style visual builder, pre-built templates, and deep app integrations – all without requiring coding. Its cloud-first architecture and 2,800+ native app connectors make it accessible for teams without technical expertise.
Feature Comparison
Both platforms offer visual workflow builders but approach them differently. Make guides users through automation using connected modules in a clean, linear layout. n8n uses a flexible node-based canvas that supports branching, conditional logic, and multiple triggers per workflow.
n8n’s open-source foundation gives it a unique edge: users can inject custom code, manage global error flows, and even build and share plugins. Make restricts users to its visual UI and expression editor, focusing on stability and simplicity.
Key Differences at a Glance
- n8n: Multiple triggers, custom code (JavaScript/Python), global error handling
- Make: One trigger per scenario, no code support, simpler visual flow
Pricing Breakdown
| Plan Type | Platform 1 (Cloud/Self-hosted) | Make |
|---|---|---|
| Free Plan | Community / self-hosted: Free / unlimited (self-hosted); Cloud free options exist for testing | $0 – 1,000 operations / month (Free tier) |
| Starter / Entry Paid | $24 / month (monthly) – or $20 / month (billed annually) – Starter cloud (~30,000 executions/year ≈ 2,500/month) | Core ≈ $9 / month – example pricing point for 10,000 ops / month (tier examples vary) |
| Next Paid / Pro | $60 / month (monthly) – $50 / month (billed annually) – Pro cloud (~120,000 executions/year) | Pro ≈ $16 / month – representative higher paid tier for larger ops; Make charges by operations so higher usage needs higher plans |
| Billing Model | Per execution (one full workflow run = one execution). Self-hosting is free apart from infrastructure | Per operation (each module/action in a scenario counts as an operation) |
Sources: n8n pricing & Make pricing.
Billing model
- n8n: billed by executions (one full workflow run = one execution); self-hosting is free aside from infrastructure.
- Make: billed by operations (each module/action counts), which can make long/multi-step scenarios add up quickly.
Example Cost Impact (updated)
A 10-step workflow running 1,000 times in a month
- n8n: counts as 1,000 executions → fits comfortably within n8n Starter cloud (Starter ≈ 30k exec/year → ≈2.5k/month).
- Make: counts as 10,000 operations → fits within a ~10k-ops paid tier, but heavier schedules require higher-op plans.
A 50-step workflow running 1,000 times in a month
- n8n: still 1,000 executions (step-count doesn’t change execution count).
- Make: 50,000 operations → will push into significantly higher pricing tiers.
Ease of Use
Make: Rapid Setup for All Users
Make is ideal for teams without developers. It offers guided setup, a drag-and-drop builder, and hundreds of templates. Its visual interface helps non-technical users build automations intuitively.
n8n: Deeper Control for Technical Teams
n8n has a steeper learning curve. It assumes comfort with JSON, data structures, and scripting. However, this complexity unlocks deeper control for advanced workflows, making it the preferred tool for technically sophisticated teams.
Ease-of-Use Comparison
- Make: Quick onboarding, no coding, drag-and-drop simplicity
- n8n: Requires technical knowledge, but offers greater flexibility
Integration Ecosystem
Make: Extensive Native Library
Make offers over 2,800 native integrations with popular business tools. Each app connector typically supports multiple triggers, actions, and real-time data syncing. Make focuses on plug-and-play convenience, letting business users authenticate and build workflows in minutes.
n8n: Customizable and API-First
n8n offers 400+ official integrations and 2,900+ community-built nodes. While the initial setup can require API keys or OAuth setup, its API-first approach means anything with an API can be connected. Developers can write custom authentication flows or webhook handlers with full flexibility.
Integration Highlights
- Make: More plug-and-play integrations, strong template library
- n8n: Supports any REST API, excels in custom or niche tool integrations
Hosting & Security
n8n: Full Self-Hosting Flexibility
n8n can be fully self-hosted. This enables teams to run it on-premises, on private cloud infrastructure, or in regulated environments. It’s an ideal solution for teams needing complete control over workflow data and architecture.
Make: Simplicity Through Cloud-Only Setup
Make is cloud-only. While it simplifies onboarding and eliminates infrastructure management, it does introduce vendor lock-in and may not meet compliance needs in regulated sectors.
When to Choose What
- Choose n8n if data control, air-gapped hosting, or self-deployment is critical.
- Choose Make if you want zero-maintenance automation with instant access.
Customization and Developer Power
n8n: Built for Engineers
n8n offers unmatched flexibility for developers. Its built-in JavaScript and Python support means you can perform complex data transformations, advanced branching logic, and handle edge-case API behaviors. You can even design reusable sub-workflows or share nodes with the open-source community.
Make: Power Without Code
Make’s customization is mostly visual. It supports expressions for conditional logic and calculations but lacks direct access to scripting languages. It’s best for users who prefer visual control over technical depth.
Technical Edge
- n8n: Built for developers; perfect for complex integrations and AI-based workflows
- Make: Built for business users; great for team-wide workflow management
How Peliqan Complements n8n and Make in Data-Heavy Workflows
Both n8n and Make are excellent at orchestrating automations – triggering actions, moving data between apps, and keeping business processes flowing. But once workflows become data-heavy, you’ll quickly notice their limitations.
When Workflows Become Data-Heavy
“Data-heavy” means:
- Handling large amounts of data (bulk syncs, migrations, imports).
- Dealing with complex structures (nested JSON, multi-level joins).
- Connecting multiple sources where data silos exist (CRM + ERP + Accounting + Support).
- Performing transformations (cleaning, deduping, enriching, or joining datasets).
These challenges often appear in real-world use cases like:
- Data syncs between business tools.
- Data migrations or onboarding workflows.
- Imports of product catalogs, invoices, or customer lists.
- AI agents in n8n or Make (using RAG, Text-to-SQL, or MCP) that require queryable, unified data.
This is where Peliqan steps in as the data foundation layer for both platforms.
What Peliqan Adds to n8n and Make
Peliqan provides an all-in-one data infrastructure that sits underneath your automations, ensuring they scale and remain reliable:
- 250+ connectors: Go beyond native nodes/modules with broad SaaS, file, DB, and API coverage.
- Built-in data warehouse: Cache and query large datasets efficiently instead of pulling raw data every time.
- Transformations: Use Python/SQL logic to clean and prepare data centrally, not scattered across workflows.
- 360° unified views: Merge CRM, ERP, accounting, and product data into business-ready models.
- Data explorer & governance: Browse, monitor, and enforce schemas across workflows.
- AI readiness: Built-in RAG and Text-to-SQL support so AI agents in n8n or Make can query structured, reliable business data.
n8n and Make — With & Without Peliqan
| Aspect | n8n or Make Alone | With Peliqan |
|---|---|---|
| Data Sources | Limited to native connectors | 250+ connectors (SaaS, DBs, files, APIs). Custom connector under 48 hrs |
| Data Handling | Live API calls; can slow with volume | Cached warehouse for fast, scalable access |
| Transformations | Ad-hoc logic inside workflows | Central pipelines in Python/SQL |
| Scaling | Workflows grow complex with bulk data | Robust ETL handles high volume & complex joins |
| Governance | Minimal visibility/control | Unified models, schemas, and lineage tracking |
| AI Agents | Rely on raw JSON payloads | Query clean business data via Text-to-SQL & RAG |
Who Benefits Most
- n8n creators and power users who hit scaling or complexity limits.
- Consultants & agencies building client automations where reliability matters.
- Business & ops teams that need accurate, governed data across tools.
- AI/ML teams creating intelligent workflows and agents on top of business data.
In short:
- Use n8n if you need developer-level flexibility and self-hosting.
- Use Make if you want fast, no-code setup and broad plug-and-play integrations.
- Use Peliqan with either when workflows involve heavy data operations, transformations, or AI agents—turning automation into a scalable, data-driven system.
Conclusion
n8n is ideal for technical teams that need full control, want to self-host, or need to build complex, scalable workflows cost-effectively.
Make is the right choice for business teams that want to move fast, don’t want to code, and prefer a visual-first experience with minimal setup.
Peliqan helps teams using either tool go further by solving for data prep, routing, and analytics. It enables a layered approach – automation meets data infrastructure.
