Zapier vs Make vs n8n 2026: Automation Comparison
Zapier, Make, and n8n each solve automation differently. Head-to-head comparison on pricing, complexity limits, integrations, and self-hosting for business workflows.
Zapier Integrations
Make Cost Savings vs Zapier
n8n Self-Hosted Cost
n8n Native Integrations
Key Takeaways
Workflow automation has become table stakes for competitive businesses in 2026. Three platforms dominate the market — Zapier, Make (formerly Integromat), and n8n — and each has evolved significantly over the past 18 months with AI-powered features, expanded integration libraries, and restructured pricing. Choosing between them is no longer just a price comparison; it is a decision about your team's technical capacity, data compliance requirements, and long-term automation ambitions.
This comparison cuts through the marketing to give you an honest head-to-head analysis across the dimensions that matter most: real-world pricing at scale, complexity handling, integration breadth, AI capabilities, and which specific use cases each platform handles better than its competitors. Whether you are evaluating for the first time or considering migrating from your current tool, this guide gives you the decision framework to choose correctly.
Automation Platform Market in 2026
The no-code and low-code automation market has consolidated around three distinct tiers. Zapier occupies the premium, ease-first tier with the largest app ecosystem and the most polished user experience. Make sits in the middle — more powerful than Zapier, more accessible than code, and considerably cheaper at equivalent volumes. n8n has grown from a niche developer tool into a serious enterprise alternative, particularly after raising $55 million in Series B funding in 2024 and launching a managed cloud service that removes the infrastructure burden.
The defining shift of 2025–2026 has been AI integration. All three platforms now offer native connections to OpenAI, Anthropic, and Google Gemini, plus purpose-built AI agent workflow templates. Zapier launched AI Actions and natural language workflow creation. Make introduced AI scenarios with built-in prompt engineering interfaces. n8n integrated LangChain natively, making it the strongest choice for teams building multi-agent AI pipelines.
#1
Zapier
Largest app library, easiest UX
#2
Make
Visual power at 60% lower cost
#3
n8n
Open-source, self-hostable, unlimited
Market dynamics have also shifted with AI agents entering the automation space. Tools like Salesforce Agentforce and Microsoft Copilot Studio now compete at the enterprise end of the market with agent-based automation that goes beyond triggers and actions. However, Zapier, Make, and n8n retain a decisive advantage in the SMB and mid-market segments through broader app compatibility, lower cost, and faster workflow deployment timelines.
Zapier: Simplicity at Scale
Zapier built its dominance on one principle: any business user should be able to automate a workflow without involving an engineer. That philosophy is evident in every design decision — the linear trigger-action model, the guided setup wizard, the pre-built templates, and the natural language workflow creation introduced in 2025. For teams where the bottleneck is technical access, not workflow complexity, Zapier removes that bottleneck entirely.
- 7,000+ integrations — by far the largest app library, covering virtually every SaaS tool
- Fastest time-to-automation — most users build their first zap within 15 minutes of signing up
- Natural language creation — describe your workflow in plain English and Zapier suggests the steps
- Enterprise-grade support — dedicated account managers, SSO, and SLA guarantees on higher plans
- Tables & Interfaces — built-in database and form tools eliminate need for external data storage on simple workflows
- Steep pricing at scale — 100K tasks/month costs $500+; costs compound quickly on high-volume workflows
- Limited branching logic — conditional paths and loops require workarounds compared to Make's native routing
- No self-hosting option — all data passes through Zapier's servers; not suitable for strict data residency requirements
- Linear workflow model — parallel execution and complex merging patterns require premium features
Zapier Pricing (2026)
| Plan | Price/Month | Tasks/Month | Key Limits |
|---|---|---|---|
| Free | $0 | 100 | Single-step zaps only |
| Starter | $20 | 750 | Multi-step zaps, filters |
| Professional | $49 | 2,000 | Paths, auto-replay, premium apps |
| Team | $69 | 2,000 | Shared workspaces, unlimited users |
| Enterprise | Custom | Custom | SSO, SAML, advanced admin, SLA |
Make: Visual Power
Make (rebranded from Integromat in 2022) offers a fundamentally different paradigm: an infinite canvas where automation scenarios are built visually, with modules connected by lines that show data flow. This approach makes complex branching logic, parallel execution, and data transformation far more intuitive than Zapier's linear list. The tradeoff is that Make's interface, while visual, has a steeper initial learning curve — the concepts of scenarios, modules, routes, and bundles require more upfront investment to understand.
Where Make truly differentiates is price-to-power ratio. Its operations-based pricing (each module execution = one operation) delivers dramatically more automation capacity per dollar than Zapier's task-based model. A Make Core plan at $9/month provides 10,000 operations — equivalent to running a three-module scenario 3,300 times monthly. The comparable Zapier Professional plan costs $49/month for 2,000 tasks.
- Visual scenario builder — canvas-based interface makes complex workflows easier to audit and debug
- Native routers & iterators — conditional branching, array processing, and data aggregation built into core
- 60% cheaper than Zapier — operations pricing gives dramatically more execution volume per dollar
- 1,500+ integrations — smaller than Zapier but covers all major platforms with deeper module customization
- Data stores & custom variables — built-in key-value storage for stateful workflows without external databases
- Steeper learning curve — bundle/module concepts and canvas interface require 3–5 hours to become productive
- Smaller app library — 1,500 integrations vs Zapier's 7,000 means some niche tools require HTTP modules
- No self-hosting — cloud-only like Zapier; data residency limited to Make's US/EU data centers
- Support quality varies — enterprise support is solid but free/core tier users report slower response times
Make Pricing (2026)
| Plan | Price/Month | Operations/Month | Key Features |
|---|---|---|---|
| Free | $0 | 1,000 | 2 active scenarios, 15-min intervals |
| Core | $9 | 10,000 | Unlimited scenarios, 1-min intervals |
| Pro | $16 | 10,000 | Custom variables, full-text execution log |
| Teams | $29 | 10,000 | Team collaboration, shared templates |
| Enterprise | Custom | Custom | SSO, HIPAA, dedicated support, SLA |
n8n: Open-Source Flexibility
n8n (pronounced "nodemation") is the outlier in this comparison — an open-source workflow automation platform that you can self-host on your own infrastructure at zero licensing cost, or use via n8n's managed cloud service. This fundamental architectural difference creates a set of capabilities that Zapier and Make simply cannot offer: complete data sovereignty, unlimited executions on self-hosted deployments, custom code nodes in JavaScript or Python, and the ability to connect directly to internal databases without API intermediaries.
The n8n community has grown to over 60,000 active users with more than 400 native integrations. Its LangChain integration — introduced in 2024 — positions n8n as the strongest platform for teams building AI agent workflows. Where Zapier and Make treat AI as another integration category, n8n treats it as a first-class workflow primitive, with native support for agent loops, tool calling, memory management, and RAG pipelines.
- Self-hostable & open-source — run on your own servers; complete control over data residency and security
- Unlimited executions — self-hosted deployment has no execution limits; only server resources constrain volume
- Custom code nodes — write JavaScript or Python directly in workflow nodes for complex transformations
- Native LangChain & AI agents — strongest AI workflow capabilities of the three platforms
- Direct database connections — connect to PostgreSQL, MySQL, MongoDB, Redis without REST API intermediaries
- Infrastructure overhead — self-hosted deployment requires DevOps knowledge; updates and backups are your responsibility
- Smaller integration library — 400+ native integrations vs 7,000+ for Zapier; HTTP request node fills gaps but requires API knowledge
- Steeper learning curve — node-based concepts and self-hosting complexity make onboarding harder for non-technical users
- Community support model — paid support available on cloud plans; self-hosted users rely on community forums and documentation
n8n Pricing (2026)
| Option | Price/Month | Executions | Notes |
|---|---|---|---|
| Self-Hosted (Community) | $0 | Unlimited | Server costs only; full open-source |
| Starter (Cloud) | $20 | 2,500/month | Managed hosting, no DevOps needed |
| Pro (Cloud) | $50 | 10,000/month | Variables, external secrets, debug mode |
| Enterprise (Cloud/Self-hosted) | Custom | Custom | SSO, LDAP, audit logs, dedicated support |
Feature Comparison Table
The table below provides a direct side-by-side comparison across the dimensions that most influence platform selection decisions. Pricing reflects 2026 published rates; enterprise tiers are negotiated separately for all three platforms.
| Feature | Zapier | Make | n8n |
|---|---|---|---|
| Integration Count | 7,000+ | 1,500+ | 400+ |
| Ease of Use | Easiest | Moderate | Technical |
| Starting Price | $20/mo | $9/mo | $0 (self-hosted) |
| Value at Scale | Poor | Good | Excellent |
| Self-Hosting | No | No | Yes |
| Custom Code | Code step (JS/Python) | HTTP + custom functions | Native code nodes |
| AI / Agent Workflows | AI Actions | AI modules | Native LangChain |
| Visual Workflow Builder | Linear list | Canvas-based | Canvas-based |
| Branching / Routing | Paths (Pro+) | Native routers | IF/Switch nodes |
| Data Residency | US/EU servers | US/EU servers | Your infrastructure |
| Open Source | No | No | Yes |
| Enterprise Support | Strongest | Good | Cloud plans |
Use Case Recommendations
The right platform is not universal — it depends on your team's technical profile, expected automation volume, and the types of workflows you need to build. Use these profiles as a starting framework, then validate with a 14-day free trial on your top two candidates before committing.
- Non-technical team members own the workflows
- You need a specific niche app integration not on Make or n8n
- Monthly task volume stays below 5,000
- Enterprise SLAs and dedicated account management are required
- Speed of deployment beats cost optimization
- Workflows involve branching logic, loops, or data transformation
- Your Zapier bill is growing faster than business value justifies
- Monthly volume is 5,000–100,000 operations
- Team includes at least one moderately technical user
- Visual scenario canvas resonates better than a linear list
- Data cannot leave your infrastructure (GDPR, HIPAA, financial)
- Monthly executions exceed 100,000 (self-hosted eliminates per-execution cost)
- Building AI agent workflows or LangChain-powered automations
- Engineering team comfortable with Docker or cloud infrastructure
- Direct database connections are required in workflows
For teams building autonomous AI-powered marketing automation, the platform choice also affects which AI capabilities are natively available. n8n's LangChain integration makes it the clear choice for complex agent orchestration. Zapier's AI Actions and natural language creation make it the fastest way to get simple AI-augmented workflows into production without technical help.
Migration Between Platforms
Migrating your automation stack is a significant undertaking. There is no universal migration tool that reliably converts Zapier zaps to Make scenarios or n8n workflows — complex logic, custom filters, and data transformations all require manual reconstruction. A structured migration approach minimizes disruption and ensures nothing critical breaks during the transition.
- 1Inventory all workflows — export or document every active automation with its trigger, actions, and estimated monthly run count
- 2Classify by complexity — sort into simple (trigger + 1–2 actions), medium (branching, filters), and complex (loops, data transformation, error handling)
- 3Verify integration availability — confirm all apps used in your workflows have native integrations on the target platform before committing
- 4Prioritize by business impact — rebuild revenue-critical workflows first (lead routing, order processing) and lower-priority workflows last
- 5Run parallel for 30 days — keep both platforms active for critical workflows until the new platform has proven stability
Simple Workflow
Trigger + 1–2 linear actions
15–30 minutes per workflow
Medium Workflow
Branching, filters, data mapping
1–2 hours per workflow
Complex Workflow
Loops, error handling, custom code, multi-branch
3–8 hours per workflow
Full Stack Migration
20–50 workflows, mixed complexity
4–8 weeks with parallel running
Building an Automation Strategy
Selecting a platform is only the first step. The teams that get the most value from automation build a systematic strategy around their tool of choice — mapping processes, prioritizing by ROI, and treating their automation library as a managed asset rather than a collection of one-off fixes.
1Audit Before You Build
Map every manual repetitive task in your team's workflow before building a single automation. Categorize by time spent per week and error frequency. The highest-value automations target tasks that (a) happen frequently, (b) are error-prone when done manually, and (c) follow a consistent, predictable pattern. Data entry between systems, status notifications, and report generation consistently top this list.
2Start Simple, Expand Iteratively
Resist the urge to build comprehensive automation from day one. Start with the three to five highest-value workflows, prove they work reliably in production, and use that confidence to expand. Teams that try to automate everything at once create brittle, hard-to-maintain workflow libraries that break when app APIs change. Iterative automation compounds — each working workflow creates capacity to build the next.
3Document and Version Control Workflows
Treat automation workflows like code — document what each workflow does, why it exists, and what apps it depends on. For n8n self-hosted deployments, export workflow JSON to a Git repository to track changes and enable rollbacks. For Zapier and Make, maintain a spreadsheet inventory of all active workflows with their trigger conditions, owners, and last verified date. Workflows without clear owners atrophy when app APIs change.
4Monitor Error Rates and Execution Costs
All three platforms provide error logs and execution history. Review error rates weekly in the first month of any new workflow, then monthly once stable. Track your monthly task or operation consumption against your plan limits — silent task overages are a common source of unexpected billing. Set email alerts at 80% of your monthly limit so you have time to optimize high-volume workflows before hitting the ceiling.
For businesses integrating automation with CRM workflows, see our guide on CRM and automation strategy for a structured approach to connecting your automation platform with your customer data. The most productive automation stacks treat the CRM as the central data hub and use Zapier, Make, or n8n as the connective tissue that keeps data synchronized across all tools.
Ready to Build Your Automation Stack?
Choosing between Zapier, Make, and n8n is the start — not the end — of your automation journey. Digital Applied helps businesses design and implement automation strategies that fit their team, budget, and growth trajectory.
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