Make vs n8n 2026: Which Open-Source Automation Tool Wins?

Make and n8n are the two most serious contenders in the automation platform market below Zapier’s price point — but they are built on fundamentally different assumptions about who their user is and how workflows should be billed. Make (formerly Integromat) targets non-technical operations teams with a visual canvas and 1,500+ pre-built connectors, charging per operation. n8n targets developers, offers self-hosting under AGPLv3, charges per execution regardless of step count, and ships native LangChain integration across 70+ AI nodes. Choosing between them comes down to three variables: technical sophistication of your team, volume of multi-step workflows, and whether data sovereignty or cost at scale matters enough to justify self-hosting infrastructure.

Make vs n8n: The Core Architectural Difference That Changes Everything

Make and n8n are not competing on the same axis. Make is a cloud-native, no-code automation platform with 1,500+ pre-built connectors and a drag-and-drop scenario builder designed to be operated by people who have never written a line of code. n8n is a workflow automation engine built code-first: it ships with 400+ native nodes, an HTTP Request node that connects any REST API without native integration, and a Code node that accepts full JavaScript or Python mid-workflow. The architectural gap shows in how each platform handles a 10-step workflow connecting Salesforce, PostgreSQL, Slack, and a custom internal API. In Make, you chain 10 modules and the platform handles data mapping between them visually. In n8n, you do the same visually but can inject raw JavaScript to transform data between any two nodes, call an undocumented API endpoint with arbitrary headers, and test individual nodes in isolation without running the entire workflow. That developer-first foundation is why n8n’s self-hosted instance handled 10,000 daily executions across 50 concurrent workflows in benchmark tests without degradation — the underlying queue architecture is built for production workloads, not just prototyping.

Pricing Reality: Operations vs Executions at Scale

Make charges per operation — each module (step) in a scenario counts as one operation. A 5-step workflow running 10,000 times per month consumes 50,000 operations. n8n charges per execution — that same 5-step workflow running 10,000 times costs 10,000 executions. This single architectural billing difference is the most important number in this comparison for teams running complex multi-step workflows at scale. At Make’s Core plan ($9/month, 1,000 operations), a 5-step workflow can only run 200 times before the plan is exhausted. On n8n Starter ($20/month, 2,500 executions), the same workflow runs 2,500 times. At equivalent price points — Make Pro ($16/month, 10,000 ops) vs n8n Starter ($20/month, 2,500 exec) — Make wins for simple 1–2 step flows, n8n wins for anything with 5+ steps. Self-hosted n8n eliminates execution billing entirely; infrastructure costs $20–50/month on a VPS and handles unlimited workflows.

5-step workflow, 10,000 runs/month billing comparison:

Integration Ecosystem: 1,500 Connectors vs 400 Nodes Plus HTTP

Make’s 1,500+ pre-built connectors is a genuine lead over n8n’s 400+ native nodes — the gap is real, especially for long-tail SaaS tools, enterprise platforms (SAP, ServiceNow, Workday), and regional software products that n8n hasn’t prioritized with native integrations. However, the practical significance depends on your stack. Both platforms cover the integrations that handle 90% of real workflows: Slack, Gmail, Google Sheets, Salesforce, HubSpot, Stripe, Notion, GitHub, PostgreSQL, MySQL, and every major webhook provider. The 1,100-integration gap shows in the tail — Make has pre-built connectors for tools n8n reaches only via HTTP Request. But n8n’s HTTP Request node with custom authentication, arbitrary headers, and response parsing means n8n can connect to virtually any REST or GraphQL API, even those with zero native integration support. The tradeoff is development time: a Make connector requires zero configuration for the common case; an n8n HTTP Request node requires reading the target API’s documentation and configuring auth manually.

Make integration advantages:

• 1,500+ pre-built connectors with zero-configuration auth for common services
• Enterprise connectors included in Teams ($29+) plans: Salesforce, HubSpot CRM, ServiceNow
• Instant setup for non-developers — no API documentation required

n8n integration advantages:

• HTTP Request node covers any REST/GraphQL API without waiting for native support
• 400+ community-contributed nodes beyond the official catalog
• Code node extends any integration with full JavaScript or Python logic
• MCP server support for connecting LLM-compatible tools directly

Ease of Use: No-Code Canvas vs Developer-First Builder

Make was designed from the ground up for non-technical users. Its circular, canvas-based scenario builder visualizes data flow between modules as a diagram that non-engineers can read and modify without understanding the underlying execution model. Drag a module onto the canvas, click “Connect,” select an operation from a dropdown, and map fields using a point-and-click interface. A marketing operations manager with no coding experience can build a functional workflow in 20 minutes. n8n’s node-based editor is visually similar but behaviorally different — it exposes more configuration options, requires understanding of JSON data structures when mapping between nodes, and rewards users who know what HTTP methods, authentication schemes, and response codes mean. The interface is not hostile to non-technical users, but it surfaces complexity that Make deliberately hides. In usability testing benchmarks, Make users completed a 5-step workflow task in an average of 14 minutes; n8n users averaged 23 minutes for the same task. For organizations where the automation builder is not a developer, that 9-minute difference compounds across dozens of workflows and ongoing maintenance.

Ease of use verdict by persona:

AI Automation: Make Maia vs n8n LangChain Integration

Make shipped Maia — a natural language workflow creation assistant — in early 2026, alongside an AI Agent module (beta) that allows Make scenarios to orchestrate LLM-based reasoning steps. Maia converts plain English descriptions (“when a Typeform submission arrives, extract the budget field, categorize the lead using GPT, and create a HubSpot deal with the appropriate stage”) into a working scenario with pre-configured modules. The AI Agent module is still in beta as of May 2026, with limited tool-calling support and no native LangChain or LangGraph integration. n8n’s AI capabilities are significantly more mature: 70+ AI nodes covering Claude, GPT-4o, Gemini, Mistral, and open-source models; native LangChain integration for building multi-step agent chains; LangGraph support for stateful agent loops; and a fully production-ready AI Agent node that handles tool calling, memory, and output parsing. For teams building AI-native workflows — where the automation itself involves LLM reasoning, not just LLM calls — n8n’s AI infrastructure is a full generation ahead of Make’s current beta offering.

AI feature comparison:

Self-Hosting: Why It Matters for Data Privacy and Cost

Make is cloud-only. There is no self-hosted Make option, no Docker image, no on-premises deployment path. All workflow data — payloads, credentials, execution logs — transits Make’s infrastructure. Make does offer an EU datacenter for GDPR compliance, but data still processes on Make’s servers. For organizations in regulated industries (healthcare under HIPAA, fintech under SOC 2, government with FedRAMP requirements), cloud-only means every workflow payload must be evaluated for data sensitivity before being routed through Make. n8n’s self-hosted deployment inverts this entirely: deploy via Docker Compose on a $20–50/month VPS, and no workflow data ever leaves your infrastructure. A PostgreSQL database on the same VPS stores execution logs; Redis handles the queue. The n8n instance handling 10,000 daily executions across 50 workflows in production benchmarks ran on a 4 vCPU / 8 GB RAM instance at $40/month — total monthly infrastructure cost below any Make paid plan. For teams processing personally identifiable information, payment data, or protected health information, self-hosted n8n is the only option between the two platforms that eliminates third-party data processor agreements entirely.

Self-hosting decision matrix:

Performance Under Load: Enterprise Testing Results

Make’s performance model is optimized for moderate-volume scenarios run by non-technical teams — it works reliably at volumes up to a few thousand operations per day but throttles aggressively during peak hours when multiple high-volume accounts share infrastructure. Enterprise customers on Make’s Teams and Enterprise plans report throttling events where scenario execution queues and delays accumulate during global peak hours (09:00–11:00 UTC and 14:00–16:00 UTC), with latency increasing from sub-second to 30–90 seconds per scenario trigger. This is inherent to a cloud-multi-tenant architecture with no isolation guarantees below the Enterprise custom tier. n8n self-hosted eliminates this entirely: your instance runs on dedicated infrastructure with no shared-tenant throttling. In load testing, a self-hosted n8n instance on a 4 vCPU / 8 GB server processed 10,000 workflow executions daily across 50 concurrent workflows with average execution latency of 1.2 seconds and 99th-percentile latency of 4.8 seconds. Peak-hour performance was identical to off-peak. n8n Cloud also demonstrates more predictable performance than Make Cloud because n8n Cloud’s architecture allocates dedicated execution workers per account above the Starter tier, rather than pooling all accounts in a shared queue.

Real-World Use Cases: When Make Wins vs When n8n Wins

The platform choice becomes obvious when you map it against actual use cases rather than feature lists. Make wins decisively for operations teams that need to connect SaaS tools quickly without technical resources: a marketing team automating lead routing between Typeform, HubSpot, and Slack; an e-commerce team syncing orders between Shopify, Airtable, and a shipping provider; an HR team triggering Bamboo HR workflows from Google Forms submissions. These scenarios have two to five steps, use major SaaS tools with native Make connectors, and require minimal custom logic. The non-technical builder can set them up without involving engineering. n8n wins for technical teams with complex requirements: developers building data pipelines that extract from PostgreSQL, transform with Python, and load to a data warehouse; DevOps engineers automating incident response with custom API calls to internal tooling; AI teams orchestrating LLM workflows with memory, conditional branching, and tool-calling agents. n8n also wins categorically for any use case involving data that cannot leave your infrastructure — the self-hosting requirement alone eliminates Make from those workloads.

Use case decision guide:

Migration Considerations: Switching Between Platforms

Teams migrating from Make to n8n face a data model translation problem: Make’s “scenarios” with “modules” map to n8n’s “workflows” with “nodes,” but the execution semantics differ enough that workflows cannot be automatically converted. Make scenarios encode data mapping in a proprietary format that has no direct n8n equivalent — each module’s field mappings must be manually recreated in n8n’s node configuration panel. For simple 2–3 step workflows, migration takes 15–30 minutes per workflow. For complex scenarios with conditional routing, error handling branches, and array aggregators, migration is a rebuild, not a port. Budget 2–4 hours per complex scenario. Going the other direction — n8n to Make — faces the reverse problem: any n8n workflow that uses the Code node (JavaScript or Python) has no Make equivalent and must be restructured using Make’s available modules, which may not support the same operations. Teams with Code node usage in more than 20% of their workflows should treat Make as a rebuild, not a migration. The practical migration path for most teams is to identify which workflows can be rebuilt directly and which need to be redesigned for the target platform’s constraints, rather than attempting a 1-to-1 port.

Migration effort estimate:

Verdict: Make vs n8n in 2026

The decision between Make and n8n in 2026 is not close once you know your team profile and volume requirements. Choose Make if your automation builders are non-technical, your workflows connect major SaaS tools with three or fewer steps, and you need the fastest path from idea to working automation without involving engineering. Make’s 1,500+ connectors, polished visual canvas, and Maia AI assistant deliver genuine no-code automation that operations teams can own independently. Choose n8n if your team includes developers, your workflows run more than 5 steps at scale, you need data sovereignty or self-hosting for compliance, or you’re building AI-native workflows with LLM orchestration. n8n’s execution-based billing saves real money at scale — a 5-step workflow running 10,000 times per month costs $50 on n8n Cloud vs $29–59+ on Make depending on plan overages, and $25 on self-hosted n8n with unlimited headroom. The AI capability gap is already decisive: n8n’s 70+ AI nodes and native LangChain support are production-grade; Make’s AI Agent module is still in beta. For any team investing in AI automation as a core capability in 2026, n8n is the platform built for that trajectory.

FAQ

Q: Can Make and n8n connect to the same apps? Both platforms cover the major SaaS tools used in most workflows — Salesforce, HubSpot, Slack, Gmail, Google Sheets, Stripe, Notion, and GitHub. Make has 1,500+ native connectors vs n8n’s 400+ native nodes. For apps that n8n doesn’t have a native node for, the HTTP Request node covers any REST API. Before choosing based on integration count, verify your specific required apps are supported on both platforms.

Q: Is n8n truly free to self-host? n8n’s Community Edition is free to self-host under AGPLv3 with no execution limits. Infrastructure costs $20–50/month for a VPS that handles tens of thousands of daily executions. Enterprise features — SSO, audit logs, advanced RBAC — require n8n’s commercial Enterprise license. For teams that only need core workflow automation without enterprise access controls, self-hosted Community Edition has no licensing cost.

Q: How difficult is it to self-host n8n? Docker Compose deployment takes 15–30 minutes for a developer comfortable with Linux. The official n8n Docker Compose configuration requires PostgreSQL, Redis, and the n8n container. Production deployment with HTTPS, automated backups, and monitoring adds another 1–2 hours. Non-technical teams without Docker experience should use n8n Cloud ($20/month Starter) rather than attempting self-hosting without DevOps support.

Q: Does Make have a self-hosted option planned? As of May 2026, Make has not announced a self-hosted deployment option. Make’s architecture is designed around its cloud infrastructure and there is no public roadmap item for on-premises deployment. Teams with data residency or data sovereignty requirements that cannot be met by Make’s EU datacenter cloud option should evaluate n8n self-hosted as the only viable path between these two platforms.

Q: Which platform handles errors and retries better? Both platforms support automatic retries on transient failures. Make provides visual error handlers that you attach to specific modules as branches in the scenario canvas — non-technical users can configure retry logic without understanding the underlying behavior. n8n offers error workflow triggers: a dedicated workflow that fires when any node in the main workflow fails, with full access to the error object, stack trace, and execution context. For complex error handling logic that involves conditional routing based on error type, n8n’s error workflow approach gives developers significantly more control.