TL;DR: Make vs n8n in 2026, Picked by Workflow Shape and Owner Rather Than Features
Make vs n8n in 2026 comes down to two questions: who on your team will fix automations when they break, and how many steps each run takes. For most non-technical founders, the "cheaper" n8n is actually the more expensive pick.
- "n8n is cheaper" is only half true. Make bills per module action (about $0.0009 per credit). n8n bills per full workflow run. Break-even sits at roughly 6, 9 module actions per run. Past that, long or looping workflows can cost 80, 90% less on n8n.
- Make wins for short flows and non-technical teams. A 5-step order flow at 3,000 runs a month costs less on Make and goes live in hours, not days. Many bootstrappers start on Make's free tier and switch to n8n later, as shown in this AI automation workshop for startups.
- n8n wins for AI agents, self-hosting, and EU data control. Its 2.0 release ships 70+ AI nodes. Self-hosting removes per-run fees, but server upkeep adds hidden hours to your bill.
- Both cloud plans can stop without warning at their limit. Set usage alerts at 70% and 90%, and keep 2x headroom on customer-facing flows.
The full article gives you a cost formula, the FITS scorecard, and a 30-day test plan, so you can choose in one afternoon instead of migrating next year. If Zapier is also on your shortlist, compare all three in this n8n vs Make vs Zapier founder guide. Then count the module actions in your busiest workflow: under 6 points to Make, and over 9 points to n8n.
Check out latest news, stats and trends that you might like:
Make vs n8n is the automation decision I see founders get wrong most often in 2026, and they usually get it wrong for the same reason: they compare feature lists when they should compare billing units, ownership, and who will maintain the thing at 11 p.m. when it breaks. I am Violetta Bonenkamp, also known as Mean CEO, and I run several ventures in parallel from Europe, including CADChain (deeptech for CAD and IP protection) and Fe/male Switch, a startup game and incubator built almost entirely with no-code tools. Automation is the invisible staff of every company I run. This article breaks down the numbers, the hidden traps, and the founder-specific verdicts so you can pick the right platform in one afternoon instead of migrating painfully a year from now.
Here is why this matters right now. Make renamed its billing unit from operations to credits, n8n shipped its 2.0 release with more than 70 dedicated AI nodes, and both platforms are pushing hard into AI agents. The price gap between them can swing from “basically identical” to 80, 90% in n8n’s favour depending on the shape of your workflow. Let’s break it down.
What is the quick verdict on Make vs n8n in 2026?
Choose Make if non-technical people (ops, marketing, sales, support) will build and own your automations and you want zero servers to manage. Choose n8n if you have a clear technical owner, run long or looping workflows at volume, need code-level control, or want to self-host for data control. For early-stage, non-technical founders, Make is the stronger default because a working workflow in HOURS beats a cheaper workflow in DAYS when you are still validating a market.
The right choice depends most on technical capacity and workflow shape: how many steps each run has, how many items it loops over, and whether someone on your team can own a server. Neither tool is “better” in the abstract. As the Peliqan team puts it, both are “mature 2026 platforms with very different bets about who their primary user is.”
How do Make and n8n compare at a glance?
Prices below come from published plan pages as reported by comparison sources in 2026 (checked between May and October 2026). Ratings marked “editorial” are my judgment based on running automations across several ventures.
| Criterion | Make | n8n | Better for |
|---|---|---|---|
| Best suited to | Business-owned automation (ops, marketing, sales) | Engineering-owned automation (developers, technical ops) | Depends on owner |
| Billing unit | Per credit (1 credit per module action) | Per execution (1 full workflow run) | Make for short runs, n8n for long runs |
| Entry paid price | $9/month, 10,000 credits (annual billing) | $20/month, 2,500 executions (Cloud) | Depends on steps per run |
| Free option | 1,000 ops/month, 2 scenarios | Community Edition, unlimited when self-hosted | n8n (if you can host) |
| Pre-built app connectors | 1,800+ to 3,000+ (sources differ) | 400+ plus HTTP Request and custom nodes | Make |
| Time to first workflow | Hours | Days, including setup | Make |
| Custom code | Limited (JavaScript modules) | Full JavaScript and Python nodes | n8n |
| Hosting | Fully managed, no self-hosting | Self-hosted or n8n Cloud | Depends |
| AI agents (editorial) | Maia assistant (early 2026), agent builder in beta | 2.0 release, native LangChain, 70+ AI nodes | n8n |
| Maintenance burden | Low | Medium to high (self-hosted) | Make |
| EU considerations | Data sits with vendor; check region and DPA | Self-hosting on EU servers gives full data residency control | n8n for strict data rules |
| Overall verdict (editorial) | Fastest path to live workflows | Cheapest and most flexible at scale | Conditional |
What are Make and n8n?
What is Make?
Make (formerly Integromat) is a managed visual automation platform where you drag modules onto a canvas, connect them, and configure each step in a side panel. It solves the “connect app A to app B without an engineer” problem for teams that move fast. Ops managers, marketers, agencies, and solo founders use it most. It is not a self-hostable tool, and it is not built for heavy custom code, although it does offer an HTTP module and custom apps.
What is n8n?
n8n is an open-source, node-based workflow automation platform that you can run on your own server or use through n8n Cloud. It solves the “I need full control, custom logic, and predictable costs at volume” problem. Developers, technical ops teams, and AI builders use it most. It is not a toy for developers only: the visual editor has improved a lot, but it still asks you to understand credentials, execution modes, and data structures sooner than Make does.
What is the fundamental difference?
Make optimizes for speed-to-live and accessibility for non-engineers, while n8n optimizes for control, extensibility, and cost predictability at scale. They are direct substitutes for most app-to-app workflows and partially overlapping for advanced AI agent systems, where n8n goes deeper. You can also combine them, which I cover later.
How was this comparison made?
- Evaluation date: October 2026.
- Evidence used: published pricing as reported by comparison sources (Peliqan, iimagined.ai, Automation Atlas, eesel AI, Crux Digits, HatchWorks, Goodspeed Studio, LowCode Agency, Stob.AI, G2), plus my own operating experience running no-code stacks across several European ventures.
- Test scenario: a small EU B2B company automating order confirmations and lead routing, priced at multiple volumes.
- Criteria and weighting: pricing model and true cost (highest weight), ownership and learning curve, app coverage, AI agent depth, hosting and data control, reliability when limits hit.
- Geographic scope: EU-first. Dollar and euro prices are mixed because vendors publish them that way.
- Commercial disclosure: no affiliation with Make or n8n, no sponsorship.
- Limitations: plan prices and connector counts change often, and sources disagree on Make’s connector count (1,800+ vs 3,000+). Treat numbers as snapshots.
Which decision criteria actually matter for founders?
Most comparisons list 30 features. Founders need six. Here is why each one changes your runway or your workload:
- Billing model and true cost: decides whether automation stays a rounding error or becomes a real line in your burn.
- Ownership and learning curve: decides whether your team ships workflows or waits on one developer.
- App coverage and custom connections: decides how often you hit “no module exists for this tool.”
- AI agent depth: decides whether you can build multi-step AI assistants or only add an AI step.
- Hosting and data control: decides what you tell an enterprise customer or GDPR auditor.
- Behaviour when limits hit: decides whether a busy month quietly breaks customer-facing processes.
Is n8n cheaper than Make? Pricing model compared
Short answer for the featured snippet: n8n is cheaper for long, looping, or high-volume workflows, and Make is cheaper for short, simple, low-volume workflows. Make charges about $0.0009 per credit on its $9, 10,000-credit tier, and n8n Cloud Starter charges about €0.008 per execution. According to the Make vs n8n credits and executions pricing breakdown from iimagined.ai, the break-even sits at roughly SIX TO NINE module actions per run. Self-hosting n8n Community Edition removes the per-run price entirely.
- Make: every module action costs one credit, and a module runs once for every bundle of data it receives. A 10-step scenario looping over 20 items burns credits fast. Strength: very cheap entry for short scenarios.
- n8n: a 10-step workflow that runs once counts as ONE execution. Crux Digits reports that a 10-step workflow run 10,000 times a month is 10,000 billable units on n8n vs 100,000 on per-task tools, with practitioner estimates putting the cost gap at 80, 90% in n8n’s favour.
- Key trade-off: Make gives you cheap simplicity and punishes length; n8n gives you flat-per-run pricing and charges you in setup time (or server time when self-hosted).
- Winner: n8n for long or high-volume workflows; Make for short, low-volume ones.
- Confidence: High, because published pricing from several sources agrees on the mechanics.
A surprising detail many founders miss: Crux Digits also notes that at 100,000 monthly operations, Make reportedly stays under $100, while Zapier can push past $300. So Make is cheap compared with Zapier. The n8n advantage only shows up when you count per run instead of per step.
Which is easier to learn: Make or n8n?
Ownership matters because the person who builds the automation is the person who fixes it. In Stob.AI’s operating-model view of Make vs n8n, the time to first workflow is “Hours” on Make and “Days (including setup)” on n8n. That gap is the whole story for a non-technical team.
- Make: flowchart-style builder, friendly side panel, polished templates. HatchWorks describes it as easy to grasp, with a clean design that helps newcomers feel productive fast. Weakness: very branched scenarios become messy to debug.
- n8n: denser node editor with nested subflows, callable workflows, and advanced conditionals. Goodspeed Studio calls it “steeper but rewarding”. Weakness: credentials management and execution modes take real effort upfront.
- Key trade-off: Make wins the first month, n8n often wins month twelve when workflows get tangled.
- Winner: Make for non-technical teams; n8n for developers.
- Confidence: High, consistent across sources.
My own rule from building Fe/male Switch: “Default to no-code until you hit a hard wall.” For most founders, the first hard wall is cost or custom logic, and that wall usually appears well after product-market signals do. Do not pay the n8n learning tax before you know which workflows deserve to exist.
Which has more app connectors: Make or n8n?
Make has far more pre-built connectors. Automation Atlas’s Make vs n8n comparison lists 1,800+ for Make vs 400+ for n8n, while eesel AI’s 2026 Make vs n8n guide puts Make at 3,000+. The difference likely comes from counting methods, so treat both as “several times more than n8n.”
- Make: plug-and-play modules for niche SaaS tools, high module quality, big template library.
- n8n: fewer native nodes, but the HTTP Request node handles any REST API with full control over headers and authentication, and you can build custom nodes. The community adds more continuously.
- Key trade-off: Make saves configuration time; n8n removes the ceiling if you can read API docs.
- Winner: Make for niche SaaS coverage without code.
- Confidence: High on direction, low on exact counts.
Is n8n better than Make for AI agents in 2026?
Yes, for technical AI builds. n8n shipped its 2.0 release in December 2025 with native LangChain support and 70+ AI nodes: tool calling, persistent memory in Redis or Postgres, vector database connections for RAG (retrieval-augmented generation), and human-in-the-loop approvals on the canvas. Make launched Maia in early 2026, an assistant that builds scenarios from a natural-language description, with its agent builder still marked beta, according to Crux Digits’ 2026 AI agent verdict on n8n, Make, and Zapier.
- Make: AI features designed for business users; the fastest way to ship an AI-enhanced workflow that a non-engineer can maintain.
- n8n: multi-agent systems, custom logic, memory, and RAG. Peliqan reports the community consensus that “n8n has pulled ahead on AI-native capabilities.”
- Winner: n8n for real agents; Make for “add a GPT step to my CRM flow.”
- Confidence: Medium, because AI features change monthly.
I run AI agents as mini-teams across my ventures, and I insist on human-in-the-loop checkpoints. n8n’s built-in approval steps make that pattern easy to enforce. With Make, you will usually patch it together with email or Slack approvals.
Can you self-host Make or n8n?
Only n8n. HatchWorks’ analysis of the hidden limits of n8n vs Make states it plainly: Make is fully cloud-managed and you cannot host it on your own server, which may be a dealbreaker for private data or strict governance rules. n8n runs on your own infrastructure, including EU servers you choose.
- Make: zero ops effort, vendor handles security patches and scaling. You rely on the vendor’s data processing terms.
- n8n: full data residency control, unlimited executions when self-hosted, but you own updates, backups, and security.
- Winner: n8n for strict data rules; Make for teams with no one to run a server.
- Confidence: High.
What happens when you run out of credits or executions?
This is the trap nobody talks about. Per iimagined.ai, Make’s error reference says running out of credits is a FATAL error that disables the scenario’s scheduling, and n8n’s support article says over-limit executions “are not queued.” In plain words: on both cloud plans, a busy month can silently stop your invoicing, onboarding, or lead routing.
- Winner: Tie on cloud; self-hosted n8n wins because there is no quota.
- What to do: set usage alerts at 70% and 90%, and never run a customer-facing workflow on a plan with less than 2x headroom.
Real-world scenario: which platform wins for a 25-person EU wholesaler?
Scenario
- Company: 25-person Dutch wholesaler (a groothandel, the example Crux Digits uses).
- Team: one ops manager, no in-house developer.
- Budget: under €100/month for automation.
- Goal: automate order confirmations: receive order email, extract line items, check ERP stock, draft confirmation, log to spreadsheet.
- Constraints: 5 steps, about 3,000 orders per month, GDPR applies.
How Make performs
5 steps × 3,000 runs = 15,000 credits/month. At roughly $0.0009 per credit, the raw usage value is about $13.50, so you land on a modestly priced tier above the 10,000-credit entry plan. The ops manager can build this in an afternoon using the email, parser, and Google Sheets modules. The friction appears if stock checks loop over each line item: an order with 8 items can turn one step into 8 credits.
How n8n performs
3,000 runs = 3,000 executions. That is above the 2,500-execution Starter plan, so on n8n Cloud you move to the $50/month tier (10,000 executions). Self-hosted, the software costs nothing, but someone must set up the server, secure credentials, and maintain updates, which this team cannot do in-house.
Scenario verdict
Make wins in this scenario because the workflow is short (5 steps, below the 6, 9 step break-even) and the team has no technical owner. This surprises people who read “n8n is cheaper” headlines. If the workflow grew to 12+ steps with per-line-item loops, or the company hired a technical ops person, n8n would become the better choice within weeks.
What are the real costs and hidden costs of Make vs n8n?
| Cost factor | Make | n8n |
|---|---|---|
| Upfront cost | €0 (free plan, 1,000 ops) | €0 (Community Edition) or Cloud trial |
| Recurring cost | From $9/month; grows with every step and item | From $20/month Cloud; self-hosted = server cost only |
| Setup | Hours, no technical skill | Days; self-hosting needs DevOps basics |
| Training or hiring | Minimal | Technical owner needed (in-house or freelance) |
| Maintenance | Low, vendor-managed | Medium to high when self-hosted |
| Switching cost | Rebuild every scenario; no export to n8n format | Rebuild every workflow; open-source lowers vendor lock-in |
| Opportunity cost | Credit bills creep as workflows grow | Founder hours lost to setup before validation |
| Potential upside | Speed, team independence | Flat costs, AI agents, data control |
A quick total cost of ownership formula: subscription + (setup hours × your hourly value) + (monthly maintenance hours × hourly value) + expected rebuild cost if you switch. If your time is worth €80/hour and self-hosting n8n costs you 3 hours a month in maintenance, that is €240/month of hidden cost, which can wipe out the savings on a small workflow volume. Self-hosting is free like a puppy is free.
Payback period: divide setup cost by monthly savings. If moving a 15-step, high-volume workflow to n8n saves €150/month and the migration costs €900 in freelancer time, you break even in 6 MONTHS. Under that, migrate. Over 12 months, wait.
How can each platform fail you?
Where Make fails
- Credit explosion: iterators and long scenarios multiply credits. Warning sign: monthly bill jumps after adding one loop. Fix: aggregate data before looping, filter early.
- Silent stops: running out of credits disables scheduling. Warning sign: scenario stops firing without an obvious error. Fix: usage alerts plus headroom.
- Spaghetti scenarios: deep branching gets hard to debug. Fix: split into smaller scenarios with webhooks between them.
- Data control limits: no self-hosting. Fix: check vendor data region and DPA before signing enterprise customers.
Where n8n fails
- Single-developer dependency: one person understands the setup and leaves. Fix: document workflows and use sub-workflows with clear names.
- Neglected self-hosted server: missed security updates and no backups. Fix: managed hosting or n8n Cloud until you have ops capacity.
- Dropped executions on Cloud: over-limit runs are not queued. Fix: plan headroom and alerting.
- Slow start: founders spend weeks building infrastructure instead of testing the market. Fix: time-box setup to one week.
Which parts of this decision are reversible?
The choice itself is fairly reversible because neither tool locks your data inside, but every workflow must be rebuilt by hand when you switch. The real lock-in grows with the number of workflows, not with time. Ten workflows is a weekend migration; a hundred workflows is a quarter of someone’s job. Make your call before you cross roughly 20 production workflows.
Which platform fits which founder type?
| Founder or startup type | Better default | Why | Watch out for |
|---|---|---|---|
| Bootstrapped startup | Make, then n8n | Cheapest short workflows, no hosting time | Credit creep as workflows grow |
| VC-backed startup | n8n (with an engineer) | Flat costs at volume, AI agents, data control | Engineering time pulled from product |
| Solopreneur or freelancer | Make | Hours to live, no server | Silent stops when credits run out |
| Non-technical founder | Make | Visual builder, huge connector library | Paying for length; split long scenarios |
| Technical founder | n8n | Code nodes, self-hosting, LangChain | Over-engineering before validation |
| Women-led startup | Depends on team skills, often Make first | Low-risk start without hiring a developer | Structural access gaps to technical talent and capital, not ability |
| EU startup with sensitive data | n8n self-hosted | Full data residency on EU servers | You own security and updates |
| Scaling team | Hybrid | Business teams on Make, heavy flows on n8n | Duplicate spending, unclear ownership |
On the women-led row: my experience running Fe/male Switch tells me women founders are not less technical; they often face fewer warm introductions to technical co-founders and freelancers. As I like to say, “Women do not need more inspiration; they need infrastructure.” A tool that lets you ship without first finding a developer removes one structural barrier. Pick based on your actual team, not stereotypes.
What should European founders consider?
- GDPR (EU-wide): every automation that moves personal data needs a lawful basis and a processor agreement. With Make, the vendor is your processor. With self-hosted n8n, you control where data lives, which simplifies conversations with enterprise buyers.
- EU AI Act (EU-wide): if your n8n or Make agents make decisions about people (hiring, credit, access), document the logic and keep human approval in the loop.
- Currency: n8n Cloud prices appear in euros in several sources; Make often quotes dollars. Watch exchange-rate drift on annual plans.
- Country rules: sector rules (health, finance, public procurement in countries such as Germany or the Netherlands) may require data to stay on national or EU soil. Check before you build.
From my CADChain work on IP and compliance, my principle is simple: “Protection and compliance should be invisible.” Bake data rules into the workflow itself (filters that strip personal data, EU-hosted nodes, logs) instead of hoping every team member remembers the policy.
Can you use Make and n8n together?
Yes, and as a parallel entrepreneur I often recommend it. Here are the patterns that work:
- Sequence: start on Make, move a workflow to n8n when its monthly credit cost passes a set threshold (say €50) or it exceeds 10 steps.
- Segment: marketing and sales own Make scenarios; the technical team owns n8n for data-heavy and AI agent flows.
- Layer: Make handles app triggers, then calls an n8n webhook for heavy processing or AI reasoning.
- Experiment: rebuild your most expensive Make scenario in n8n for two weeks and compare cost and errors.
The coordination cost is real: two bills, two places to debug, two sets of credentials. A hybrid only pays off with clear ownership written down. If nobody can tell you which tool runs your invoicing, you have a problem bigger than pricing.
When should you choose Make?
- Choose Make if non-technical team members will build and maintain workflows.
- Choose Make if most workflows have fewer than 6, 9 steps and modest volume.
- Choose Make if you need a niche SaaS connector without touching APIs.
- Choose Make if you want workflows live TODAY and no server to maintain.
- Avoid Make if contracts or regulation require you to host data yourself, or your workflows loop over hundreds of items per run.
When should you choose n8n?
- Choose n8n if you have a clear technical owner.
- Choose n8n if workflows are long, looping, or run thousands of times per month.
- Choose n8n if you are building AI agents with memory, RAG, or tool calling.
- Choose n8n if data residency and self-hosting matter to your customers.
- Avoid n8n if nobody on the team can maintain a server or debug JSON, and you are still validating whether the workflow is needed at all.
When should you choose both, or neither?
- Both: when business and technical teams have different needs and clear ownership.
- Postpone: when you are automating a process you have done manually fewer than 20 times. Do it by hand first so you know what “correct” looks like.
- Third option: Zapier, with its 8,000, 9,000 app directory and 80+ ready-made agents, if you need the widest app coverage and price is secondary to you. Expect to pay more per task.
- Wrong problem: if your process is broken, automation makes it break faster.
How can you decide in under an hour? The FITS framework
- Fit: Who will own automation? What does your longest workflow look like?
- Investment: How many hours can you spend on setup and monthly maintenance?
- Trade-offs: Are you buying speed (Make) or control (n8n)?
- Switching: At what monthly cost or workflow count will you review the choice?
Score each criterion: weight 1, 5, then rate each tool 1, 5. Multiply and sum. The example weights below are for a non-technical bootstrapped founder; change them for your situation.
| Criterion | Weight (1, 5) | Make score | n8n score |
|---|---|---|---|
| Speed to first workflow | 5 | 5 | 2 |
| Cost at your volume | 4 | 4 | 3 |
| App connectors needed | 4 | 5 | 3 |
| AI agent depth | 2 | 3 | 5 |
| Data control | 2 | 2 | 5 |
| Weighted total | 75 | 54 |
Swap the weights for a technical founder building AI agents (speed 2, AI 5, data control 4) and n8n wins comfortably. Treat the scorecard as a thinking aid, not objective truth.
What does a 30-day validation plan look like?
- Define the job: “Every new lead is enriched and routed to the right salesperson within 5 minutes.”
- Set success criteria: founder hours saved, error rate, monthly cost, time to build.
- Set guardrails: max €50 spend, max 8 hours of setup, no live customer data during the test.
- Run a fair test: build the same workflow on Make’s free plan and n8n Cloud trial (or a self-hosted instance) with identical inputs.
- Record friction: log every error, every documentation search, every credit or execution consumed.
- Decide: pick the tool with the lower total cost per successful run, including your time.
- Set a review date: 90 days, or when monthly automation cost passes €100, whichever comes first.
This mirrors how I design learning in Fe/male Switch: “Education must be experiential and slightly uncomfortable.” Reading comparisons (including this one) will not tell you how your data behaves. Running the same workflow on both tools for two weeks will.
What is the final recommendation?
For entrepreneurs, startup founders, and freelancers, choose Make when your builders are non-technical and your workflows are short. Choose n8n when you have a technical owner and your workflows are long, high-volume, AI-heavy, or bound by data residency rules. If you cannot tell which camp your workflows fall into, run the 30-day test above on your single most important workflow before committing. Reassess when you hire your first technical person, pass 20 production workflows, or see your monthly credit bill cross €100.
For deeper reading, check the Peliqan guide to n8n vs Make architecture and pricing math, the Goodspeed Studio n8n vs Make control and scale review, the LowCode Agency n8n vs Make FAQ, and the G2 user reviews comparing Make and n8n.
Next step: open your current automation tool (or your to-do list of manual tasks), pick your most frequent workflow, and count its steps and the items it loops over. If the number of module actions per run is under 6, start with Make. If it is over 9, start an n8n trial today. It takes 15 minutes, and it will save you a migration later.
FAQ on Make vs n8n Workflow Automation for Startups in 2026
Is n8n actually open source, and does its license matter for my startup?
n8n uses a "fair-code" Sustainable Use License, not a classic OSI open-source license. Internal business use is free. Reselling n8n as a hosted service or embedding it in a commercial product may require a paid license, so read the terms before building client offerings. Check n8n licensing and cost structure for startups
How do I prove an automation is paying off in the first 90 days?
Record baselines before launch: manual hours per week, error rate, response time and conversion. Review results after two to four weeks. Early returns usually come from fewer handoffs and fewer operator mistakes, not full autonomy, so judge Make or n8n on those metrics first. See realistic n8n ROI expectations for founders
Should I commit to an annual Make or n8n plan to save money?
Wait until at least one workflow has proven its value. The automation market is growing fast, which pushes prices down and free tiers up, so monthly billing keeps your leverage. Switch to annual only after three stable months of predictable credit or execution usage. Review AI workflow automation statistics before buying
How do I know my team has outgrown Zapier, and where should it move?
Warning signs include runaway task bills, fragile multi-step Zaps, duplicate records and workflows too critical to fail silently. Move to Make if your team wants visual branching without code. Choose n8n if a developer can own custom logic, self-hosting and approval flows. Spot the signs you have outgrown Zapier
Can I automate emails for free before paying for Make or n8n?
Yes. At low volumes, a Google Sheet, Apps Script and an email provider's API can send personalized sequences at no cost. Use this setup to validate copy and timing first. Move the workflow into Make's free tier or n8n once volume and branching justify it. Join the AI for startups automation workshop
Which workflows should a startup automate first on either platform?
Pick frequent, rule-based tasks with clear success criteria, such as lead intake and routing, invoice reminders, support triage, client onboarding and weekly reporting. Avoid judgment-heavy processes like pricing or hiring decisions. One reliable workflow that saves five hours a week beats ten half-finished scenarios on any platform. Explore AI automations for startups
How do error handling and retries differ between Make and n8n?
Make attaches error-handler routes (Resume, Ignore, Break, Rollback, Commit) to modules and can store incomplete executions for later replay. n8n offers per-node "Retry on Fail" and "Continue on Fail" settings, plus a dedicated Error Trigger workflow for alerts. Configure these before any customer-facing workflow goes live. Compare n8n and Make reliability and error handling
Why does my Make bill grow even when nothing happens?
Scheduled polling triggers use a credit every time they check for new data, even when they find none. A scenario polling every 15 minutes burns about 2,880 credits a month doing nothing. Switch to instant webhook triggers or longer intervals to cut wasted credits. Read the Make credits vs n8n executions breakdown
Do AI steps cost extra on top of Make or n8n pricing?
Usually, yes. When you connect your own OpenAI, Anthropic or other model account, the provider bills per token, separately from your credits or executions. Agents with memory and RAG multiply calls per run. Cap token limits, use smaller models for classification, and log spend per workflow. See the 2026 AI agent verdict for n8n, Make and Zapier
Where can I find unbiased user feedback on Make and n8n support?
Use verified review platforms filtered by company size and role. Focus on complaints about support speed, documentation gaps and billing surprises. Pair the reviews with community forums: n8n's forum is very active, and Make has an official community and partner network. Also ask peers running similar volumes. Read G2 user reviews comparing Make and n8n
People Also Ask:
Why is n8n so popular?
n8n has grown fast because it gives technical teams a free, self-hostable automation tool with few limits. Its per-execution billing keeps costs predictable, since a workflow with 50 steps still counts as one run. Developers like being able to write JavaScript or Python inside workflows, call any API through HTTP nodes, and build AI agents with built-in LLM and LangChain nodes. A large community also shares templates and custom nodes.
What are the main differences between Zapier and n8n?
Zapier is a fully hosted, no-code tool with thousands of app connections and a simple trigger-and-action builder aimed at non-technical users. It bills per task, so each completed action step counts toward your plan. n8n is source-available, can be self-hosted, charges per workflow execution, and supports custom code, branching logic, and AI agent nodes. Zapier wins on ease of use and app coverage, while n8n wins on control, flexibility, and cost at high volume.
Is Make the same as n8n?
No. Both are visual workflow automation platforms that connect apps and automate tasks, but they are built differently. Make is a cloud-only service with a drag-and-drop canvas aimed at non-technical users, and it charges per operation. n8n uses a node-based editor designed for developers, can be self-hosted for free, and charges per full workflow execution on its cloud plans.
Is n8n costly?
Not usually. The self-hosted Community Edition is free, so you only pay for your own server, which can cost as little as a few dollars a month. n8n Cloud plans start at around $20 to $24 per month. Because it charges per workflow execution rather than per step, n8n tends to cost less than Make or Zapier for long, multi-step, or high-volume workflows. The hidden costs come from server upkeep, security, and the developer time needed to run a self-hosted setup.
Is Make or n8n better for beginners?
Make is the easier starting point for most beginners. Its flowchart-style canvas, clear module settings, and 3,000+ ready-made app modules let non-technical users build working scenarios quickly without touching servers or code. n8n has a steeper learning curve and a denser interface, and it rewards users who are comfortable with JSON, APIs, and some JavaScript.
Can you self-host Make?
No. Make is strictly a cloud-hosted SaaS platform running on AWS. You don't manage any servers, which means no maintenance work, but your data stays inside Make's environment. If self-hosting matters for privacy, data rules, or cost, n8n is the better choice, since its Community Edition can run on your own server or local machine.
How does Make pricing compare to n8n pricing?
Make charges per operation (now billed as credits), so every module that runs in a scenario counts against your monthly allowance. Plans start at about $9 per month, which is cheap for simple flows, but loops, frequent schedules, and large arrays can use up operations fast. n8n charges per workflow execution, so an entire run counts once no matter how many steps it contains. Self-hosting n8n removes execution fees completely.
Which is better for AI agents, Make or n8n?
Both handle AI workflows well. n8n is the favorite for custom AI agent building, with dedicated nodes for LLMs, LangChain, vector stores, and RAG (Retrieval-Augmented Generation) pipelines, plus the option to add your own code. Make connects with major AI providers and offers visual AI agents and Make Grid for orchestration, which suits users who want AI features without writing code.
Does n8n have as many app connections as Make?
No. Make offers more than 3,000 pre-built app modules, while n8n has around 500+ native nodes. n8n closes much of that gap with its HTTP Request node, webhook handling, community nodes, and JavaScript or Python code nodes, which let you connect to almost any service that has an API.
Was Make formerly called Integromat?
Yes. Make started out as Integromat, a Czech automation platform. Celonis acquired Integromat in 2020 and rebranded it as Make in 2022, introducing a refreshed visual builder and new pricing. Many comparison articles still use "Make (Integromat)" when referring to the same tool.
