TL;DR
Both are visual, canvas-based automation platforms aimed at builders past the Zapier-simplicity stage. They look similar in the screenshots and diverge sharply once you understand what each one is optimized for.
- Make is a polished cloud product that scales with you on per-operation pricing. Best when you want a great canvas and don’t want to think about infrastructure.
- n8n is source-available and self-hostable, with first-class AI agent nodes. Best when control over hosting, costs, or data is more important than zero-touch operations.
For most solo builders Make is the smoother default in 2026. For technical builders running AI-heavy workflows, n8n self-hosted has a unit-economics argument that’s hard to beat.
How to think about the choice
Both products give you a node graph, branches, iterators, and a visual debugger. The interesting differences are not on the canvas — they’re behind it.
Make is a fully managed cloud product. Pricing is per-operation, scales predictably, and the operator (you) doesn’t worry about infrastructure. The canvas quality is excellent, the integration catalog is broad, and the failure modes are “my Make bill went up” rather than “my server fell over.”
n8n is a source-available platform with two operating modes:
- Cloud-hosted by n8n (~€20/mo entry tier on annual billing — see our n8n tracker)
- Self-hosted on your own infrastructure (Docker, fly.io, Hetzner, etc.) — free for most uses under the Sustainable Use License
The deciding question isn’t “which canvas do I prefer.” It’s: how much do I value infrastructure ownership over zero-touch operations?
Pricing model
This is where the gap is real, especially at high volume.
Make — per-operation, cloud-only
Make charges per operation, with the number of operations included scaling per tier. The model is simple, predictable, and well-suited to mixed workloads — most of your scenarios will run cheap, and high-volume scenarios will be the visible cost driver.
For typical solopreneur workloads (under 10k operations/month), Make’s pricing is modest. For AI-heavy workflows that loop over hundreds of records and call an LLM on each, operation counts can climb fast.
For live pricing: Make tracker.
n8n — flat cloud tiers OR free self-hosted
n8n’s cloud is execution-based with hard caps per tier:
- Starter: ~€20/mo annual, 2.5K workflow executions/month
- Pro: ~€50/mo annual, custom executions
- Business: ~€667/mo annual, ~40K executions/month
- Enterprise: contact sales
The cap shape rewards fewer, denser workflows rather than many small ones. If your workflows each do meaningful work, 2.5K executions/month is a lot. If you’ve written 30 trigger-action automations that each fire 10x/day, you’ll burn through the cap.
Self-hosted is the escape valve: a Docker container on a $5–$20/mo VPS runs n8n fine, and the unit cost per execution becomes “essentially free.” For high-volume workflows, this advantage compounds.
Live pricing for both: Make / n8n.
AI workflow fit
This is where 2025-2026 has separated the two products.
n8n ships AI agent nodes as first-class workflow primitives. A LangChain- style agent with tools, memory, and a structured output schema is a node, not a sequence of HTTP calls and JSON-parsing steps.
Make has improved its AI integrations significantly but still treats LLMs as “specialty modules” rather than first-class agents. You can build comparable workflows on Make, but it takes more nodes and more glue per scenario.
If your automation portfolio is shifting from “trigger → action” to “trigger → agent reasoning → conditional action,” n8n’s primitives reduce the per-workflow authoring effort. If your automations are mostly classical trigger-action chains with the occasional LLM step, Make’s polish wins.
Self-hosting and control
This is the n8n-only advantage that has no Make equivalent.
For a builder whose automations touch customer data, payment information, or proprietary prompts, self-hosting on infrastructure you control is materially different from “data passing through a third-party SaaS.” This isn’t paranoia — it’s a legitimate product-design decision when your customers care about where their data lives, or when you’re approaching audit-relevant volumes.
Self-hosting also removes the platform-pricing failure mode: there is no scenario where n8n’s self-hosted instance starts costing meaningfully more next year. The infrastructure you own scales with the load you put on it; the n8n license doesn’t change underneath you.
The cost: real ops work. Backups, upgrades, occasional troubleshooting. For a technical builder this is a one-evening setup and a few hours per quarter ongoing. For a non-technical operator, this is a non-starter — go with cloud n8n or Make.
Integration catalog
Make has the wider catalog of long-tail SaaS integrations, especially niche vertical tools. n8n’s catalog is broad and growing fast, but for any specific SaaS you should check the integration list before committing.
That said, both platforms have full HTTP-request fallbacks, which means any SaaS with a REST API can be integrated with manual setup work. The integration gap matters most at the “I want this to take 10 minutes, not 2 hours” level.
Debugging and developer experience
Both platforms have visual canvases with execution history, per-step inputs and outputs, and the ability to re-run individual nodes with modified data.
The honest difference:
- Make’s canvas is more polished. The interaction design has had years of refinement and the long-running scenarios stay readable.
- n8n’s canvas is good and improving fast. The recent UI updates have closed most of the gap. Where n8n pulls ahead is when your workflow is heavy on code nodes — n8n’s JS/Python code nodes are a first-class workflow tool, while Make’s code modules feel more like an escape hatch.
For workflows that mix visual nodes with custom logic, n8n’s canvas is more welcoming. For workflows that stay entirely in visual primitives, Make’s canvas edges ahead.
Reliability and ops
Make is operated by a vendor with SLAs and a status page. When something breaks at the platform level, you find out from the status page and wait for them to fix it.
n8n cloud is similar in shape but smaller in operating history. The platform has been reliable in our experience but doesn’t yet have Make’s tenure.
n8n self-hosted is your responsibility. Backups, monitoring, scaling, all of it. The advantage is that “they had an incident” stops being a category of problem and “I had an incident” becomes the only category. Some operators prefer this trade.
When to pick which
Pick Make if:
- You don’t want to think about infrastructure, ever
- Your workflows are mostly classical trigger-action with occasional AI steps
- You value canvas polish and a deeper integration catalog
- You’re already comfortable with per-operation pricing and have no high-volume AI workflows
Pick n8n cloud if:
- You want the same canvas-first developer experience but on EUR pricing or with execution-cap economics that match your workflow shape
- Your workflows are dense (each execution does meaningful work)
- You’re piloting before committing to self-hosting
Pick n8n self-hosted if:
- You’re technical enough to run a Docker container on a small VPS
- You’re running high-volume AI agent workflows where per-execution pricing would compound badly
- Data residency, compliance, or proprietary-prompt protection matters
- You’d rather own infrastructure than rent SaaS pricing
The honest verdict
For the BuildersOS audience:
- Most solo builders should use Make. It’s the path of least resistance with the best canvas-to-cost ratio for typical workflow portfolios.
- Technical solo builders running AI-heavy workflows should seriously consider n8n self-hosted. The unit economics and control compound, and the setup cost is one evening.
There’s no shame in either choice. The escape valve in both directions is reasonable: Make → n8n is a workflow-by-workflow port. n8n → Make is the same pattern in reverse. Pick the one whose default failure mode you’d rather live with — “my Make bill went up” or “I need to update my n8n container.”
You can check Make pricing and n8n pricing on our trackers, including the history of past changes.
This comparison is based on hands-on use of both platforms. AI assistance was used for drafting and proof-reading; editorial decisions and the verdict are human-reviewed. Affiliate links are disclosed where present.