This is not a feature comparison. The three tools change monthly and any table of ticks would be out of date by the time you read it. It is a record of which tool we reached for on real client systems, why, and what happened afterwards, because the question we actually get asked is not “which is best” but “which one for this job”.
The one-line version
Make is the fastest way to connect two or three business tools in a flow that a non-technical owner can read on screen. Best for linear scenarios: something arrives, four things happen, done.
n8n is a workflow engine with proper branching, loops, code steps and error handling, which you can run on your own server or in its cloud. Best for anything that runs every day with many branches, real volume, or data you want kept under your own control.
Claude Code is not a workflow tool. It is an agent that works inside a folder of files, follows written instructions and uses connected tools. Best for document-heavy work that needs reading, drafting and judgment, with a person reviewing the output.
Most of our systems use two of the three.
Where each one ran in our work
Everything below is on the Work page; nothing here is new information about a client.
Make runs the booking automation at Avision Digital: a booking email arrives, one AI step reads it, and Make creates the Google Drive folders and share link, opens the job in ClickUp with its tasks and posts to the team’s Slack channel. Make also runs the list engine behind Gizoom’s outreach: a search pulls companies, every address is verified, the clean ones go into Smartlead and get marked as sent. The email side of Automated Investments is Make too.
n8n runs the LinkedIn side of Automated Investments, which picks a different investor group each weekday, has AI write a short note for each person, sends the requests, and checks for replies every two hours so that automation stops the moment anyone answers. It also runs a two-way WhatsApp assistant for a wealth advisory firm, an AI phone receptionist for a hotel spa (with ElevenLabs for the voice and the spa’s booking software underneath), a production assistant that turns plain Slack messages from a film crew into Notion records, and a WhatsApp entry system for a food brand’s promotion.
Claude Code runs Aevolve itself, the content system that finds video ideas and drafts scripts for Avision’s founder, and the weekly LinkedIn drafts for a founder in the US. In each of those, the output is a draft and a person decides.
The pattern is not subtle. Make and n8n carry data and schedules. Claude Code reads, writes and judges.
Make: when it is right, and when it stops being
Make is right when the job is linear, the tools involved are mainstream SaaS products, and the person who will own it wants to be able to open it and understand what it does. The visual scenario is genuinely readable. An operations lead at Avision can look at the booking flow and follow it left to right.
It stops being right in three situations. When the flow needs many branches and exceptions, the canvas becomes a thicket and the readability advantage disappears. When error handling matters, because Make’s tools for it are basic and a failed run in the middle of a flow can leave half a job done. And when volume climbs, because Make charges by operation, so a scenario that polls every few minutes costs far more than one that waits for a trigger, and a big list job can burn through a month’s allowance in a day. Design around triggers, not polling, and watch the operation count.
Make is hosted only. If keeping data on your own infrastructure matters, it is not the tool.
n8n: when it is right
n8n is right when the system has to run every day with real branching: check this, and if that, do these three things, otherwise wait and try again. It is right when you need a code step in the middle, when webhooks from other systems drive the work, when the same workflow has to loop over hundreds of records, and when the credentials and the data should stay on a server you control. The AI nodes are good, which is why the spa receptionist and the WhatsApp assistant live there.
The cost of that power is that someone has to be comfortable with it. The canvas gets dense quickly. Self-hosting means owning uptime, updates and backups; we have seen more automations die from an unapplied update than from any bug. The hosted version removes that burden and is the right default for a team without a technical owner.
Claude Code: the different animal
Claude Code does not belong in the same column as the other two, which is why comparisons that put it there go wrong. It does not run on a schedule as its main mode. It is not deterministic: the same input can produce a different output. It is charged by usage rather than by seat. And it needs a reviewer.
What it does that the other two cannot is work on documents and context. The content system for Avision’s founder reads hundreds of videos that performed, scores ideas against his audience and his offer, and drafts scripts from his brand documents. That is not a flow with branches. It is reading and judgment inside limits, written down as instructions a person can edit. We wrote about where it fits in a business separately.
It also does something unusual: it can build and maintain the other two. A good share of the n8n and Make workflows we ship now are drafted, documented and debugged with Claude Code, and the instruction file for a client’s system includes how its workflows are wired.
How they fit together
The most common shape we build has three layers.
- A workflow tool carries the data and the schedule. Make for a short linear flow, n8n when it branches.
- One AI step, called through an API from inside that workflow, handles the single part that needs reading or writing: the booking email, the two-line note. This is not Claude Code; it is a model call inside the flow.
- Claude Code sits beside the operation, in its own folder, doing the document-heavy work with a reviewer, and keeping the workflows documented.
Avision is layers one and two. Gizoom is layer one alone. The content systems are layer three alone. Nothing we have built needed all three for the same job, and that is a useful test: if a design needs all three for one workflow, the workflow has probably not been written down properly.
Who maintains it afterwards
This decides more projects than any feature.
A Make scenario can be maintained by the owner or an operations lead. Changes are small and visible. An n8n system needs a technical owner on the team, or a partner who checks in, because a broken credential or a stuck execution needs someone who knows where to look. A Claude Code setup splits: what it does is plain text the owner can change, how it is wired is engineering.
Ask who will open the tool in six months when something changes in a connected app. If the answer is nobody, pick the simpler tool or budget for the person.
How each one fails
Make fails quietly when a connected app changes a field name or a permission. The scenario errors, the notification goes to an inbox nobody reads, and three weeks later someone notices the jobs stopped appearing. Turn the error notifications on and route them to a channel people watch.
n8n fails when a server update is skipped, a credential expires or a long loop hangs. Executions pile up or stop. The fix is routine maintenance, which is a person, not a tool.
Claude Code fails by drifting: instructions that were clear six months ago no longer match how the business works, and the drafts slide. It also fails by confidence, producing something plausible and wrong. Both are caught by the same two things, a reviewer and a log, which is why we never ship it without them.
Choosing
| If the job looks like this | Reach for |
|---|---|
| Something arrives, a fixed set of things happen, two or three mainstream tools, the owner wants to read it | Make |
| Runs daily, many branches and exceptions, loops over records, webhooks, data stays on your server | n8n |
| Lives in documents and text, needs reading or drafting or judgment, a person reviews the output | Claude Code |
| A fixed flow with exactly one messy step in it | Make or n8n with one AI call inside |
| You cannot describe the steps yet | None of them. Write the steps down first |
The honest caveats
Pricing is left out on purpose; all three have changed it in the last year and will again. The tools’ own marketing claims are not evidence of anything. And our preference is openly biased toward whatever the client’s team can keep running after we leave, which is why the simplest tool that does the job wins most arguments in our engagements.
If you want the decision made against your actual workflows rather than in general, that is Learn & Recommend: we map how the work runs, mark every step, and the tool choice falls out of the map.