Table of Contents
Make vs n8n for AI Agents: The July 2026 Decision Guide
Pick Make if your team is visual-first, wants a hosted builder with nothing to run, and does not fire AI steps at volume. Pick n8n if you have someone who can run a Docker container, if you live inside an SAP stack, or if your agents run often enough that Make's credit billing on AI steps turns into a monthly bill you did not plan for. That last condition is the one every other comparison skips, and it is the reason a July 2026 guide exists at all: an agent step on Make bills operations plus tokens, so a busy agent loop can drain a $9 Core plan's whole credit pool in under four days (Make credit docs). n8n bills one execution per run regardless of how many AI steps run inside it (n8n pricing). For agent workloads, that difference is the whole decision.
Both tools are good. Our standalone reviews rate n8n 4.5 (n8n review) and Make 4.4 (Make review). Neither is broken. The pick just flips hard on how much your agents run and whether you can host your own container.
What changed since the last comparison
Three facts landed after the spring comparisons, and each one moves the verdict.
n8n shipped a first-party MCP server on April 29, 2026 (n8n blog). Point Claude Desktop, Cursor, or any MCP-aware client at your n8n instance, describe a workflow in plain English, and n8n builds it, validates it, runs a test, and hands back a draft. Make has a MCP server too, but it exposes your existing scenarios to a model as callable actions (Make MCP); it does not build the scenario for you from a prompt. That is a real capability gap for any team already living in Claude Code or Cursor.
SAP took a strategic stake in n8n on May 12, 2026, more than doubling the valuation to $5.2 billion from $2.5 billion and embedding n8n natively inside Joule Studio, SAP's agent-building environment in SAP Business AI Platform (Pulse 2.0). And Make's AI billing got fully documented: an AI step running Make's built-in AI Provider costs one credit per operation plus credits by token count, where a standard non-AI module costs a flat one credit (Make credit docs). The rest of this guide is about what those three changes do to the pick.
Pricing comparison (as of July 2026)
Same money buys very different things here, and the caveat column is where the two models diverge. Prices below are pulled from each vendor's live pricing page on 2026-07-03.
| Tier | Make | n8n | The caveat that matters |
|---|---|---|---|
| Free | $0/mo, 1,000 credits/mo, hosted (Make) | Community Edition, free, self-hosted, unlimited executions (n8n) | Make's free tier is hosted but tiny; n8n's free tier is unlimited but you run the box. |
| Entry paid | Core, $9/mo for 10,000 credits (Make) | Starter, 20 EUR/mo for 2,500 executions (n8n) | Make credits get eaten by AI-step tokens; n8n executions are flat no matter how many AI steps run inside. |
| Mid | Pro, $16/mo for 10,000 credits (Make) | Pro, 50 EUR/mo for 10,000 executions (n8n) | Priority execution on Make Pro; n8n Pro adds concurrent executions and AI Workflow Builder credits. |
| Team / business | Teams, $29/mo for 10,000 credits (Make) | Business, 667 EUR/mo for 40,000 executions (n8n) | n8n Business adds SSO/SAML and Git version control; Make Teams adds shared templates and roles. |
| Enterprise | Custom (Make) | Custom (n8n) | n8n Enterprise runs hosted or self-hosted with an external secret store; Make Enterprise is hosted with overage protection. |
Two numbers reward a second look. n8n's annual billing saves 17%, and teams under 20 people can apply for a Startup plan at 50% off Business (n8n pricing). The Make credit slider runs well past 10,000, so heavy Make users buy up the credit ladder rather than switching plans.
AI agents: the credit math that decides it
Here is the mechanic no spring comparison put on the page. On Make, a plain module (add a row, send an email, fetch a record) costs one credit (Make credit docs). An AI step running Make's built-in AI Provider bills one credit for the operation plus credits by token count. For GPT-5, Make charges one credit per 723 input tokens and one credit per 90 output tokens (Make credit docs). Output tokens are the expensive side, and agents generate a lot of them.
Walk it through three ways.
A solo operator building one content pipeline. Say a nightly agent that drafts a post: one AI step, maybe 3,000 tokens in and 800 out, running once a day. Make Core at $9/mo for 10,000 credits (Make pricing) swallows that without noticing. The credit multiplier is real but it does not bite at this volume. If Make is where you already build, Make Core at $9/mo is the pick here.
Example: one GPT-5 agent step at 3,000 input + 800 output tokens costs roughly 4.2 credits (input) + 8.9 credits (output) + 1 credit (operation), or about 14 credits per run, using Make's published GPT-5 rates of 723 input and 90 output tokens per credit (Make credit docs). Larger contexts cost more: Make charges only 361 input and 60 output tokens per credit on GPT-5 calls above a 272,000-token context.
An ops team running five agents 200 times a day. Now the arithmetic turns. Take a two-step agent loop (a reasoning step and an action step), each modest at the rate above, at roughly 28 credits per loop. Run one such agent 100 times a day and the 10,000-credit Core pool is gone in under four days.
Example: 28 credits per two-step loop x 100 runs/day = 2,800 credits/day, so a single agent exhausts a 10,000-credit Core plan in about 3.6 days. Five agents at 200 runs/day each pushes past 28,000 credits per day, roughly 840,000 credits a month, which is a different pricing conversation entirely. These are illustrative from Make's published per-token credit rates, not a measured bill; your real number scales with token count per call.
A dev shop building agents on demand. Cost is not the deciding factor here; the build workflow is. On n8n you describe an agent to Claude or Cursor and its MCP server assembles the workflow for you, which the next section covers in full. Make cannot do that, so a shop already living in Claude Code leans n8n on capability alone.
n8n's model sidesteps the multiplier: one execution equals one full run from trigger to finish, however many AI nodes fire inside it (n8n pricing). Pro at 50 EUR/mo buys 10,000 executions whether each one calls an LLM once or five times. Cost stays linear with runs, not with tokens.
There is a Make escape hatch worth knowing. Switch the AI step from Make's built-in AI Provider to a custom AI provider connection (your own OpenAI or Anthropic key, BYOK), and the credit cost drops to a fixed one credit per operation; you then pay OpenAI or Anthropic directly for tokens (Make credit docs). To set it up in a Make scenario: open the Run an agent module, click Create a connection under the AI provider field, choose Custom instead of Make AI Provider, and paste your provider API key. From then on, that step bills one credit per run and your token cost lands on your provider dashboard, not your Make credit pool.
BYOK on a paid Make plan turns that 28-credit loop into a 2-credit loop, moving the variable cost to a bill you can watch. If you build agents on Make at any real volume, wire up BYOK on day one. Once your scenario is live, you trigger it like any other Make webhook:
curl -X POST https://hook.make.com/<YOUR_WEBHOOK_ID> \
-H "Content-Type: application/json" \
-d '{"prompt": "summarize today inbox"}'Make's paid plans unlock the custom-connection option.
The MCP server: n8n has one that builds, Make does not
n8n's MCP server is the capability Make cannot match right now. It is public preview, first-party, and it ships in every edition including the free self-hosted Community Edition (n8n review). You enable an instance-level MCP endpoint in n8n settings, add the connection to your MCP client, and describe the workflow you want.
Here is the shape of the Claude Desktop config that points at a self-hosted instance, using the HTTP transport n8n exposes:
{
"mcpServers": {
"n8n": {
"url": "https://your-n8n-instance.example.com/mcp/",
"headers": {
"Authorization": "Bearer <YOUR_N8N_API_TOKEN>"
}
}
}
}Config shape follows the n8n MCP server docs; swap in your instance URL and API token.
Point Cursor or Claude Code at that endpoint and you can say "build a workflow that watches a Google Sheet for new rows, summarizes each with Claude, and posts to Slack," and n8n assembles the nodes, validates them, and runs a test before handing back a draft (n8n review). It is a builder, not a reviewer: public preview means it will happily leave a blank field or a mis-mapped credential for you to finish, so treat generated workflows as first drafts. For a team already paying for Claude Code or Cursor, that subscription now doubles as an n8n workflow builder. Make has no equivalent; its MCP server lets a model trigger scenarios you already built, which is a different job.
The SAP angle (skip this if you are not an SAP shop)
If your company does not run SAP, jump to the next section. Nothing here changes your pick.
For teams inside SAP Business AI Platform, the May 12 deal reframes the whole question. n8n is now embedded natively in Joule Studio, SAP's agent-building environment, as part of a multi-year partnership alongside SAP's investment (Pulse 2.0). Inside Joule Studio, n8n inherits SAP's security and compliance posture from day one and connects SAP systems to the rest of your stack through its 1,000-plus integrations.
What that does to the buying decision: the usual "self-host or buy the cloud plan" tradeoff mostly dissolves. Inside Joule Studio, data residency, GDPR, and sector compliance are handled by SAP's wrapper rather than by n8n's own Business or Enterprise plan. For an ops team already governed by SAP procurement, n8n stops being a tool you weigh against Make and becomes the automation canvas that ships with your ERP stack. Make would be a standalone SaaS you bring in separately, with its own vendor review. The SAP embedding is what turns n8n from a developer favorite into enterprise-sanctioned infrastructure.
Self-hosting: n8n wins, Make cannot play
Make is hosted-only. There is no self-managed Make. n8n's Community Edition is a Docker image with no license fee and no execution cap, and that is the single strongest cost argument in the category. Running it yourself deletes per-execution pricing entirely.
The setup is one command if you want to try it locally:
docker run -it --rm \
--name n8n \
-p 5678:5678 \
-v n8n_data:/home/node/.n8n \
docker.n8n.io/n8nio/n8nThat gives you the full AI Agent node and the MCP server on the free edition. Open http://localhost:5678 and you are building. Confirm the container is healthy before you wire anything to it:
curl -sf http://localhost:5678/healthz && echo "n8n up"For production you want backups, patching, and uptime, which is where managed hosting earns its keep without the license fee.
The cheapest reliable managed path is Cloudways, which puts a 2GB DigitalOcean box at about $11/mo on monthly billing and handles patching and backups so you are not also being a sysadmin (Cloudways pricing). Run n8n on Cloudways and you get the unlimited-execution economics with someone else owning the server layer. Current n8n is on 2.29.1 (release notes, fetched 2026-07-03), with recent releases being maintenance work on top of the 2.0 agent capabilities, so the image you stand up today is current and maintained.
If nobody on your team wants to touch a container, this whole section is moot, and that is a legitimate reason to pick Make. Hosted-with-no-ops is a feature.
The verdict, by team shape
Four common shapes, one pick each, one reason each.
| Your team | Pick | Why |
|---|---|---|
| Non-technical team, wants hosted with no ops | Make | Best visual builder in the category, nothing to run, and the credit multiplier does not bite at low AI volume. Start on Make Free, move to Core at $9/mo (Make pricing). |
| Developer on cloud, comfortable with Docker | n8n | The MCP server builds workflows from a prompt, and self-hosting the free Community Edition deletes per-execution pricing. Run it managed on Cloudways from about $11/mo (Cloudways pricing). |
| Ops team with Docker capacity, agents at volume | n8n | One flat execution per run means agent loops do not multiply your bill by token count the way Make's AI Provider does. |
| Enterprise inside an SAP stack | n8n | Embedded in Joule Studio with SAP's compliance wrapper from day one; it ships with your ERP stack rather than getting evaluated against it. |
What to do tomorrow morning
Agents run rarely and your team wants zero ops? Start on Make, stay on Core until your credit usage says otherwise, and switch AI steps to BYOK the moment you push them at volume. Have Docker capacity or run heavy agent loops? Run n8n's free Community Edition self-hosted, or put it on a managed Cloudways box for about $11/mo (Cloudways pricing) and skip execution pricing entirely. In an SAP shop, the pick is n8n inside Joule Studio and the comparison is already over.
For the deeper single-tool detail, including the full walkthroughs and candid cons, read the n8n review and the Make review. The n8n Community Edition ships free from n8n if you want to stand it up before you decide.