Workflow Automation Platforms
n8n vs Make: which is better for self-hosting + Git version control + dev/stage/prod environments?
7 min read
When teams ask this question, they’re usually not comparing “automation tools in general.” They’re deciding where to run real production workflows: self-hosted, version-controlled, and deployed across dev/stage/prod with guardrails that don’t fall apart at scale. From that angle, n8n and Make aren’t symmetrical. Make is primarily a hosted, visual automation tool; n8n is designed to run like infrastructure—with self-hosting, Git, and environments treated as first-class concerns.
Quick Answer: If you care about self-hosting, Git-based workflow version control, and clean dev/stage/prod environments, n8n is the better fit. Make is strong as a hosted “automation SaaS,” but it doesn’t match n8n’s depth on self-managed deployments, Git-native workflows, or environment separation for serious engineering teams.
Quick Answer: n8n is better suited for teams that need self-hosting, Git version control, and multiple environments (dev/stage/prod) because it’s built as an automation platform you can run like infrastructure, not just a cloud SaaS.
Frequently Asked Questions
Is n8n or Make better if I want full self-hosting and infrastructure control?
Short Answer: n8n. It’s built for self-hosted deployments (Docker, Kubernetes, on-prem) with full control over data, dependencies, and scaling; Make is primarily a cloud-hosted SaaS with limited self-managed options.
Expanded Explanation:
n8n treats self-hosting as a core deployment model, not an afterthought. You can run it on your own servers, in your preferred cloud, or fully on-prem in restricted environments. That means you control data residency, security posture, scaling, and how n8n integrates with the rest of your stack (monitoring, logging, secret management).
Make is optimized for teams who are happy to live in a multi-tenant SaaS and don’t need deep infrastructure control. It’s great for quick cloud-based automations, but if you’re in a regulated industry, need strict network boundaries (no outbound internet, private APIs only), or want to wire automation into your platform engineering stack, Make is limiting compared to n8n.
Key Takeaways:
- n8n supports full self-hosting for maximum control over data, security, and infrastructure.
- Make is primarily a hosted SaaS; good for convenience, but less flexible for strict or complex environments.
How do n8n and Make handle Git version control for workflows?
Short Answer: n8n offers Git-based version control with workflow diffs as a first-class feature; Make doesn’t provide Git-native workflow versioning in the same way, so you can’t manage automations like code in your standard DevOps toolchain.
Expanded Explanation:
In n8n, you can manage workflows using Git, just like application code. That means you can:
- Store workflows in a Git repo
- Use branches for features and fixes
- Review changes via pull requests
- Inspect workflow diffs before merging
- Promote changes across environments in a traceable way
This is crucial when automation starts touching production data or security-sensitive paths. You don’t want “click-edits-in-prod”; you want a reviewable change history.
Make, by contrast, is centered around in-app editing and its own internal revision history. That’s fine for lightweight use, but it doesn’t plug into your existing Git workflows, code review culture, or CI/CD pipelines the way n8n does. You lose the ability to treat automation as code with the same rigor as the rest of your stack.
Steps:
- In n8n, connect your workflows to a Git repository (via n8n’s Git integration).
- Use branches to make and test changes to workflows without touching production.
- Open pull requests, review diffs, merge, and then deploy to the right environment (dev/stage/prod).
How do n8n and Make compare for dev/stage/prod environments and promotion?
Short Answer: n8n is built for clearly separated environments (dev/stage/prod) and promotion workflows; Make mainly assumes a “single live canvas” experience, with far less structure for environment-based deployment.
Expanded Explanation:
n8n acknowledges that production workflows need a lifecycle: experiment in dev, validate in stage, then roll out to prod. You can:
- Run multiple instances (dev, stage, prod)
- Wire each instance to its own environment-specific secrets and infrastructure
- Use Git to move workflow definitions across instances
- Keep clear boundaries so experiments don’t accidentally hit production systems
On n8n Cloud, you get multiple instances (e.g., dev and prod) out of the box; self-hosted setups can be as granular as you need. With environments and Git combined, n8n supports a real promotion model instead of “copy and hope.”
Make’s environment story is closer to simple “scenarios” that you turn on and off. You can clone them, sure, but there’s no opinionated dev/stage/prod separation or Git-backed promotion path. That’s workable for small teams or one-off automations, but it’s fragile once multiple engineers need to make changes safely.
Comparison Snapshot:
- Option A: n8n
- Environments (dev/stage/prod) via separate instances.
- Git-based workflow version control and diffs.
- Promotion path that mirrors your application deployment process.
- Option B: Make
- Primarily single-environment SaaS with scenario-level duplication.
- Internal revision history instead of Git.
- Manual, click-heavy “promotion” by copy/adjust.
- Best for:
- n8n: Engineering teams treating automation as part of their platform, with clear environments and reviews.
- Make: Smaller teams or business users who want quick cloud automations without infrastructure ambitions.
How would I actually implement n8n with self-hosting, Git, and dev/stage/prod?
Short Answer: You’d run n8n in multiple environments (e.g., dev and prod instances), connect workflows to Git for version control, and promote changes between instances via branches and pull requests.
Expanded Explanation:
A typical setup that I’ve seen work well in production looks like this:
- Self-host n8n using Docker or Kubernetes so it fits cleanly into your infra.
- Define environments as separate n8n instances (dev, stage, prod), each with its own database, secrets, and triggers.
- Use Git as the source of truth for workflows, with branches that map to work-in-progress vs. stable.
- Automate promotion: merge to a “stage” or “prod” branch triggers deployment of workflow definitions into the right n8n instance.
- Combine with governance: SSO (SAML/LDAP), RBAC, audit logs, log streaming to SIEM, and encrypted secrets so you can answer “who changed what, when, and why” on any workflow.
Once this is in place, your automation strategy looks like any other engineering practice: code review, diffs, environments, logs, and observability—not “click something in the UI and hope nothing breaks.”
What You Need:
- A self-hosted or multi-instance n8n setup (e.g., Docker/Kubernetes for dev/stage/prod, or n8n Cloud with multiple instances).
- A Git repository and branching strategy for workflow definitions, plus basic CI/CD to sync workflows to the correct n8n environment.
Strategically, when does n8n make more sense than Make for long-term automation?
Short Answer: If you expect automation to become part of your core platform—with AI steps, security or finance flows, and multiple engineers touching workflows—n8n is the more sustainable choice because it supports self-hosting, Git, environments, and enterprise governance from the start.
Expanded Explanation:
Make is excellent when you’re early on the curve: a few scenarios, mostly SaaS-to-SaaS, owned by one or two power users. But as soon as workflows touch critical systems (IDP, ticketing, customer billing, AI-driven decisioning), the lack of self-hosting, Git, and environment structure becomes a liability.
n8n is built for that “next phase”:
- Hybrid building (UI + code): Visual canvas when it’s fast, JavaScript/Python when you need last-mile logic.
- Custom integrations: Pre-built nodes where they exist; HTTP Request node and custom nodes where they don’t.
- Operational rigor: Step-level reruns, visible inputs/outputs on every node, workflow history, execution search, error handling, and retries—so you can debug without guesswork.
- AI with guardrails: LLMs are just another node in your workflow—with evaluations, human-in-the-loop steps, and error workflows to catch failures before customers do.
- Enterprise controls: SSO (SAML/LDAP), RBAC, audit logs, log streaming, encrypted secrets, environments, and Git version control.
If you’re aiming for measurable impact—like Huel saving 1,000 hours of manual work or Vodafone saving £2.2M on security automation—you’ll want automation that fits into your engineering system, not a separate, opaque tool.
Why It Matters:
- Risk and reliability: With n8n, you can re-run single steps, inspect inputs/outputs, and audit change history via Git and logs—reducing production incidents from “invisible automations.”
- Scalability over time: Self-hosting, environments, and Git mean you can grow automation usage across teams without losing control, visibility, or compliance posture.
Quick Recap
For self-hosting, Git version control, and clean dev/stage/prod separation, n8n is built to behave like part of your platform stack—not just another SaaS. Make shines for fast, cloud-only automations, but falls short when you need infrastructure control, Git-native workflows, and an environment strategy that looks like the rest of your software delivery lifecycle. If automation is becoming production-critical for your team, n8n’s approach—self-hosting, environments, Git, and enterprise governance—will age better.