n8n vs Make vs Power Automate: Workflow Automation for IT Operations

Three workflow automation platforms keep showing up in IT-operations decisions: n8n, Make, and Microsoft Power Automate. Every vendor pitch says the same thing about flexibility and integration depth. This is the honest comparison after operating all three in production across 12 real scenarios.

None of the three is universally the right answer. They live in different operational worlds. n8n is the platform team's tool — self-hosted, code-friendly, no per-seat licensing, owned by you. Power Automate is the Microsoft 365 estate's native option — best-in-class integration with Entra ID, SharePoint, Teams, Exchange, but with licensing and governance complexity. Make (formerly Integromat) is the citizen-developer's tool — visual-first, friendly, fast to ship, slower to operate at scale.

The decision matters because workflow automation accumulates. Every flow you ship adds operational debt: monitoring it, owning it, upgrading it, retiring it. Picking the wrong platform for the second flow leaves you with sunk cost on the first. This article walks the 12 scenarios we use to size up workflow estates and a clear decision tree that maps scenario shape to platform choice.

The platforms in one sentence each

• n8n — Open-source workflow engine you self-host. JavaScript-friendly. No per-execution fee. Best for technical teams that want full control and Git-native flow definitions.
• Microsoft Power Automate — Microsoft's low-code automation layered over the Power Platform. Cloud Flows (events + APIs) and Desktop Flows (RPA, screen automation). Best when the workflow lives mostly inside the Microsoft 365 + Dynamics estate.
• Make — Hosted visual automation platform with 1,500+ pre-built integrations. Best for fast iteration by non-developers on workflows that span consumer SaaS tools.

The decision dimensions that actually matter

Vendor comparisons usually focus on integration count, "AI features", or pricing tiers. Those are noise. The five dimensions that determine whether the platform survives in your estate at month 18:

1. Operational ownership. Who keeps the flows alive when they break? Self-hosted means your team. Managed means the vendor's SLA — which is usually worse than yours.
2. Versioning + ALM. Can the flows live in Git? Can a change pass code review? Can you roll back? The platforms differ wildly here.
3. Integration depth where it matters. Not "1,500 integrations" — the depth of the integrations you actually use. Power Automate's Entra ID integration is genuinely deep. Make's is shallow.
4. Cost shape at scale. Per-seat (Power Automate Premium), per-execution (Make), per-host (n8n self-hosted) all behave differently at 10x your current volume.
5. Governance surface. Citizen-developer governance (DLP, environment strategy), audit logging, secret management. Each platform has a story; the depth varies.

The 12 scenarios we score against

We have built variants of each of these in production. The scores are real engineering experience, not vendor matrices.

Scenario 01: HRIS-triggered onboarding

New hire in BambooHR / Personio → provision Entra ID account → assign group memberships → enrol device in Intune → send welcome message in Teams → schedule training.

Platform	Fit	Why
Power Automate	★★★★★	Native connectors for Entra ID, Intune, Teams; the integration depth is the win
n8n	★★★★	Works with Microsoft Graph; you build the connectors but they are robust
Make	★★★	Microsoft connectors exist; rate limits and OAuth refresh bite at scale

Scenario 02: Cross-tenant SaaS license reconciliation

Monthly pull from Microsoft 365, Adobe, GitHub, Atlassian → reconcile against HRIS → flag unused licenses → produce a report for finance.

n8n	★★★★★	Long-running, complex transformations in JavaScript; runs to completion on your schedule with no execution-time limit
Make	★★★	Operation count balloons; iterator handling is fine but expensive
Power Automate	★★	Premium connectors required for most SaaS; rate-limit ceiling problematic on large estates

Scenario 03: Ticket triage with LLM classification

New ServiceNow / Jira ticket → LLM classifies into queue + priority → routes accordingly → drafts first response.

n8n	★★★★★	HTTP node + LangChain nodes give clean control over prompts, retries, structured output
Power Automate	★★★★	AI Builder + Copilot connectors work; cost adds up at volume
Make	★★★	OpenAI / Anthropic modules work; less control over retries and JSON parsing

Scenario 04: Slack / Teams ChatOps for deployments

Engineer types /deploy staging in Slack → bot validates permission → triggers CI pipeline → posts status updates → waits for approval emoji before promoting to prod.

n8n	★★★★★	Webhook receiver + long-running flow + state via Redis = perfect fit; full control over the response timing
Power Automate	★★★	Teams-first is fine; Slack integration is shallow; long-running approvals work but feel awkward
Make	★★★	Works but the cost of long-running scenarios with multiple wait states is significant

Scenario 05: Compliance evidence collection

Quarterly audit asks for MFA-enabled snapshots, conditional-access policies, admin-account roster. Walk Entra ID + AWS IAM + Okta, format per template, archive to SharePoint with retention.

Power Automate	★★★★★	Microsoft-native everything; SharePoint output is one node away; audit trail intrinsic
n8n	★★★★	Pulls fine; SharePoint output needs Graph setup but is robust once built
Make	★★	Multi-source aggregation is awkward; the deep AWS / Okta operations need custom modules

Scenario 06: Document approval routing with conditional logic

Sales rep uploads quote → policy engine decides approver based on deal size, region, product → routes for approval → handles re-routes on rejection → notifies CRM on completion.

Power Automate	★★★★★	Approval connector is genuinely good; Dynamics 365 integration carries the rest
n8n	★★★★	You build the approval primitive; great if you have it already, more work if not
Make	★★★	Works for simple approval chains; conditional re-routing gets messy in the visual editor

Scenario 07: Cloud cost anomaly response

AWS Cost Anomaly Detection fires → enrich with cost-attribution tags → identify responsible team → post in their Slack channel → open ticket if not acknowledged in 4 hours.

n8n	★★★★★	Webhook receiver + AWS SDK + time-based escalation = native to the platform
Make	★★★★	Works well; the AWS modules are solid; time-based escalation needs cron-like scheduling
Power Automate	★★	AWS integration is shallow; you end up calling APIs through HTTP and losing the platform's value

Scenario 08: Customer-support email parsing and routing

Email arrives at support@ → LLM extracts customer ID + intent + sentiment → looks up account in CRM → routes ticket to right queue → drafts response from KB.

n8n	★★★★★	IMAP + LLM + RAG retrieval all clean; the platform handles the volume
Make	★★★★	Email parsing modules are fine; visual editor gets cluttered as the flow grows
Power Automate	★★★	Outlook integration is great; AI Builder works; cost adds up with AI Builder credits

Scenario 09: GitHub PR enrichment

New PR opened → Linear ticket pulled in → reviewer suggestions based on file ownership → security scan triggered → results posted as PR comment.

n8n	★★★★★	GitHub webhooks + JS expressions + custom logic; the natural fit
Make	★★★★	GitHub integration is solid; Linear is supported; cost is acceptable at modest volumes
Power Automate	★★	Possible via HTTP; the platform is the wrong shape for developer workflows

Scenario 10: Periodic data sync with deduplication

Pull from CRM every 15 minutes → deduplicate against existing records → upsert to data warehouse → trigger downstream reports.

n8n	★★★★★	Bulk processing with no per-record cost; SQL-style operations against the warehouse
Make	★★	Per-operation pricing makes this scenario expensive at any meaningful volume
Power Automate	★★	Per-flow-execution limits hit before you finish a single dedup pass on real data

Scenario 11: Vendor risk monitoring

Daily check on vendor status pages → cross-reference against your active services → if downtime detected on a critical vendor, post to incident channel + open ticket.

Make	★★★★★	RSS / HTTP modules + visual scheduling = built in 30 minutes
n8n	★★★★	Cleaner code; takes a few hours instead of 30 minutes for the same result
Power Automate	★★★	Possible but the per-flow limits make daily-polling-across-30-vendors awkward

Scenario 12: SaaS account provisioning automation

HRIS event → check role permissions → call vendor SaaS APIs to provision accounts → audit trail in SharePoint.

Power Automate	★★★★★	Premium SaaS connector library + Entra-driven decisioning + native audit
n8n	★★★★	You build the API calls; full control; deeper customisation possible
Make	★★★★	Wide SaaS coverage; you hit OAuth refresh issues on long-tail vendors

Cost shape at scale, beyond the marketing

The honest cost models for a 200-seat IT operations estate with ~50 active workflows:

n8n self-hosted

• VM: €60-€120/month (small VM, Postgres backing store)
• License: €0 (Community Edition) or €50-€100/month (Cloud / Starter)
• Operational engineering: 0.1-0.2 FTE-equivalent
• All-in TCO: €800-€2,500/month

The cost is mostly operational time. The execution cost is effectively zero — run a million workflows or a thousand, the VM cost is the same. This is the lever that makes n8n the winner for high-volume scenarios.

Microsoft Power Automate

• Per-user Premium license: €15.10/user/month — but only for users running Premium flows
• Per-flow plan: €100/month/flow with 5 flow runs/second guarantee
• AI Builder credits: separate budget, €40,000/year for 1M service credits
• Operational engineering: 0.1 FTE for governance (DLP, environment strategy, CoE)
• All-in TCO at 50 flows: €5,000-€15,000/month

The cost shape rewards small numbers of high-value flows running in a tight Microsoft estate. Punishes high-volume polling workflows and cross-stack integrations.

Make

• Operation-based pricing: €9-€29 per 10K operations
• For 50 workflows running modestly, expect 1-5M operations/month
• Operational engineering: 0.05 FTE — vendor handles ops
• All-in TCO at typical volume: €600-€2,500/month

Make wins on TCO at low-to-modest volume because there is no operational burden. Loses at high volume because the operation count grows fast.

Governance surface, compared honestly

Power Platform Center of Excellence (CoE)

Microsoft ships a CoE Starter Kit for free. It is the most complete out-of-the-box governance surface across the three platforms. DLP policies per environment, environment strategy (dev/test/prod), maker analytics, security baseline. Real, useful, and increasingly necessary because Power Platform deployments grow citizen-developer-style and need governance.

The CoE is also a maintenance commitment. Once deployed, someone has to operate it. The CoE itself is a Power Platform workload.

n8n self-hosted governance

Built-in is minimal. The platform has version control via Git export, environment variables, credential vaults. Beyond that you build what you need. The advantage is that all governance lives in your existing engineering disciplines (Git review, secret management, observability stack).

For a platform team that already runs production engineering, this is the cleaner story. For citizen developers, the lack of built-in governance is a real gap.

Make governance

Modest. Team management, scenario visibility, run logs. No environment strategy in the Power Platform sense, no DLP policy engine. You build governance in operational discipline rather than in the platform.

The decision tree

If you can answer the questions below, you have your platform.

1. Does the workflow estate live mostly inside Microsoft 365 / Dynamics / Power Platform? If yes, default to Power Automate unless dimension 2 disqualifies it.
2. Will the workflow estate process more than 1M operations / month in total? If yes, n8n self-hosted wins on cost shape, even against Power Automate inside the Microsoft estate.
3. Does the workflow require long-running state, complex branching, or developer-shaped logic? If yes, n8n. The platform is designed for code-shaped workflows.
4. Is the workflow estate going to be built and operated by citizen developers without dedicated engineering support? If yes, Make or Power Automate Cloud Flows. n8n's developer-first surface is not a fit.
5. Do you need deep Entra ID, SharePoint, Teams, Intune integration? If yes, Power Automate is genuinely better here, and the licensing cost is often worth it.
6. Is data sovereignty / EU-residency a hard constraint? If yes, n8n self-hosted (EU bare-metal). Make's EU residency is partial. Power Automate's EU residency is real for the platform but the connector ecosystem brings in dependencies.

What we actually run, in our own operations

We use all three. The split:

• n8n self-hosted at n8n.itsailor.io for ~80% of our automations. Stripe webhooks → Directus → email; lead enrichment; CRM sync; project tracking; Captain's Log content workflows. Production-grade for things that matter.
• Make for one-off integrations that span consumer SaaS (LinkedIn → CRM, calendar → notification). Cheaper than the operational cost of doing the same in n8n.
• Power Automate in client estates where the work is Microsoft-native. We deploy and govern it; we do not run our own internal operations on it.

The pattern is platform-per-workload, not platform-monopoly. The teams that pick one tool and force everything through it spend the next 18 months working around the tool's shape. The teams that segment by workload type get the right answer for each scenario and accept that they have three flow editors to maintain.

The brutal summary

• Pick n8n self-hosted if you have engineering capacity, want full control, and are running anything resembling production-grade automation. Default for the technical team.
• Pick Power Automate if your workflows live in Microsoft 365 / Dynamics and you have budget for the licensing model. Best fit for Microsoft-heavy estates with a governance practice.
• Pick Make for fast iteration by non-developers on consumer-SaaS-heavy workflows. Best fit for marketing / ops teams that need to ship without engineering.
• Pick more than one if your estate is heterogeneous. Most healthy organisations end up with two of the three running in parallel for different workloads.

If you want the platform decision made for your specific scenarios — we score the workload, we recommend the platform, we ship the first 10 flows — that's the Intelligent Workflow Automation engagement. The discovery call is free and we promise to tell you when your scenarios are wrong-shaped for all three platforms (it happens).