It's 9:47 p.m., your sales director just pinged you about a broken CRM alert, and the marketing team wants AI-generated briefs tomorrow morning. You're the reluctant integration owner-so which automation platform actually bails you out: n8n, Zapier, or Make?
Over the past month our automation lab rebuilt three production workflows on all three platforms-alerting, batch enrichment, and API failover. We timed every build, measured execution costs, and stress-tested error handling so you don't have to re-learn the same painful lessons.
How We Tested
- Scope: CRM-to-Slack alerts, 10k-row data enrichment with GenAI, and PDF OCR fallback pipelines.
- Metrics: Build time, execution runtime, operational cost, and resilience under failure.
- Environment: Zapier Professional account, Make Pro plan, n8n 1.51 self-hosted on a $12/month Hetzner VPS.
- Data sources: Public product documentation plus Topic Wise lab benchmarks logged in our research repository.[^1][^2][^3][^4]
Comparison Table
| Dimension | n8n | Zapier | Make |
|---|---|---|---|
| Integrations | 400+ nodes + custom code[^2] | 8,000+ app directory[^1] | 1,500+ official apps + HTTP modules[^3] |
| Hosting | Self-host or cloud (fair-code) | Fully hosted SaaS | Fully hosted SaaS |
| Best for | Technical teams needing code flexibility | Business teams shipping fast automations | Ops teams orchestrating complex branches |
| Build time (CRM alert) | 28 min (JS Function node) | 22 min (AI suggestions) | 18 min (Scenario router)[^4] |
| 10k record sync runtime | 13m 42s self-hosted | 18m 05s (15-min polling) | 11m 20s (array aggregator)[^4] |
| Error handling | Try/Catch nodes, custom retries | Limited; retries via Paths/Code | Scenario-level error handlers, auto-retry |
| Pricing posture | Self-host cost control | Task-based metering | Operation-based metering |
Platform Deep-Dive
n8n: Control-first Automation for Builders
n8n gives technical teams a visual canvas with optional JavaScript or Python nodes when the drag-and-drop flow isn't enough.[^2] Once you need to parse a custom header, sign an API request, or loop over a weird JSON payload, dropping into code for four lines feels faster than wrestling with a dozen formatter steps. Self-hosting also means you can keep data on your own infrastructure, meet compliance requirements, and scale horizontally without per-task fees.
What surprised us: The built-in AI Workflow Builder shipped in 2025 actually generated 60% of the initial CRM alert, including environment variables. But we still had to add a Function node to clean Slack rich text, which pushed the build time to 28 minutes-longer than Zapier or Make.
Watch out for: You're trading flexibility for maintenance. Expect to provision logging, queueing, and patch cycles. Our Hetzner VPS peaked at 65% CPU with five concurrent runs, so budget time for scaling if you're ingesting spikes.
Zapier: Velocity and Ecosystem
Zapier's pitch hasn't changed: connect everything quickly, now with an AI orchestration layer and 8,000+ integrations out of the box.[^1] The new Canvas view plus AI-triggered mapping shaved about six minutes off the CRM alert build because Zapier suggested the exact HubSpot fields we needed. For exec dashboards or once-a-quarter automations, the speed matters more than infrastructure freedom.
Hidden costs: Task metering adds up fast once you cross into AI-heavy flows. Our 10k-record enrichment ate 1,200 task credits even after we optimized chunking. You're also limited by polling intervals on certain triggers; the data sync paced at 15-minute windows, stretching total runtime to 18 minutes.
When it wins: Zapier is still the fastest way to stitch together SaaS tools for marketing, RevOps, or customer support teams that don't want to touch code. And if you already live inside the Zapier ecosystem (Tables, Interfaces, Canvas), the workflow context carries across.
Make: Scenario Powerhouse for Ops Teams
Make (the artist formerly known as Integromat) leans into a dense scenario builder where every module's input and output is visualized in one place.[^3] Routers, aggregators, and iterators are first-class citizens, so branching and batching feel native rather than bolted on.
During the 10k-record enrichment test Make finished in 11 minutes because we could process records in batches of 100 using the array aggregator, then parallelize OpenAI calls with built-in throttling.[^4] Error handlers also let us auto-resume two failed calls without leaving the platform.
Caveats: People either love or hate the Make UI. It rewards ops specialists who live inside the canvas, but less-technical teams might freeze the first time they see 23 modules on one screen. Pricing counts "operations," so long-running loops can spike usage if you're not careful.
Scenario Findings
1. CRM-to-Slack Alert (Revenue Teams)
You promised the CRO that every six-figure deal would land in Slack with owner, stage, and last activity. Zapier got there fastest: AI-assisted mapping, four steps, done. But when we needed a conditional branch for deals without owners, the Make router handled it natively, while Zapier required an extra Code step. n8n took longer only because we added a Function node to reformat Slack markdown. If your revops trainee has to ship the alert in under 30 minutes, Zapier is a safe default. If you plan to layer on custom approval flows later, Make or n8n give you more headroom.
2. 10k-Row Data Enrichment (Product Ops)
Bulk processing is where the platforms diverged most:
- Make: Array aggregator + auto-retry shaved runtime to 11m 20s with minimal babysitting.
- n8n: 13m 42s after we added Wait nodes to respect OpenAI rate limits; zero incremental cost on self-host.
- Zapier: 18m 05s and 1,200 task credits. The polling interval is the bottleneck unless you move to webhooks.
If you're enriching customer cohorts nightly, Make offers the cleanest combination of speed and safety. Self-hosted n8n wins purely on cost predictability-once you're paying for infrastructure anyway, heavy workloads become effectively free.
3. API Failover with Error Handling (Ops SRE)
We simulated a flaky OCR vendor and required an automatic fallback:
- n8n's Try/Catch node plus inline code delivered zero failed runs after tuning.
- Make's error handlers replayed two failures automatically-no manual reruns needed.
- Zapier escalated four failed tasks; we had to replay them manually because Code steps bubble errors straight to email alerts.
If compliance or uptime is a concern, n8n and Make both offer mature error paths. Zapier is improving, but enterprise incident playbooks still prefer deterministic retries.
Pricing & TCO Snapshot
| Expense Driver | n8n | Zapier | Make |
|---|---|---|---|
| Base cost | VPS $12/mo (self-host) or Cloud plan | Professional plan $73/mo billed annually | Pro plan €36/mo billed annually |
| Variable cost | Infrastructure + AI API usage | Task credits (1k-100k+) | Operations pool (10k-1M+) |
| Overages | Run another container | Buy more tasks or upgrade tier | Buy more ops or throttle |
Numbers reflect October 2025 list prices; exchange rates may vary. Always confirm with current pricing pages before committing.
Decision Framework
| Choose... | If you value... | Trade-offs |
|---|---|---|
| n8n | Compliance, on-prem data, custom logic, budget control | You own infra, monitoring, and scaling. |
| Zapier | Speed, breadth of integrations, business-user autonomy | Task metering + limited deep error handling. |
| Make | Visual orchestration, branching, high-volume ops | Steeper learning curve, operation-based billing. |
Quick Questions to Ask
- Who will maintain the workflow in six months? If it's still you, self-hosting n8n might be worth it. If turnover is high, Zapier's low barrier saves re-training.
- How bursty is your workload? Make's aggregators and error handlers shine with unpredictable spikes; Zapier throttles unless you pay for higher tiers.
- Do you need to touch sensitive data? Self-hosting n8n keeps data off third-party SaaS. Otherwise, ensure Zapier/Make SOC2 paperwork satisfies procurement.
Implementation Tips
- Start your pilot with one high-value workflow per platform. Write down build time, blockers, and duplicate those notes in a knowledge base. When leadership asks "why Make?", you'll have receipts.
- Pair Zapier with AI Presentation Makers guide for prompt templates that accelerate Formatter and OpenAI steps.
- If you commit to n8n, budget week one for observability--hook it into ChatGPT vs Claude comparison-style logging (we used OpenTelemetry Collector).
- For Make, treat routers as guardrails. Label each branch with the business rule ("Deals without owner", "VIP >$50k") so newcomers understand the logic instantly.
The Verdict
Make wins our 2025 benchmark for operations teams that juggle complex branching and bulk data. The canvas is busy, but the time savings and error safety net are real. Zapier still rules for fast-moving go-to-market squads who value velocity and ecosystem depth over deep customization. n8n is the power move when compliance, cost control, or custom logic outweigh convenience-especially if you already have DevOps support.
In practice? Many teams blend all three: Zapier for quick wins, Make for mission-critical operations, and n8n for code-heavy or sensitive flows. Just make sure you document your stack and keep a living playbook so future you isn't the next person staring at a broken alert at 9:47 p.m.
[^1]: "Zapier," Wikipedia, last modified September 30, 2025. https://en.wikipedia.org/wiki/Zapier [^2]: "n8n - Secure Workflow Automation for Technical Teams," n8n GitHub README, updated October 15, 2025. https://raw.githubusercontent.com/n8n-io/n8n/master/README.md [^3]: "Integromat Rebrands as Make and Launches the Platform for the Creator Economy," PR Newswire, January 26, 2022. https://www.prnewswire.com/news-releases/integromat-rebrands-as-make-and-launches-the-platform-for-the-creator-economy-301474238.html [^4]: "Automation Lab Notes - n8n vs Zapier vs Make," Topic Wise Research Archive, October 20, 2025. Internal document available to the editorial team.