When you're running 3,000 tasks a month, the headline price of an automation platform stops mattering. What matters is what breaks, how long it takes to fix, and what you're actually paying once you account for failed tasks, downtime, and the labour to manage edge cases. We audited Zapier, Make, n8n, and native automation built into a business platform , tracking per-task cost, platform uptime, failure rates, and time-to-support-resolution across a full calendar quarter. The results reshuffled the math. Pricing at 3,000 tasks: Headline vs reality Zapier's public pricing starts at ₹2,400/month for up to 1,000 tasks. At 3,000 tasks, you need the ₹6,800/month plan. Make's £12/month (roughly ₹1,200) covers 1,000 operations, scaling to £48/month (₹4,800) at 4,000 operations. n8n self-hosted costs ₹0 for up to 1,000 executions monthly, but cloud hosting runs ₹34/month (₹2,800) for 5,000 executions. Headline winner: Make at 3,000 tasks. But headlines lie when they don't account for task failures that force rework. A single failed payment sync in your invoicing layer doesn't cost one task—it costs the task, the manual reconciliation (4–8 hours), and the customer escalation. The platform's advertised uptime tells you nothing about failure rates within your own workflows. We tested three real scenarios: a Zapier CRM-to-invoicing sync, a Make e-signature webhook chain, and an n8n multi-step order-processing flow. Each ran 250 workflows over 90 days. Zapier showed 99.8% uptime but 3.2% workflow failure (timeouts, bad field mapping, rate limits). Make hit 99.95% platform uptime but 1.8% failure. n8n on managed cloud delivered 99.9% platform uptime with 0.6% failure, but failures required manual intervention because error logs weren't always clear. The failure tax: When one broken task costs ₹500 A failed payment sync isn't a data point. It's a customer who didn't receive an invoice, a finance team member who has to manually chase it, and a 48–72 hour delay that compounds across your customer base. At 3,000 tasks monthly: Zapier (3.2% failure): ~96 failed tasks/month. Even if only 30% need manual rework, that's 29 tasks that cost labour to fix. At ₹500 per manual intervention (internal time + urgency), that's ₹14,500/month in unplanned work. Make (1.8% failure): ~54 failed tasks/month, ~16 requiring rework. ₹8,000/month labour cost. n8n (0.6% failure): ~18 failed tasks/month, ~5 requiring rework. ₹2,500/month labour cost. True cost of ownership at 3,000 tasks: Zapier: ₹6,800 + ₹14,500 = ₹21,300/month Make: ₹4,800 + ₹8,000 = ₹12,800/month n8n cloud: ₹2,800 + ₹2,500 = ₹5,300/month n8n's true cost is 4× lower than Zapier's, not because of platform price but because fewer things break. Support response time: When a 6-hour outage costs a day When your invoice sync breaks at 9 AM, support response time determines whether it's resolved by noon or becomes a customer-facing incident by 5 PM. We logged tickets with each platform, simulating a broken webhook that silently failed (task ran, no error logged): Zapier: First response in 8 hours (UK working hours). Second response with diagnosis: 22 hours. Root cause: we needed to check Zapier's task history UI, which didn't show field-level errors. Workaround took 18 hours of back-and-forth. Make: First response in 3 hours. Diagnosis in 6 hours. Make's execution history shows field content and mapping, so the support agent saw the bad data in context and pointed us to a validation rule we'd missed. Fix time: 90 minutes after diagnosis. n8n: Community Slack response in 45 minutes (peer support). We found the issue ourselves (missing error handling node), but n8n's workflow editor let us add error handling without platform intervention. No paid support ticket needed. This isn't theoretical. A 22-hour support loop on a payment sync means customers see a broken checkout. At 3,000 monthly tasks, you'll hit a support-level incident roughly once a quarter. Zapier's slow loop compounds across a year. Uptime SLA vs platform reliability: Two different things Zapier and Make both publish 99.9%+ uptime SLAs. Both held those numbers across our 90-day window. But platform uptime ≠ your workflow reliability. Platform uptime = is Zapier's/Make's infrastructure running? Workflow reliability = did your specific workflow succeed, find the right data, and deliver the output you expected? Zapier's 3.2% failure rate against a 99.8% uptime SLA means most failures aren't platform outages. They're timeouts (Zapier's 30-second task limit, vs Make's 300 seconds and n8n's configurable limit), rate limits from third-party APIs, bad field mappings, or silent data mismatches. You can't blame Zapier's infrastructure, but your automation still failed. Make's lower failure rate (1.8%) comes partly from longer timeout windows and partly from better error logging—when a task fails, the reason is usually clear in Make's execution history. n8n's 0.6% failure rate reflects a mature workflow editor (you can add explicit erro