Make vs Zapier vs n8n in 2026: Which Should RevOps Pick?

Most RevOps teams pick their automation platform the same way they pick a CRM: whoever got there first wins, and now you’re stuck maintaining 200 Zaps built by a sales ops analyst who left in 2023. In 2026, the gap between these three platforms has widened enough that a wrong choice costs real money and real engineering hours. Make (formerly Integromat), Zapier, and n8n have each staked out very different territory, and the right answer depends almost entirely on whether you’re optimizing for speed, cost, or control.

At a glance

The three platforms, broken down

n8n Top pick

Best for technical RevOps and cost-sensitive scale

Free self-hosted; ~$50/mo Cloud Pro

n8n is where I land technical RevOps teams that care about cost at volume. Self-host it for roughly $20 a month with no per-operation tax, drop into JavaScript Code nodes for any custom logic, and wire Error Trigger workflows for instance-wide alerting. The native app catalog is smaller than Zapier's, but it is closing fast, and self-hosting assumes someone on the team is comfortable with Docker and Postgres.

Highlights

• Self-host for ~$20/mo, no per-op pricing
• First-class AI agent nodes plus JS Code nodes anywhere

Try n8n →

Make

The pragmatic mid-market choice

Free, ~$9/mo entry, ~$160/mo at 200K ops

Make is the sensible middle ground. The visual canvas is approachable for non-engineers, native error routes make it credible in production, and iterators plus aggregators handle the real list logic that Zapier simply cannot. Cost per operation beats Zapier but is not free at high volume, and self-hosting is gated behind enterprise plans, so the bill still climbs as you scale.

Highlights

• Native error routes, iterators, and aggregators
• Visual canvas a non-engineer can run

Try Make →

Zapier

Only justified for simple, low-volume Zaps

Free, $19.99/mo Starter, $800+/mo at high volume

Zapier earns its place only for simple, low-volume automations. It has the largest app catalog and the fastest path to a working Zap, which is why non-technical operators reach for it first. But the per-task pricing tax compounds quickly, error handling is poor for the price, and there is no real iteration or branching. Treat it as a starter tool, not a platform you scale on.

Highlights

• Largest native app catalog
• Fastest path to a working automation

Pricing at scale: where Zapier becomes untenable

Start with the number that eventually forces every RevOps team to have a platform conversation: the monthly invoice.

Zapier’s pricing is task-based, and “tasks” count every individual action step in a Zap. Run a workflow that enriches a contact in Clay, updates Salesforce, sends a Slack alert, and logs to a Google Sheet, that’s four tasks per execution. At 50,000 contact enrichments a month, you’re looking at 200,000+ tasks, which puts you firmly in Zapier’s Professional or Team tier: $800-$1,200/month just for the automation layer, before you’ve paid for Clay, Salesforce, or anything else.

Make counts “operations” similarly but is dramatically cheaper. The same 200,000 operations runs about $160/month on Make’s Teams plan. For pure volume automation, syncing HubSpot contact updates to a data warehouse, routing inbound leads from multiple form sources, Make wins on cost without debate.

n8n’s self-hosted version is free beyond infrastructure costs. Their cloud plan caps at around $50/month for most mid-market RevOps use cases. Run n8n on a $20/month DigitalOcean droplet or a small AWS instance and you’re paying effectively nothing for the orchestration layer. Teams running high-volume Outreach sequence enrollment workflows, Salesforce trigger automations, or nightly data syncs regularly report saving $1,500-$3,000/month after switching from Zapier to self-hosted n8n. That’s not a rounding error, that’s a headcount conversation.

The verdict: Zapier’s per-task model is a tax on success. As your GTM motion grows, costs compound fast. Make is the pragmatic middle ground. n8n is the clear winner if anyone on your team can manage a Docker container, which they can.

Error handling: the thing nobody talks about until production breaks

This is where you see the real maturity gap between platforms. And Zapier’s position here is genuinely embarrassing for a product charging enterprise prices.

You get email notifications when Zaps fail and a basic retry mechanism. No native branching on error types, no dead letter queue, no structured way to route failed records into a Slack alert with the actual payload attached. For RevOps workflows where a failed CRM sync means a lead goes unworked, that’s not a minor inconvenience, it’s a pipeline problem.

Make introduced “Error Handler” routes natively. Attach an error handler to any module and route failures to a fallback path: log to a Google Sheet, ping a Slack channel with the full error and the data that failed, retry with modified parameters. This is table-stakes behavior for production automation. Make has had it for years. The fact that Zapier still hasn’t matched it tells you something about their priorities.

n8n goes further. Every node has configurable error handling, continue on fail, retry on fail with backoff, stop and error, and you can build dedicated Error Trigger workflows that fire whenever any workflow in your instance throws an uncaught exception. RevOps teams building Salesforce-to-HubSpot sync workflows or Apollo-to-Outreach sequence automations use this to create a proper ops alert system. Failed records land in a Notion database or Slack thread with full context. Nothing silently disappears.

The tactic worth stealing: Build a dedicated “Error Handler” workflow in n8n using the Error Trigger node. Extract the workflow name, node name, and failed item data, then post to a #revops-alerts Slack channel. Every production workflow in your instance routes through it automatically. This single setup has prevented more missed follow-ups than any SLA policy I’ve seen.

AI nodes and agent workflows: 2026’s actual differentiator

Every platform has bolted on AI features. They are not equivalent. Not even close.

Zapier has “AI by Zapier”, a basic OpenAI wrapper that handles simple text generation inside a Zap. Summarizing a Gong call transcript before logging it to Salesforce? Fine. But it’s a single node with limited prompt control, no memory, and no ability to make conditional decisions based on model output. It’s a demo feature dressed up as a capability.

Make is genuinely useful here. The OpenAI module plus native HTTP calls to any LLM API means you can call Claude, GPT-4o, or a self-hosted Ollama endpoint, parse structured JSON responses, and branch your scenario based on the output. Building an ICP-scoring step that calls an LLM with enriched company data from Clay and routes leads to different Salesforce queues based on the response is completely viable in Make. That’s real work.

n8n has first-class AI Agent nodes with LangChain integration baked in. Tool-calling agents that access external APIs, maintain session memory, and take multi-step actions based on reasoning, all native. This matters for RevOps because it enables workflows that simple if/then branching can’t touch. An agent reviews a new inbound lead, pulls firmographic data from Clearbit, checks Salesforce for existing contacts at the domain, decides whether to create a net-new account or associate to existing, and enrolls into the right Outreach sequence, one n8n workflow with actual decision-making in the middle of it.

The verdict: n8n’s LangChain nodes are a full year ahead of the competition for anything beyond basic text generation. Make is capable if you’re comfortable with custom HTTP calls. Zapier’s AI story is a product marketing checkbox, and you should treat it as one.

Self-hosting and data control: the compliance angle

For RevOps teams at companies with SOC 2, HIPAA, or a legal team that actually reads the DPAs, data residency matters. Every automation platform routes your CRM data, enrichment payloads, and prospect information through its servers. Where those servers live, and who controls them, is a real question.

Zapier has no self-host option. Full stop. Your data transits their infrastructure, period.

Make offers a self-hosted enterprise option, but it’s priced like enterprise software and gated behind sales conversations. Unless you’re a large enterprise with a dedicated vendor team, you’re on their cloud.

n8n is fully open source under a fair-code license. Run the entire platform on your own infrastructure, AWS, GCP, Azure, or a bare VPS. The operational overhead is real: someone needs to handle updates, monitor uptime, and manage the Postgres database backend. That’s not nothing. But the tradeoff is complete data control, no per-task pricing, and the ability to build custom integrations you’d never get approved on Zapier’s marketplace. For teams running sensitive workflows, anything touching MEDDICC qualification data, compensation models, or healthcare GTM, self-hosted n8n is often the only acceptable answer.

Learning curve: be honest about your team

Here’s the take that will get pushback: Zapier’s ease-of-use advantage is overstated for any team with a real RevOps function.

Yes, a non-technical marketing manager can build a basic “form submit to CRM” workflow in Zapier faster. But if you have a dedicated RevOps hire or a rev tech resource, Make’s visual scenario builder is learnable in a day. n8n’s node-based editor is learnable in a week. The complexity ceiling on Zapier, no real looping, limited inline data transformation, sub-workflows added late and still awkward, means you’ll hit the wall faster than you expect, usually right when a workflow actually matters.

Make’s iterator and aggregator modules handle list-based data transformation that Zapier simply cannot replicate without painful workarounds. n8n’s Code node lets you drop into JavaScript when the visual interface isn’t enough. That covers 95% of edge cases without leaving the platform, and it means your RevOps team stops filing tickets to engineering for one-off data manipulations.

The teams that struggle with n8n are the ones who hand it to a non-technical stakeholder and walk away. Keep it with the RevOps or rev tech team, document your workflows properly, and the learning curve pays for itself inside 60 days.

Final word

If you’re running more than 50,000 operations a month, building AI-assisted lead routing, or working in a regulated industry, the decision is n8n. The pricing math alone makes Zapier indefensible at volume, and Make’s error handling and AI capabilities trail n8n’s roadmap by a meaningful margin. Reserve Zapier for the handful of simple, low-volume automations where speed-to-build genuinely matters more than everything else, and treat every Zap you ship as technical debt from the moment it’s live.