n8n vs Make vs Zapier: A Practitioner's Comparison

Pick the wrong workflow automation tool and you will spend the next two years either paying ten times what you should or fighting a platform that cannot do what you need. The choice between n8n, Make and Zapier is not really about features on a comparison grid. It is about how your team works, what you are willing to operate, and how much logic lives inside the automation versus around it.

This is a working comparison from an agency that has built production systems on all three. No vendor sponsorship, no affiliate links, no pretending one tool wins every category.

The short version

If you are a non-technical team running fewer than 50 simple automations, Zapier is still the right answer. Its catalogue of pre-built integrations is unmatched, the UI is genuinely the easiest, and you will not need an engineer to keep it running.

If you have moderate volume, want visual workflows with branching logic, and are happy on a managed cloud platform, Make (formerly Integromat) gives you more power per pound than Zapier and a more visual canvas than n8n.

If you have a developer or technical operator in the room, want to self-host, run high volume, or need to embed real code into workflows, n8n wins on cost, control and ceiling. It is the default we reach for in agency builds, but it is not the right tool for every team.

The rest of this article unpacks why.

Pricing: where the real differences show up

Pricing is where most teams discover they picked the wrong tool. The three platforms use fundamentally different billing models, and the cheapest sticker price rarely wins at scale.

Zapier charges per task. A task is a single action step inside a Zap. The Professional plan starts around $19.99/month for 750 tasks and scales steeply: 100,000 tasks/month lands in the $800-1,000/month range depending on contract. Multi-step Zaps burn tasks fast - a five-step automation triggered 1,000 times consumes 5,000 tasks, not 1,000.

Make charges per operation, which is roughly equivalent to a task but with more granular metering. Plans start at $9/month for 10,000 operations and scale much more gently than Zapier. At 100,000 operations/month you are looking at roughly $30-40/month on the Pro tier. For comparable workloads, Make is typically 3-5x cheaper than Zapier.

n8n on the cloud plan charges per workflow execution, not per step. One execution runs the entire workflow, regardless of how many nodes it contains. Cloud plans start around €20/month. But the real n8n pricing story is self-hosting: the community edition is free under a fair-use license, and a £10/month VPS will comfortably run hundreds of thousands of executions if you set it up properly.

For a mid-market team running 50,000+ workflow runs per month, the realistic monthly cost difference is roughly £800 on Zapier, £150 on Make, £20-50 on self-hosted n8n including infrastructure. That is not a marginal difference. Over three years it is the cost of a junior hire.

Integrations: breadth versus depth

Zapier wins on raw integration count. The platform claims 7,000+ app integrations, and the long tail is real - obscure SaaS tools that nobody else supports usually have a Zapier connector. If your stack includes niche industry software, Zapier will probably be the only platform that talks to it natively.

Make has roughly 1,800+ integrations and tends to support the popular SaaS tools well, with more flexible HTTP/webhook handling than Zapier when you need to call something custom. The visual data mapping inside Make is materially better than Zapier's for complex transformations.

n8n has around 1,000+ native integrations, but this number understates its real capability. Every n8n workflow can drop into a Code node and execute arbitrary JavaScript or Python, and the HTTP Request node handles any REST API directly with proper auth handling. In practice, the lack of a native integration is rarely a blocker - you write the API call yourself, and it usually takes 10 minutes.

The depth question matters more than breadth. Zapier's integrations are often shallow - a Salesforce action in Zapier covers maybe 20% of what the Salesforce API can actually do. n8n and Make tend to expose more of the underlying API surface, which matters when you are doing anything beyond simple CRUD operations.

Reliability, debugging and observability

Reliability is the dimension that gets ignored in feature comparisons and then dominates your support load six months in.

Zapier is the most reliable of the three for simple, low-volume use cases. Errors are surfaced clearly, the run history is searchable, and the platform handles its own scaling. The weakness is debugging complex multi-step Zaps: when something fails three steps in, the diagnostic experience is not great, and there is no way to replay a single step.

Make has the best visual debugging of the three. You can see data flowing through each module, inspect the payload at every step, and re-run from any point in the scenario. For complex workflows with branching logic, this is genuinely superior. The downside is that Make's error handling on large scenarios can become tangled, and the queue behaviour under load is occasionally surprising.

n8n gives you the most observability if you are willing to set it up. Self-hosted n8n exposes Prometheus metrics, integrates with any logging stack, and stores execution history in Postgres where you can query it directly. Workflow versioning, retry policies, and error workflows are first-class concepts. The cost is that you have to operate it - someone has to monitor the VPS, manage upgrades, and respond when the queue backs up.

A practical rule: if you do not have someone who can SSH into a server and read logs, do not self-host n8n. Use n8n Cloud or pick Make.

AI and LLM features

All three platforms have shipped AI features in the last two years, but the depth varies significantly.

Zapier's AI offering centres on Zapier Central and the AI Actions feature, which lets you call OpenAI from inside Zaps with templated prompts. It is fine for simple use cases - summarising an email, classifying a support ticket, drafting a reply. It is not built for serious agent work or RAG pipelines.

Make has invested in AI modules including direct OpenAI, Anthropic and Mistral connectors, plus a Make AI Agents feature in beta. The visual workflow model maps reasonably well to multi-step LLM chains, and Make is genuinely usable for moderate-complexity AI workflows.

n8n has gone furthest here. The platform ships with a LangChain integration, a dedicated AI Agent node, vector store nodes for Pinecone, Qdrant and pgvector, and proper memory management for conversational workflows. You can build a production RAG pipeline inside n8n that would require custom code on the other platforms. For teams building AI-heavy automations, this is the deciding factor.

That said, none of these platforms is the right place to build a sophisticated multi-agent system. If you are doing real agent orchestration, you write it in Python with LangGraph or similar and use the workflow tool for the integration layer around it.

Self-hosting: the n8n trump card

n8n is the only one of the three that offers genuine self-hosting. This matters for three reasons.

First, data residency. Under UK GDPR, processing customer data through a US-hosted automation platform raises lawful basis and international transfer questions that your DPO will not enjoy. Self-hosted n8n on a UK or EU VPS sidesteps the entire conversation. For regulated sectors (financial services, healthcare, legal), this is often the only acceptable option. The ICO's guidance on international transfers is worth reading before you commit to a US-based SaaS platform for sensitive workflows.

Second, cost at scale. As covered above, the gap between Zapier's per-task pricing and self-hosted n8n is roughly two orders of magnitude at high volume. For teams running tens of thousands of executions per day, this is not optional.

Third, extensibility. Self-hosted n8n lets you install custom community nodes, run alongside your existing Postgres database, mount internal network resources, and integrate with on-premise systems that public SaaS platforms cannot reach.

The honest counter: self-hosting is operational work. You need someone who can run a Linux server, manage backups, handle upgrades, and respond to incidents. For teams without that capability, n8n Cloud removes the operational burden but eliminates most of the cost advantage.

Vendor lock-in and exit costs

This is the dimension most teams ignore until they need to migrate.

Zapier's lock-in is moderate. Your workflows live in Zapier's format and cannot be exported in a meaningful way. Migrating 200 Zaps to another platform is a months-long project. The integrations themselves are typically replicable, but you rebuild every workflow by hand.

Make has similar lock-in. Scenarios export as JSON blueprints, which helps with backup but not with migration to a different platform.

n8n's workflows are stored as JSON in your own database (if self-hosted) and can be exported, version-controlled in Git, and moved between instances trivially. Because n8n is open source under a fair-code license, you also retain the option to fork and maintain it yourself if the company changed direction. This is the strongest exit position of the three.

When each tool is the right answer

Pick Zapier if: you are a small team without engineering resource, you need integrations with obscure SaaS tools, your automations are mostly simple two-to-three-step flows, and your monthly task volume is under 10,000. Marketing teams, sales ops at small companies, founders running their own ops - Zapier is genuinely the right call.

Pick Make if: you want visual workflows with serious branching logic, you have moderate volume (10,000-200,000 operations/month), you want lower cost than Zapier without operating infrastructure, and your team includes someone reasonably technical who can debug data mappings. Operations and revenue ops teams at scale-ups land here often.

Pick n8n if: you have a developer, technical operator or agency in the loop; you need self-hosting for compliance or cost reasons; you are building AI-heavy workflows; you want workflows in version control; or you are running serious volume. This is the default for mid-market companies with real engineering capability and the default we recommend for most agency-built systems.

One more pattern worth naming: many mature teams end up running two of these in parallel. Zapier for the long-tail integrations nobody else supports, n8n for the high-volume core workflows. Make in the middle for visual workflows owned by non-engineering teams. There is no rule that says you must pick one.

Frequently asked questions

Is n8n actually free if I self-host?

Yes, with caveats. The n8n community edition is free to use under the Sustainable Use License, which allows internal business use including running automations for your own company or clients. You pay for the infrastructure - typically £10-30/month for a VPS that can handle most mid-market workloads. The license restricts you from offering n8n itself as a hosted service to third parties, which only matters if you are building a competing SaaS product. For agency or in-house use, the community edition covers everything most teams need.

How hard is it to migrate from Zapier to n8n or Make?

Harder than vendors suggest. There is no clean automated migration - each workflow needs to be rebuilt by hand in the new platform. For a portfolio of 20-50 active Zaps, expect 3-6 weeks of focused work to rebuild and test. The migration is usually worth it at scale (cost savings pay back in 2-4 months at higher volumes) but it is a real project, not a weekend job. Most teams migrate incrementally, moving the highest-volume workflows first and leaving low-traffic Zaps in place until natural attrition.

Which is most secure for handling customer data under UK GDPR?

Self-hosted n8n on UK or EU infrastructure gives you the cleanest GDPR position because you control where data is processed and stored, and no third-party processor handles the data in transit. Zapier and Make both rely on US-based infrastructure for most workloads, which means international data transfer assessments and reliance on adequacy mechanisms or SCCs. None of the three is inherently non-compliant, but the documentation burden is materially lower with self-hosted n8n. For regulated sectors, this often tips the decision regardless of other factors.

Can these tools replace a developer building custom integrations?

For 70-80% of integration work, yes. Anything involving standard SaaS APIs, webhook handling, data transformation, and scheduled jobs can be built faster in n8n, Make or Zapier than in custom code. Where they fall short: high-throughput real-time processing (above ~100 events/second), complex business logic that needs proper testing, integrations with legacy on-premise systems with unusual protocols, and anything requiring serious compliance audit trails. The right pattern for most mid-market teams is a workflow platform for 80% of work and custom code for the 20% that genuinely needs it.

How long does a typical workflow automation project take to build?

For a single workflow connecting two or three systems with moderate logic, expect 1-3 days of build time including testing. For a full operations automation programme covering 15-30 workflows across a mid-market business, plan for 8-16 weeks from kickoff to production, including discovery, build, testing, and a stabilisation period. The discovery phase is where most projects under-invest - getting the process map right before building saves weeks of rework. Most of our agency builds run on this timeline regardless of which platform we use.

What happens if n8n the company gets acquired or shuts down?

This is the strongest argument for n8n over the alternatives. Because n8n is open source and you can self-host, an acquisition or shutdown does not take your workflows offline. Your existing instance keeps running, your workflows remain in your database, and the community could fork the project if needed. With Zapier or Make, a fundamental platform change or shutdown means you lose access to your workflows. This is a low-probability risk for all three companies today, but it is a real consideration for systems you expect to run for five to ten years.

Do I need a specialist agency to set this up, or can my in-house team do it?

In-house teams can absolutely build on any of these platforms - that is the point of them. The case for an agency is speed to a working system, avoiding architectural mistakes that cost months later, and access to patterns that have been tested across multiple builds. Common in-house mistakes include underestimating error handling and observability, building workflows that work for the happy path but break under real-world data quality, and not setting up proper version control or environments. If your team has time to learn through iteration, in-house works fine. If you need production-grade systems in 8-12 weeks, an agency build is usually faster.

Which platform is best for AI agent workflows specifically?

n8n by a meaningful margin, today. The AI Agent node, native LangChain integration, vector store support, and memory management are all production-ready and ship in the open-source version. Make is catching up with its AI Agents feature but it is still in beta and less flexible. Zapier's AI offering is fine for simple LLM calls embedded in workflows but is not designed for agent orchestration. That said, if you are building serious multi-agent systems with complex routing and tool use, none of these platforms is the right primary tool - you write that in Python with a framework like LangGraph and use the workflow tool around it for integrations.

Conclusion

There is no universal winner between n8n, Make and Zapier. Zapier remains the right call for non-technical teams and obscure integrations. Make sits in the middle as the visual-workflow sweet spot for technical-adjacent operations teams. n8n is the most powerful, most flexible, and cheapest at scale - if you have the technical capability to operate it. Most mid-market teams we work with end up on n8n for the core, with Zapier kept around for the long tail.

The decision usually comes down to who is going to own the system after launch, not which platform has the longest feature list. Pick the tool that matches your team's actual operating model, not the one with the prettiest landing page.

If you want a second opinion on which platform fits your specific stack, or you need a build partner to get a production system live in 8-12 weeks, AI Advisory builds and operates workflow automation systems across all three platforms for UK mid-market businesses.

Ready to put this into production? book a discovery call.