All three tools are workflow automation platforms. You define a trigger — something that happens in one system — and one or more actions that follow in other systems. For example: A form submission triggers a CRM record, which triggers a Slack notification, which triggers an email.  

This is the core of what Zapier, Make, and n8n all do. Where they diverge is in:

• How they do it

• What they cost at scale

• How much AI capability they expose natively

• How much control they give developers who want to go deeper

What It Is

The original and most widely adopted workflow automation tool. Zapier connects over 7,000 applications and has the largest library of pre-built integrations of any platform in this category. If you want to connect two SaaS tools that exist, Zapier almost certainly has the connector.

The interface is deliberately simple — linear, step-by-step, built for non-technical users who want to connect tools without thinking about data structures or execution logic.

AI Capabilities

Zapier has added AI features progressively — an AI action that calls OpenAI's API, a "Zapier Central" AI assistant, and basic prompt-chaining capability within Zaps. These are workable for simple AI steps: classify this text, extract this information from this email, generate a response from this template.

What they aren't is a genuine AI automation environment. The AI steps in Zapier are add-ons to a rules-based infrastructure. The tool was built for linear if-this-then-that logic, and the AI layer sits on top of that rather than being native to the architecture.

Pricing

Free plan covers basic two-step Zaps. Paid plans start at around £15/month but escalate significantly with task volume — Zapier charges per task (each action in a Zap counts as a task), which becomes expensive at volume. A business running high-frequency workflows can find itself spending considerably more than anticipated.

Who It's For

Non-technical users connecting standard SaaS tools with simple automation logic. If you want to connect Typeform to HubSpot to Gmail without thinking about it, Zapier does this better than anything else. If you want to build AI-powered workflows with branching logic, error handling, and meaningful LLM integration, you'll hit the ceiling.

Verdict for AI automation: Adequate for simple AI steps embedded in otherwise standard workflows. Not designed for AI-first automation.

What It Is

Make sits meaningfully above Zapier in workflow complexity while remaining accessible to non-developers. The visual scenario builder — a canvas where you see the entire workflow as a connected diagram rather than a linear list — makes it significantly easier to design and debug complex multi-step automations.

Make handles branching logic, data transformation, error handling, and iterating over arrays in ways that Zapier either can't do or does awkwardly. The mental model is closer to how workflows actually work than Zapier's simplified interface.

AI Capabilities

Make has better AI integration than Zapier — native OpenAI and Anthropic modules, HTTP request flexibility for calling any AI API, and the workflow structure to handle more complex AI logic. You can build multi-step AI workflows where the output of one LLM call feeds into the next, with conditional branching based on AI outputs.

It's still a visual workflow tool with AI added in rather than an AI-native platform, but the ceiling is higher and the architecture handles AI steps more naturally.

Pricing

Make charges per operations (each module execution), not per task. This model is generally more economical than Zapier at volume. Free plan is generous. Paid plans start at around £9/month and scale with operations.

Who It's For

Teams that need more workflow complexity than Zapier provides without wanting to write code. Small businesses with someone technically minded enough to use a visual canvas tool. Anyone running moderate-to-complex automation who finds Zapier's simplicity frustrating.

Verdict for AI automation: Better than Zapier for AI workflows that need conditional logic and multi-step LLM integration. Reaches its limit when workflows need genuine AI reasoning, dynamic tool selection, or agent-like behaviour.

What It Is

n8n is the open-source, code-friendly, AI-native alternative. It shares the visual workflow concept with Make but is built from a different philosophy: maximum flexibility, developer accessibility, and the ability to run complex AI workflows that the other platforms can't support.

Open-source means it can be self-hosted — run it on your own infrastructure, pay no per-operation fees, and modify it however you need. A cloud-hosted version (n8n Cloud) is also available for teams who don't want to manage infrastructure.

AI Capabilities

This is where n8n genuinely differentiates. It was redesigned with AI workflows at the centre rather than added to an existing rules-based architecture. Native nodes for:

LLM chains — connecting multiple LLM calls in sequence, with the output of one feeding the next. The basis of more sophisticated AI reasoning workflows.

AI agents — a dedicated agent node that gives an LLM access to tools (other n8n nodes) and lets it decide which tools to use to accomplish a goal. This is genuine agent behaviour: the LLM reasons about what to do next, not just follows a defined sequence.

Vector store integration — connecting AI workflows to embeddings databases for retrieval-augmented generation (RAG). The foundation of internal knowledge base Q&A, document search, and context-aware AI responses.

Memory — workflows that maintain context across multiple interactions, enabling conversational AI workflows that remember previous exchanges.

Code nodes — JavaScript or Python execution within workflows for logic that visual nodes can't express.

The combination of these capabilities makes n8n the most capable of the three for AI automation that goes beyond simple LLM API calls. An n8n workflow can function as a lightweight AI agent, complete with tool access, memory, and multi-step reasoning — without writing a full agent framework from scratch.

Pricing

Self-hosted: free. The infrastructure cost (a modest VPS) is typically £5–20/month depending on workload. For teams without the capability or appetite to manage infrastructure, n8n Cloud starts at around £18/month.

At volume, n8n's economics are significantly better than Zapier or Make because there are no per-operation charges in the self-hosted model.

Who It's For

Teams with some technical capability — developers or technically minded operations people — who want genuine AI automation rather than AI-flavoured workflow automation. Businesses running AI workflows at volume who can't absorb Zapier's per-task costs. Any team building workflows that need agent-like behaviour, RAG, or persistent memory.

Verdict for AI automation: The strongest of the three for AI-first workflows. Requires more technical involvement to set up and maintain than Zapier or Make, but the ceiling is substantially higher.

Ease of use: Zapier > Make > n8n.  

Zapier is the most accessible to non-technical users. n8n assumes comfort with technical concepts.

AI automation capability: n8n > Make > Zapier.  

n8n is the only one with genuine AI agent architecture. Make handles multi-step LLM workflows. Zapier has AI as an add-on.

Cost at volume: n8n (self-hosted) > Make > Zapier.  

Per-operation pricing in Zapier becomes expensive quickly. Make is more economical. n8n self-hosted removes usage-based costs entirely.

Integration library: Zapier > Make > n8n.  

Zapier's 7,000+ connectors are unmatched. n8n has around 400 native integrations but can call any API via HTTP request, closing much of the gap.

Flexibility and customisation: n8n > Make > Zapier.  

n8n is the only one that supports code execution, self-hosting, and modification of the platform itself.

Support and community: Zapier > n8n > Make.  

Zapier has the largest user community and most extensive documentation. n8n's open-source community is active and growing. Make's community is smaller.

Use Zapier if:  

• Your team is non-technical

• You're connecting standard SaaS tools with simple automation logic

• AI automation is a minor element of an otherwise standard workflow

• Ease of setup matters more than AI capability or cost at volume.

Use Make if:  

• You need more workflow complexity than Zapier handles (branching logic, data transformation, multi-step AI calls) but don't want the technical overhead of n8n.  

• You have someone technically minded enough to use a canvas-based builder.  

• You're running moderate volume where Make's pricing is more economical than Zapier.

Use n8n if:  

• AI automation is the primary use case rather than a feature of otherwise standard workflows.  

• You need agent-like behaviour, RAG, memory, or multi-step LLM reasoning.  

• You're running workflows at volume where per-operation pricing creates cost pressure.  

• You have developer capability to set up and maintain the platform.

All three tools are no-code or low-code platforms with inherent capability ceilings.

n8n gets you further than the others for AI automation, but it's still a visual workflow platform. When your AI automation requirements involve complex custom logic, deeply integrated business systems with no standard API, proprietary data processing, or production-grade reliability requirements that a workflow tool's infrastructure can't meet — custom development becomes the right answer.

The signal is usually familiar: workarounds multiplying, workflows becoming brittle and hard to maintain, the platform's architecture fighting the specific logic the business needs. At that point, building the automation layer in code, designed exactly for the requirements at hand, produces better outcomes than continued platform wrestling.

We work with businesses at that transition point: helping them get more from their existing automation tools where possible, and building custom AI automation where the platforms run out of road.

Tell us what you're trying to automate and we'll tell you whether a platform or a custom build is the right answer.

Octogle Technologies builds custom AI automation for businesses whose requirements have outgrown what workflow platforms handle well — from complex LLM workflows to multi-agent systems integrated with specific business infrastructure. Tell us what you're trying to automate.