We have built production workflows on all three platforms. This is what actually matters when choosing between them for sales and marketing automation, based on real deployment data and cost analysis.
The workflow automation market has grown into a $13.4 billion industry according to Grand View Research, projected to reach $46 billion by 2030 at a 19.4% compound annual growth rate. For go-to-market teams specifically, the choice of automation platform is no longer a minor tooling decision. It determines the ceiling of what your operations team can build, how fast they can iterate, and what the ongoing cost looks like at scale.
Zapier, Make (formerly Integromat), and n8n represent three fundamentally different philosophies for solving the same problem. Each has clear strengths and equally clear tradeoffs. The right answer depends entirely on your team's technical maturity, budget model, volume requirements, and whether AI workflows are central to your roadmap.
Below is a direct comparison across the dimensions that matter most for sales and marketing teams. All pricing reflects current published rates as of early 2025.
| Dimension | Zapier | Make | n8n |
|---|---|---|---|
| Starting Price | $19.99/mo (Professional) | $9/mo (Core) | $24/mo (Starter Cloud) |
| Included Volume | 750 tasks/mo | 10,000 ops/mo | 2,500 executions/mo |
| Self-Host Option | No | No | Yes (unlimited runs) |
| Native Integrations | 7,000+ | 1,800+ | 400+ (plus HTTP/API node) |
| AI/LLM Nodes | Limited (ChatGPT action) | OpenAI, HuggingFace modules | 70+ AI nodes, LangChain native |
| Branching Logic | Paths (paid plans) | Full router with filters | If/else, switch, merge, loops |
| Error Handling | Basic retry | Advanced (break, resume, rollback) | Advanced (try/catch, custom logic) |
| Visual Builder | Linear (step-by-step) | Canvas (drag and drop) | Canvas (drag and drop) |
| Source Code | Proprietary | Proprietary | Open source (fair-code) |
| Best For | Simple, fast automations | Complex visual workflows | AI workflows, technical teams |
Zapier pioneered the category and still commands the largest user base with over 2.2 million businesses according to their published figures. The platform's strength is accessibility. Anyone can build a Zap in minutes without technical background, and the library of 7,000+ pre-built integrations means that the connector you need almost certainly exists already.
The tradeoff is architectural. Zapier uses a linear, step-by-step model where each Zap follows a single trigger through a sequence of actions. For straightforward automations like sending a Slack notification when a HubSpot deal closes, this is perfect. For workflows that require branching, loops, data transformation, or conditional routing, the linear model becomes a constraint. You end up building multiple Zaps to handle what other platforms do in a single workflow.
Pricing reality: Zapier's task-based pricing can scale quickly. A workflow that enriches leads through Clay, scores them, routes them to reps, and updates the CRM might consume 4 to 6 tasks per lead. At 100 leads per day, that is 12,000 to 18,000 tasks per month. The Professional plan at 750 tasks will not be sufficient, and the Team plan at 2,000 tasks still falls short. You are looking at the Company plan or custom pricing, which starts at $69.50/mo for 2,000 tasks and climbs from there.
Make (rebranded from Integromat in 2022) offers the most intuitive visual canvas for building complex, multi-branch workflows. Where Zapier constrains you to a linear sequence, Make lets you build workflows that split, merge, loop, and handle errors with granular control. The visual approach makes it possible to understand a complex 30-step automation at a glance.
The operation-based pricing model is significantly more generous than Zapier's task model. At $9 per month for 10,000 operations, Make offers roughly 13 times more volume per dollar than Zapier's entry tier. For marketing teams running campaign automations, lead enrichment flows, or multi-channel sequences, this cost advantage compounds quickly.
Where Make excels: Complex routing logic, data transformation with built-in functions, scenario scheduling with precise control, and robust error handling with break/resume/rollback patterns. The platform has also added respectable AI modules for OpenAI and other LLM providers, though they remain secondary to its core workflow capabilities.
The limitation: Make's integration library at 1,800+ connectors is substantial but still less than one-third of Zapier's catalog. For niche SaaS tools, you may need to use the generic HTTP/webhook module instead of a native connector, which adds development time.
n8n occupies a fundamentally different position in the market. It is an open-source, fair-code licensed platform that can run entirely on your own infrastructure with no per-execution limits. This architectural difference changes the economics of automation entirely.
Self-hosting n8n on a cloud server (AWS, DigitalOcean, Railway) typically costs between $5 and $20 per month for the infrastructure, regardless of how many workflows you run. For teams processing thousands of executions daily, this can represent a 90%+ cost reduction compared to equivalent Zapier or Make plans.
Where n8n truly separates itself is in AI capabilities. The platform ships with over 70 dedicated AI nodes including native LangChain integration, support for every major LLM provider (OpenAI, Anthropic, Google, Mistral, Ollama for local models), vector store connections, memory management, and agent-building tools. This makes n8n the clear choice for teams building AI-powered GTM workflows, such as automated research agents, intelligent lead scoring, or content generation pipelines.
The tradeoff: n8n has a steeper learning curve than both Zapier and Make. The integration library at 400+ nodes is the smallest of the three (though the HTTP Request node can connect to any API). Self-hosting requires basic DevOps knowledge, and while the cloud offering removes that complexity, its pricing at $24/month for 2,500 executions is not the cheapest entry point.
Most comparison articles quote entry-level prices, which is misleading. Revenue teams running real automation workloads process thousands to tens of thousands of operations monthly. Here is what each platform costs at three volume tiers based on current published pricing.
| Monthly Volume | Zapier | Make | n8n (Cloud) | n8n (Self-Hosted) |
|---|---|---|---|---|
| 5,000 ops/mo | $49.00 | $9.00 | $50.00 | $10 to $15 |
| 25,000 ops/mo | $299.00 | $29.00 | $120.00 | $10 to $15 |
| 100,000 ops/mo | $599.00+ | $99.00 | Custom | $15 to $25 |
How execution counting differs: These platforms count volume differently, which makes direct comparison complicated. Zapier counts each step in a Zap as a "task." Make counts each module execution as an "operation." n8n counts each workflow run as an "execution" regardless of how many nodes it contains. A 10-step workflow counts as 10 tasks in Zapier, 10 operations in Make, but only 1 execution in n8n. This difference massively favors n8n's cost structure for complex workflows.
As AI becomes central to GTM operations, the ability to build AI-native workflows is increasingly the deciding factor. Here is how each platform handles the most common AI use cases for revenue teams.
All three platforms can call the OpenAI API to analyze lead data. The difference is in how they do it. Zapier's ChatGPT action is straightforward but limited to a single prompt/response interaction. Make's OpenAI module supports more model parameters and chained calls. n8n's AI Agent node can chain multiple LLM calls with memory, use tools (web search, database queries), and implement reasoning loops that adapt based on intermediate results.
This is where the gap widens significantly. Building a workflow that retrieves relevant documents from a vector database, injects them as context for an LLM, and generates a tailored output (such as a personalized sales proposal) requires vector store integration. n8n ships native nodes for Pinecone, Qdrant, Supabase Vector, and Weaviate. Zapier and Make require custom HTTP calls to achieve the same result, adding significant complexity.
n8n's LangChain integration enables building autonomous agents directly within the workflow canvas. These agents can decide which tools to use, execute multi-step reasoning, and handle ambiguous inputs. This capability is unique to n8n among the three platforms and directly relevant for use cases like automated competitive research, deal analysis, and intelligent customer response drafting.
Clay's position in the AI stack: It is worth noting that Clay operates as an intelligence layer that sits above these workflow platforms. Clay's waterfall enrichment pulls from 75+ data providers and its built-in AI features (Claygent, AI-generated columns) handle the research and enrichment that workflow platforms then distribute to downstream systems. The most effective stacks we see pair Clay for enrichment with n8n or Make for orchestration and HubSpot or Salesforce as the system of record.
The raw connector count tells only part of the story. What matters is whether the specific integrations your GTM stack requires are available natively and how well they are maintained.
| Platform Category | Zapier | Make | n8n |
|---|---|---|---|
| CRM (HubSpot, Salesforce) | Native, deep | Native, deep | Native, deep |
| Email (Gmail, Outlook) | Native | Native | Native |
| Enrichment (Clay, ZoomInfo) | Native (Clay via webhook) | Native (Clay via webhook) | HTTP node / webhook |
| Outreach (Apollo, Lemlist, Instantly) | Native for most | Native for Apollo, Lemlist | HTTP node, community nodes |
| Analytics (GA4, Mixpanel) | Native | Native | Community nodes, HTTP |
| AI/LLM Providers | ChatGPT only | OpenAI, HuggingFace | OpenAI, Anthropic, Google, Mistral, Ollama, Groq |
| Conversational Intel (Gong, Chorus) | Native | HTTP module | HTTP node |
| Databases (Postgres, MySQL, Mongo) | Limited | Native for major DBs | Native for major DBs |
For sales and marketing stacks specifically, all three platforms cover the core integrations well. The difference appears at the edges: niche tools, newer APIs, and custom internal systems. Zapier's size advantage means it usually has a connector first. Make and n8n compensate with stronger generic HTTP/API capabilities that let technical teams connect to anything with a REST endpoint.
Your team is non-technical and needs simple, reliable automations running quickly. You are connecting popular SaaS tools with straightforward trigger/action logic. Your monthly volume stays under 2,000 tasks. You value the largest integration ecosystem and do not need complex branching or AI workflows.
Typical use case: A small sales team that needs Slack notifications for new deals, automatic meeting scheduling from form submissions, and basic CRM-to-email sync.
You need complex, multi-branch workflows at a fraction of Zapier's cost. Your team has moderate technical comfort and appreciates a visual canvas for building and debugging automations. You run high-volume operations (lead processing, campaign orchestration, data sync) where per-operation cost matters. You want robust error handling without writing code.
Typical use case: A mid-market marketing ops team running enrichment flows, multi-channel campaign triggers, and CRM data sync across 20,000 to 50,000 operations per month.
AI workflows are central to your automation strategy. You have engineering resources (or a partner) who can manage a self-hosted deployment. You need unlimited execution volume at a fixed infrastructure cost. You want full control over your data and workflow logic, including the ability to run LLMs locally with Ollama. You are building GTM workflows that require chained AI reasoning, vector search, or autonomous agents.
Typical use case: A GTM engineering team building automated lead research agents, AI-powered personalization pipelines, and intelligent routing systems that process tens of thousands of records daily at a predictable cost.
The hybrid approach works well. Many of the most effective operations teams we work with use multiple platforms. Zapier for simple, high-reliability triggers (form submission to CRM, Slack notifications). Make for complex marketing orchestration. n8n for AI-heavy workflows and high-volume data processing. The right answer is rarely a single platform for everything.
We build production automation systems on all three platforms every day. Tell us about your current stack, your volume requirements, and your automation goals. We will recommend the right architecture and build it for you.
Book a Free Consultation →If you are already running automations on one platform and considering a switch, the migration path matters. Workflows do not transfer directly between these platforms. Each uses a proprietary format for its workflow definitions. A migration means rebuilding workflows on the new platform, which can range from straightforward (simple linear Zaps to Make scenarios) to complex (multi-branch Make scenarios to n8n workflows with AI nodes).
The most efficient migration approach is to audit your existing automations first. Identify which workflows are high value, which are low complexity, and which are candidates for retirement. Migrate the high-value workflows first, rebuild them on the new platform with any improvements the new platform enables, and run both in parallel for a validation period before decommissioning the originals.
n8n has a structural advantage here. Because workflows are stored as JSON and the platform is open source, you retain full ownership of your automation logic. If you self-host, your execution logs and workflow data live entirely on your infrastructure. With Zapier and Make, your workflow definitions are stored on their servers, and while both offer export capabilities, you are dependent on their platform's continued availability.
A note on vendor lock-in: The deepest form of lock-in with automation platforms is not the workflow definitions. It is the institutional knowledge about how your processes work that gets encoded into the automations. Document your workflows separately from the platform. Maintain a living operations map that describes what each automation does, why it exists, and what breaks if it stops running. This documentation is what makes platform migrations manageable and is valuable regardless of which platform you use.
We architect and deploy automation systems on n8n, Zapier, and Make for go-to-market teams. We'll match the right platform to your workflows, build it, and manage it.
Book a Free Consultation