n8n and Make (formerly Integromat) are workflow automation platforms that agency operators use to connect APIs, enrich lead data, and route information between systems. n8n is open-source and code-friendly - built for technical operators who want full programmability and low cost at scale. Make is visual-first with 1,800+ app connectors and a gentler learning curve. For AI agency operators the decision is straightforward: n8n wins on cost and AI depth, Make wins on connector breadth and non-technical usability.
TL;DR: n8n is the better choice for AI agency operators running outbound data pipelines - Clay enrichment, lead routing, CRM sync - because self-hosted n8n is essentially free at any volume, and its native AI/LLM nodes (Claude, OpenAI, Langchain agents) go far deeper than Make's. Make is the better choice when your agency builds client-facing automations across dozens of SaaS apps where connector breadth matters more than cost. Neither tool sends outreach or manages replies - you still need a delivery platform for that.
What Is n8n?
n8n (pronounced "nodemation") is an open-source workflow automation platform. The interface uses a drag-and-drop canvas where you connect nodes: triggers, logic branches, data transformations, HTTP calls, and native integrations. It ships as a Docker image you deploy on your own server or runs on n8n's cloud starting at $20/mo.
What separates n8n from visual-only tools: every node can execute custom JavaScript or Python. Webhooks, HTTP requests, conditional branching, retry logic, and error paths are all first-class. The AI node library covers OpenAI, Anthropic Claude, Langchain agent chains, vector databases (Pinecone, Weaviate, Chroma), tool-calling agents, and embeddings. For a technically inclined operator, n8n sits at the intersection of low-code and real backend programming.
The practical limitation is the connector count. n8n ships with roughly 400 built-in integrations. For any API not on that list, you use an HTTP Request node - which is fine technically, but requires you to handle auth, pagination, and field mapping yourself, whereas Make would have a pre-built module for it.
What Is Make?
Make (formerly Integromat, rebranded in 2022) is a cloud-based visual automation platform with over 1,800 native app connectors. Workflows are called "scenarios" and are built by connecting modules in a linear or branching layout. The UI guides you through each step without requiring any code, which makes Make accessible to non-technical operators and faster to set up for straightforward integrations.
Make has a free tier (1,000 operations/month) and pricing that scales by operation count. For agencies building client automations across popular SaaS stacks - HubSpot, Slack, Airtable, Notion, Gmail, Stripe, Salesforce - Make almost certainly has a native connector for every tool in that stack. The time-to-first-working-automation is lower than n8n for most use cases that do not require code.
The constraint appears at scale and at complexity. Make's pricing charges per operation, not per workflow, so a 10-module scenario processing 5,000 leads costs 50,000 operations. That adds up quickly at agency volumes. And when workflows need custom logic - complex data transformations, branching enrichment waterfalls, AI agent workflows - Make's no-code approach requires creative workarounds where n8n would use a Code node.
Feature-by-Feature Comparison
| Feature | n8n | Make |
|---|---|---|
| Hosting model | Self-hosted (open-source) or cloud | Cloud-only |
| Free tier | Self-hosted: unlimited; Cloud: 5 workflows, 2,500 exec/mo | 1,000 operations/mo |
| Paid entry | $20/mo cloud Starter | $9/mo Core |
| Cost at high volume | Self-hosted server cost ($6-20/mo VPS) | Scales steeply by operations |
| Native integrations | ~400 | 1,800+ |
| Code support | Full JavaScript + Python in Code nodes | Formula-based only |
| AI/LLM nodes | Claude, OpenAI, Langchain, agents, vector DBs, embeddings | OpenAI module + webhook-based |
| Visual UX | Canvas (steeper learning curve) | Linear step-by-step (gentler) |
| Error handling | Advanced branches, retry, error routing | Basic retry + rollback |
| Self-hosting option | Yes (full Docker deployment) | No |
| Multi-client workspaces | None native | Teams plan (basic) |
| Best for | Technical operators, AI pipelines, cost-conscious agencies | Non-technical teams, broad SaaS coverage |
Pricing Breakdown
The pricing models are different enough that a side-by-side dollar comparison requires a scenario assumption. Take an agency processing 50,000 lead records per month through an 8-step workflow:
n8n pricing at 50K records, 8 steps
- Self-hosted: server cost only - roughly $10-20/mo for a dedicated VPS. No execution limits.
- Cloud Starter ($20/mo): 2,500 workflow executions - insufficient at this volume (you'd need a higher tier).
- Cloud Pro ($50/mo): 10,000 executions. Still may fall short depending on execution definition.
For any meaningful volume, self-hosted n8n dominates. The Docker setup takes 30 minutes and the ongoing cost is a server bill.
Make pricing at 50K records, 8 steps
- Operations count: 50,000 records x 8 modules = 400,000 operations per month.
- Teams plan ($29/mo): includes 10,000 operations - 40x under what you need.
- Additional operation packs: required at additional cost, pushing monthly spend to $150-300+.
Cost comparison at scale: for an AI agency running outbound data pipelines at 50,000-200,000 records per month, self-hosted n8n typically costs $10-20/mo (server only). Equivalent Make usage runs $150-600+/mo depending on operation volume. The gap widens as volume grows - Make's per-operation pricing is not built for high-volume agency pipelines. Source: public Make and n8n pricing pages, verified as of June 2026.
Where n8n Wins for Agency Operators
- Cost at scale. Self-hosted n8n is free at any execution volume. This is not a minor point when you are running multiple client campaigns simultaneously, each processing thousands of leads. Make's per-operation model punishes exactly this use case.
- AI and LLM depth. n8n ships with native Claude, OpenAI, Anthropic API, Langchain agent chain, tool-calling, and vector database nodes. You can build a full retrieval-augmented generation pipeline, an autonomous research agent, or a multi-step enrichment workflow that calls Claude for ICP scoring inside n8n without leaving the canvas. Make's AI capability is primarily an OpenAI module with basic prompting.
- Code freedom. The Code node in n8n accepts real JavaScript or Python. Complex data transformations, custom business logic, regex parsing, multi-step conditionals - no workarounds. In Make, non-trivial transformations require creative formula chaining that breaks for edge cases.
- Outbound data pipelines. Lead ingestion from scrapers, enrichment via Clay's API, ICP scoring, conditional routing to different campaigns in ACA, deduplication against CRM records - this is exactly what n8n handles natively. The full architecture for this is covered in the n8n outbound automation guide.
- Data sovereignty. Self-hosted means your lead data stays on your infrastructure. For agencies handling prospect data under GDPR or client confidentiality requirements, this matters practically, not just theoretically.
Where Make Wins for Agency Operators
- Connector breadth. 1,800+ native connectors covers almost every SaaS tool in a typical client stack. HubSpot, Salesforce, Zendesk, Slack, QuickBooks, Stripe, Typeform, Monday.com - Make has pre-built modules with handled authentication and pagination. n8n can reach any API via HTTP Request, but you're building the integration from scratch each time.
- Non-technical operators. Make scenarios can be built and maintained by someone without coding experience. If your agency employs non-technical ops staff or if clients need to manage their own automations, Make's visual UI is meaningfully easier to hand off. n8n gets technical quickly as complexity increases.
- Client-facing automations. When building automations for clients who need to see and understand what they're running, Make's step-by-step layout is easier to document and present. n8n's canvas becomes a tangle of nodes at scale.
- Speed for simple integrations. Connecting two popular SaaS apps with a trigger-action workflow is genuinely faster in Make. The native connector handles field mapping suggestions, pre-built auth flows, and built-in pagination. n8n's HTTP node approach requires more configuration for the same result.
The Critical Gap Both Leave Open
n8n and Make are orchestration tools. They move data and apply logic. Neither sends LinkedIn messages, generates AI-personalized cold email copy, manages a unified reply inbox, or runs multi-channel outbound sequences across LinkedIn, email, WhatsApp, and Instagram simultaneously.
This is the part of the AI agency stack that most operators underestimate. You can build a flawless lead pipeline in n8n - perfect enrichment, tight ICP filtering, zero duplicates - and still have no way to actually contact those leads without a dedicated outreach platform downstream.
In Cedric's experience running outbound operations for B2B clients, the teams that waste the most time are the ones trying to build an outreach engine inside n8n via custom LinkedIn and email HTTP nodes. They end up rebuilding a worse version of a platform that already exists - without the account safety logic, rate limiting, deliverability infrastructure, or AI content generation that comes built-in to a dedicated execution layer.
The right architecture: n8n (or Make) handles the data pipeline upstream - ingestion, enrichment, scoring, deduplication. A platform like ACA handles everything from sequence execution to reply management to CRM updates. Both tools connect to ACA via webhook or HTTP Request. The full stack is documented in the AI agency stack 2026 guide.
For the agency building AI-powered outbound as a service: the infrastructure layer is n8n plus ACA plus your CRM. n8n is not optional if you want clean, enriched, scored leads going into campaigns. ACA is not optional if you want those leads to receive coordinated multi-channel outreach with AI-personalized content. For how agencies package and sell this: the AI automation agency guide covers positioning and pricing.
The foundational point from ACA's positioning: AI agency = infrastructure + offer + niche. n8n and Make are tools inside the infrastructure. ACA is the infrastructure that connects directly to the offer. Neither n8n nor Make is the platform - they are upstream of it.
Your outbound data pipeline should be invisible to your clients. All they should see is booked meetings. n8n runs the data layer silently, ACA runs the delivery layer - clients see outcomes, not plumbing.
Which Should You Pick
Pick n8n when: you or someone on your team can write basic JavaScript, you are running high-volume lead pipelines where per-operation costs matter, you need deep AI/LLM integration (agents, embeddings, Claude or OpenAI tool use), you want self-hosted data control, or you are building a repeatable outbound infrastructure that feeds into ACA campaigns. This covers most serious AI agency operators building their own client acquisition stack.
Pick Make when: your agency builds automations for clients across broad SaaS stacks (HubSpot, Salesforce, Stripe, QuickBooks), you need non-technical staff or clients building their own flows, or you are connecting popular business apps where Make's 1,800+ native connectors eliminate the configuration overhead of n8n's HTTP approach.
Consider both when: your agency does outbound automation work (n8n for the data pipeline) AND client SaaS integration work (Make for the connector breadth). Running both tools in different use-case lanes is a legitimate option - n8n handles anything AI/data-heavy, Make handles client-facing SaaS workflows.
The outbound sales automation guide covers how the full pipeline fits together, including where n8n slots in between your data sources and your campaign execution layer.
Frequently Asked Questions
Can both n8n and Make connect to ACA?
Yes. Both connect to ACA via webhook triggers or HTTP Request nodes. You POST to ACA's lead ingestion endpoint, CRM endpoints, or campaign enrollment API. n8n's tighter AI/LLM integration makes it the better upstream layer for pre-processing and scoring leads before campaign injection, but Make's HTTP module handles basic lead ingestion equally well for simpler flows.
Is n8n actually free if self-hosted?
Yes. The n8n codebase is open-source under a fair-code license. You deploy it on any server running Docker. A $6-15/mo VPS (Hetzner, DigitalOcean, Render) handles most agency-scale workflows without performance issues. The only costs are your server and any external API calls (OpenAI, Clay, Apify, etc.) your workflows make. There is no n8n execution charge on self-hosted.
Which platform is better for building AI agents inside workflows?
n8n, by a wide margin. Native Langchain agent nodes, Claude and OpenAI tool-calling support, vector database connections for RAG, and sub-workflow chaining make n8n genuinely capable for autonomous agent pipelines. Make's AI support is primarily an OpenAI module that accepts text input and returns text output - useful for simple summarization or classification, not for agent loops with tool use and memory.
How steep is n8n's learning curve compared to Make?
For simple trigger-to-action flows, n8n and Make take roughly the same time to learn - an afternoon to build your first working workflow. The gap widens on complexity: n8n's canvas-based layout and optional Code node mean that as workflows grow in logic complexity, comfort with JavaScript becomes genuinely useful. Most agency operators with a technical background are productive in n8n within a week. Non-technical operators typically find Make faster to use consistently.
Can I migrate existing Make scenarios to n8n?
There is no automated migration path. Each scenario needs to be rebuilt as an n8n workflow. Simple linear flows (trigger - transform - HTTP call) typically take 1-2 hours to rebuild. Complex multi-branch scenarios with custom data transformations can take half a day. For agencies migrating for cost reasons, the self-hosted savings typically pay back the migration time within the first month at meaningful volumes.
Does n8n work well for client-facing agency automations?
It depends on the client. n8n does not have a polished client portal the way some tools do. If clients need to view, manage, or edit their own workflows, you are either giving them full n8n access (which exposes all your workflows) or building a custom interface. Make's Teams plan is better suited for scenarios where clients interact with their own automations through a clean UI. For internal agency infrastructure - your own outbound pipeline, not client-managed - n8n is the stronger choice.