Quick answer: n8n wins for AI agent workflows — LangChain-native, self-hostable, best cost at scale. Zapier wins for non-technical teams with 8,500+ integrations. Make wins on value for mid-complexity AI workflows. Most teams in 2026 choose based on technical depth vs. ease of use.
n8n vs. Zapier vs. Make is the comparison every team building AI agent workflows faces in 2026. All three platforms now support AI agents, LLM integrations, and autonomous decision-making — but they approach it in fundamentally different ways.
Zapier gives you 8,500+ app connections and the easiest setup. Make offers a visual canvas with competitive pricing. n8n gives you LangChain under the hood, self-hosting, and the deepest AI capabilities of any no-code platform.
The right choice depends on your team's technical depth, AI ambitions, and budget. After testing all three platforms on real AI agent workflows, here's what actually matters — including the pricing traps nobody mentions in the marketing.
If you've already chosen n8n and want to build your first agent, see our n8n AI agents tutorial for step-by-step setup with production patterns.
Quick Comparison
| Feature | n8n | Zapier | Make |
|---|---|---|---|
| AI agent approach | LangChain-native agent nodes | Zapier Agents + AI Actions | AI Agents module (2025) |
| Starting price | Free (self-hosted) / $24/mo cloud | Free / $19.99/mo | Free / $9/mo |
| Billing model | Per execution (entire workflow = 1) | Per task (each action = 1 task) | Per credit (each action = 1+ credits) |
| App integrations | 400+ native, 1,200+ with community | 8,500+ | 2,000+ |
| Self-hosting | Yes (free, unlimited) | No | No |
| LLM support | OpenAI, Anthropic, Gemini, Ollama, any OpenAI-compatible | OpenAI primarily, some third-party | Custom AI providers on paid plans |
| RAG support | Native vector stores + embeddings | Limited (third-party) | Via AI modules |
| MCP support | MCP Trigger node | Zapier MCP (30,000+ actions) | Not featured |
| Best for | Technical teams, AI-heavy workflows | Non-technical teams, broad integrations | Visual builders, mid-complexity flows |
n8n — The AI Agent Powerhouse
n8n is the clear leader for building AI agent workflows. Its AI capabilities aren't a bolt-on feature — they're built on LangChain under the hood, giving you access to the full agent ecosystem through a visual drag-and-drop canvas.
n8n raised $180M at a $2.5 billion valuation in October 2025, driven by 6x user growth and 10x revenue growth — largely fueled by AI agent adoption. The platform ships 70+ AI-specific nodes covering LLMs, embeddings, vector databases, speech, OCR, and image models.
Pros
- LangChain-native with 70+ AI nodes (agents, embeddings, vector stores, memory)
- Free self-hosting with unlimited executions
- Supports any LLM: OpenAI, Anthropic, Gemini, Ollama (local models)
- Built-in RAG with native vector store nodes (Pinecone, Qdrant, Supabase)
- MCP Trigger node turns workflows into MCP-compatible tools
- Sub-agents and multi-agent orchestration
- Execution-based pricing (10-step workflow = 1 execution)
Cons
- 400 native integrations vs Zapier's 8,500+
- Steeper learning curve for non-technical users
- Self-hosting requires basic DevOps knowledge
- Cloud pricing gets expensive at scale ($800/mo Business plan)
What makes n8n different for AI agents
The key distinction: in n8n, your AI agent sits inside the same workflow as your regular automation steps. The agent isn't a separate product — it's a node connected to your HTTP requests, database queries, Slack messages, and CRM updates. This means agent decisions flow directly into actions without API stitching or context loss.
n8n supports four agent types out of the box:
- Tools Agent (ReAct) — reasons and calls tools iteratively until the task is done
- Conversational Agent — chat-based with persistent memory
- Plan and Execute Agent — breaks goals into sub-tasks, executes step-by-step
- OpenAI Functions Agent — uses OpenAI's function-calling API
Each agent type connects to the same pool of tools — every n8n node becomes a tool the agent can call. And with the AI Agent Tool node, you can build hierarchical systems where a main orchestrator delegates to specialist sub-agents.
Self-hosted n8n on a $10/month VPS gives you unlimited executions, full data privacy, and local LLM support via Ollama. For teams in healthcare, finance, or legal — where data can't leave your infrastructure — this is the only real option among the three platforms.
n8n pricing breakdown
| Plan | Price | Executions | Best for |
|---|---|---|---|
| Self-hosted Community | Free | Unlimited | Teams with DevOps capability |
| Cloud Starter | $24/mo | 2,500/mo | Solo builders testing the platform |
| Cloud Pro | $60/mo | 10,000/mo | Small teams in production |
| Cloud Business | $800/mo | 40,000-50,000/mo | Scaling teams with SSO needs |
| Enterprise | Custom | Unlimited | Large organizations |
The critical detail: one workflow run = one execution, regardless of how many nodes it contains. A 50-step AI agent workflow still counts as 1 execution. This makes n8n dramatically cheaper than Zapier at scale.
Zapier — The Integration King
Zapier remains the easiest way to connect apps and automate workflows. With 8,500+ integrations and natural-language Copilot, it's the platform where non-technical teams build AI automations in minutes. But that convenience comes at a cost — literally.
Zapier's AI Agents (evolved from Zapier Central) can autonomously research, decide, and act across thousands of apps. A new lead fills out a form, and the agent researches the company, checks LinkedIn, drafts personalized outreach, and updates the CRM — all without human intervention.
Pros
- 8,500+ app integrations — largest catalog by far
- Zapier Copilot builds workflows from natural language descriptions
- Zapier Agents for autonomous multi-step AI tasks
- Zapier MCP exposes 30,000+ actions to any MCP client
- Lowest learning curve of the three platforms
- Enterprise-ready with Admin Center and team management
Cons
- Task-based pricing punishes complex workflows (each action = 1 task)
- Limited AI depth — no native RAG, vector stores, or local LLM support
- No self-hosting option
- MCP calls cost 2 tasks each
- AI agents lack agentic loops and memory compared to n8n
Zapier's AI capabilities
Zapier approaches AI differently than n8n. Instead of giving you agent architecture building blocks, it provides higher-level abstractions:
- Zapier Agents — autonomous AI agents that make decisions and take actions across connected apps
- Zapier Copilot — natural-language workflow builder that creates Zaps from descriptions
- AI Actions — add GPT-powered steps (generation, summarization, classification) into any Zap
- Zapier Chatbots — customer-facing AI chatbots on paid plans
- Zapier MCP — exposes 30,000+ actions as MCP-compatible tools for external AI clients
The approach works well for teams that want AI automation without thinking about LLM providers, embedding models, or vector databases. But it means you're locked into Zapier's AI implementation — you can't swap in a local model, build a RAG pipeline, or customize the agent's reasoning loop.
Every action in a Zap counts as a task — including filters, formatters, and AI steps. A single multi-step Zap with 10 actions consumes 10 tasks per run. An AI agent that calls 5 tools burns 5+ tasks per interaction. At high volumes, this adds up fast.
Zapier pricing breakdown
| Plan | Price | Tasks/mo | Best for |
|---|---|---|---|
| Free | $0 | 100 | Testing basic automations |
| Starter | $19.99/mo | 750 | Simple single-step Zaps |
| Professional | $49.99/mo | 2,000 | Multi-step workflows with AI |
| Team | $103.50/mo | 2,000 (25 users) | Collaborative teams |
| Enterprise | Custom | 10,000+ | Large organizations |
Make — The Visual Builder on a Budget
Make (formerly Integromat) sits between n8n and Zapier — more visual than n8n, cheaper than Zapier, with AI capabilities that are newer but improving fast. Its visual scenario builder is the most intuitive of the three for complex branching logic.
Make launched AI Agents in April 2025 and announced next-generation agents at Waves '25 (October 2025) with a redesigned reasoning panel, multimodal inputs (documents, images, audio), and shareable agent templates. The AI capabilities are catching up, but they're roughly a year behind n8n's maturity.
Pros
- Cheapest entry point ($9/mo) for meaningful automation
- Best visual workflow builder with clear branching and conditional logic
- AI Agents launched 2025 with next-gen features rolling out 2026
- 2,000+ integrations — more than n8n, less than Zapier
- Maia AI assistant for building scenarios
- Rollover credits (unused carry forward one month)
Cons
- AI agent features are newest and least mature of the three
- No self-hosting option
- AI modules can consume multiple credits per action
- No native RAG or vector store support
- Limited LLM provider flexibility compared to n8n
Make's AI capabilities
Make's AI approach is evolving rapidly:
- AI Agents — real-time decision-making within scenarios, with global system prompts and scenario-specific customization
- Maia — AI-powered assistant for building automations from descriptions
- AI modules — classification, summarization, content generation, sentiment analysis
- Custom AI providers — connect your own AI provider on all paid plans (since November 2025)
- The Grid — enterprise-level AI orchestration and observability dashboard
The biggest gap compared to n8n: Make has no native vector store nodes, no embedding pipelines, and no built-in RAG capabilities. If your AI agent needs to search a knowledge base, you'll need to integrate external services — adding complexity and cost.
Make pricing breakdown
| Plan | Price | Credits/mo | Best for |
|---|---|---|---|
| Free | $0 | 1,000 | Exploring the platform |
| Core | $9/mo | 10,000 | Simple automations |
| Pro | $16/mo | 10,000+ | AI workflows with priority execution |
| Teams | $34/mo | Shared | Collaborative teams |
| Enterprise | Custom | Custom | Large organizations |
Make renamed "operations" to "credits" in August 2025 (1:1 conversion). Standard actions cost 1 credit each, but AI modules can consume multiple credits per action. A text generation module might cost 1 credit, while audio transcription could cost 50.
The Real Cost Comparison
Sticker prices are misleading. Here's what a real AI agent workflow costs on each platform.
Scenario: An AI agent that triages incoming emails, researches the sender, updates your CRM, and drafts a reply. The workflow has 10 steps and runs 1,000 times per month.
| Platform | How it's billed | Cost per run | Monthly cost (1,000 runs) |
|---|---|---|---|
| n8n (self-hosted) | 1 execution per run | ~$0 (server only) | $10-20 (VPS cost only) |
| n8n (cloud) | 1 execution per run | ~$0.024 | $24/mo (Starter plan) |
| Make | 10+ credits per run | ~$0.016-$0.10 | $16-100/mo (depends on AI module cost) |
| Zapier | 10 tasks per run | ~$0.25 | $250/mo (need 10,000 task plan) |
At 10,000 monthly runs, the gap widens dramatically:
| Platform | Monthly cost (10,000 runs) |
|---|---|
| n8n (self-hosted) | $10-20 (same VPS) |
| n8n (cloud) | $60 (Pro plan) |
| Make | $100-300+ |
| Zapier | $500+ (100,000 tasks needed) |
Zapier's task-based billing is the biggest pricing trap in automation. A 10-step workflow running 20,000 times/month requires 200,000 tasks — pushing you into enterprise pricing. The same workflow on n8n self-hosted costs $10-20/month for the server.
AI Agent Capabilities: Head-to-Head
| Capability | n8n | Zapier | Make |
|---|---|---|---|
| Agent types | Tools (ReAct), Conversational, Plan & Execute, OpenAI Functions | Zapier Agents (single type) | AI Agents (single type) |
| Reasoning loops | Full agentic loops with tool calling | Limited autonomous actions | Real-time decisions within scenarios |
| Memory | Window Buffer, Token Buffer, Postgres, Redis | No persistent agent memory | No persistent agent memory |
| Sub-agents | AI Agent Tool node for hierarchical multi-agent systems | Agents can use existing Zaps as tools | Agents can access existing scenarios |
| RAG / vector search | Native: Pinecone, Qdrant, Supabase, Weaviate, Chroma, PGVector | Not native (third-party only) | Not native (third-party only) |
| Embeddings | Native embedding nodes (OpenAI, Cohere, local) | Not available | Not available |
| Document loaders | PDF, CSV, JSON, Google Docs, Notion | Not available | Limited via AI modules |
| Local / private LLMs | Ollama, any OpenAI-compatible endpoint | No | No |
| MCP protocol | MCP Trigger node (workflows become MCP tools) | Zapier MCP (expose actions to MCP clients) | Not featured |
| Custom code | JavaScript + Python with npm/PyPI libraries | Limited code steps | Limited code modules |
The gap is clear: n8n is a full AI agent framework wrapped in a visual builder. Zapier and Make are automation platforms with AI features added on top.
If your use case is "connect Slack to Google Sheets with an AI summary step," all three work fine. If your use case is "build a RAG-powered support agent that searches your documentation, remembers conversation history, and delegates to specialist sub-agents" — n8n is the only no-code platform that handles it natively.
Which Platform Should You Choose?
Choose n8n if...
You want deep AI agent capabilities — agentic loops, RAG, memory, sub-agents, local LLMs. Your team has some technical ability. You value self-hosting and data sovereignty. You're building AI-heavy workflows at scale.
Choose Zapier if...
Your team is non-technical and needs the easiest setup. You need connections to 8,500+ apps. Your AI needs are simple (summarization, classification, basic agent actions). Budget is flexible but time is not.
Choose Make if...
You want a visual builder that's cheaper than Zapier. Your workflows have complex branching logic. You need AI features but not full agentic depth. You're budget-conscious and okay with growing AI capabilities.
Or combine them...
Many teams use n8n for AI-heavy agent workflows and Zapier for simple app-to-app automations. The platforms aren't mutually exclusive — use each where it's strongest.
Decision by use case
| Use case | Best platform | Why |
|---|---|---|
| AI agent with RAG and memory | n8n | Only platform with native vector stores, embeddings, and memory nodes |
| Simple AI-powered Zap | Zapier | Fastest setup, largest app catalog, Copilot builds it for you |
| Visual automation on a budget | Make | Cheapest pro tier, best visual builder for branching logic |
| Self-hosted AI agents (data privacy) | n8n | Only platform offering self-hosting with local LLM support |
| Enterprise automation at scale | n8n or Zapier | n8n for cost; Zapier for breadth of integrations |
| Multi-agent orchestration | n8n | Sub-agent support with hierarchical delegation |
| Quick prototyping | Make or Zapier | Lower learning curve, faster to first workflow |
Migration Considerations
Already on Zapier or Make and considering n8n for AI agents? Here's what to know:
Moving from Zapier to n8n:
- Most popular Zapier integrations have n8n equivalents (Slack, Gmail, HubSpot, Google Sheets, etc.)
- Zapier-specific apps without n8n nodes can use HTTP Request nodes or community nodes
- AI workflows will need to be rebuilt — n8n's agent architecture is fundamentally different
- Expect 1-2 weeks to migrate a team's core workflows
Moving from Make to n8n:
- Similar visual approach makes the mental model transferable
- Make scenarios map roughly to n8n workflows
- Credit-based concepts translate to execution-based pricing
- AI agents will be significantly more capable after migration
The smartest approach for many teams: keep Zapier for simple app-to-app automations (where its 8,500+ integrations shine) and add n8n specifically for AI agent workflows. Run n8n self-hosted for $10-20/month and let it handle the AI-heavy lifting.
The Bottom Line
The automation platform market has split into two tiers for AI agent workflows:
Tier 1 — Full agent framework: n8n stands alone here with LangChain-native agents, RAG, memory, sub-agents, and self-hosting. It's the platform where context engineering and agentic workflows are first-class citizens.
Tier 2 — AI-augmented automation: Zapier and Make have added AI capabilities on top of their existing automation platforms. They're good for AI-enhanced workflows but can't match n8n's depth for building true AI agent systems.
If you're building serious AI agent workflows — with reasoning loops, knowledge bases, and multi-agent coordination — n8n is the clear choice. If you need to connect 50 apps with some AI sprinkled in, Zapier's breadth is hard to beat. And if budget is the priority, Make offers the best value for simpler AI workflows.
The market is moving fast. n8n hit unicorn status ($2.5B valuation) on the back of AI agent adoption. Zapier is pivoting hard toward AI orchestration. Make is shipping next-gen agents. Whichever platform you choose today, expect the capabilities to look very different in 12 months.
Get Started
New to AI agents? Start with our guide to agentic AI to understand the fundamentals. Ready to build? Our n8n AI agents tutorial walks you through your first agent workflow step by step.
For a broader look at no-code options beyond these three, explore our no-code AI agent builder comparison. And if you're weighing AI agents against traditional scripted automation, read AI agents vs. automation to understand the key differences.
cowork.ink helps teams coordinate AI agents across platforms — whether your workflows run on n8n, Zapier, Make, or all three. When multiple agents work alongside your team, cowork.ink keeps humans and AI aligned.