ComfyUI Cloud Services in 2026 (Ranked by Someone Who Actually Uses Them)

Quick Verdict

In the "n8n vs Make for AI automation workflows comparison."
The short answer is: both platforms can handle AI workflows, but they're built for different builders. n8n gives developers and technical teams more control over AI agents, RAG pipelines, custom code, and self-hosting. Make is faster to get started with, has a larger pre-built app library, and suits marketers and non-technical teams who want automation without touching code. Neither is automatically cheaper — your bill depends on how your workflows are structured, not just which plan you pick.

If you've been trying to choose between n8n and Make for your AI workflows, you've probably already noticed that the comparison is harder than it looks. The feature lists overlap. The pricing pages use completely different units. And the "best tool" answer shifts depending on whether you're building a lead qualification agent, a content repurposing pipeline, or a RAG-based support assistant.

This guide cuts through the noise. It's a scenario-based comparison grounded in official documentation — no invented test data, no brand cheerleading. By the end, you'll know which platform fits your specific workflow, skill level, privacy requirements, and volume.

Pricing and features are accurate as of June 2026. Both platforms update their plans regularly — always verify current rates at n8n.io/pricing and make.com/en/pricing before committing.

n8n vs Make at a Glance

Feature	n8n	Make
Best for	Developers, technical teams, AI builders, privacy-sensitive deployments	Marketers, solopreneurs, non-technical teams, rapid visual automation
Billing unit	Executions (per complete workflow run, unlimited steps)	Credits (roughly one per module action in a scenario)
Entry paid plan	€20/month (annual) — 2,500 executions, unlimited steps	Core $9/month — 10,000 credits
AI agents	Native AI Agent nodes + tool/memory/model/retriever components	Make AI Agents — visual agent orchestration across 3,000+ apps
RAG support	Dedicated RAG docs, vector store nodes, retrievers, embeddings	Buildable via connectors, HTTP/API configuration
Self-hosting	Free Community Edition available; Business/Enterprise add governance	Managed cloud only (on-prem connectivity ≠ self-hosted platform)
App/integration count	1,905 native; extensible via HTTP, custom nodes, NPM packages	3,000+ pre-built app connectors
Custom code	JavaScript/Python steps, HTTP/GraphQL, CLI nodes (self-hosted)	Code App supports JavaScript or Python; credits consumed by runtime
Debugging	Execution logging, error workflows, retries; Git/log streaming on higher tiers	Execution history, error handlers, visual scenario inspection by plan
License	Fair-code / source-available (Sustainable Use License) — not OSI open source	Proprietary SaaS
Learning curve	Moderate to steep — more powerful, more configuration	Low to moderate — visual-first, faster onboarding

The Biggest Difference: Executions vs Credits

Before you compare prices, you need to understand that n8n and Make don't even count usage the same way. This is where most comparisons go wrong.

• n8n bills by workflow execution. Every time a workflow runs — whether it has 3 steps or 30 — that's one execution. A 500-execution month is 500 workflow runs, regardless of how complex each run was.
• Make bills by credits. Each module action in a scenario generally consumes one credit. A 12-module scenario running once uses roughly 12 credits. Run it 1,000 times a month and you're looking at around 12,000 credits — before accounting for retries, iterators, bundles, code runtime, or any special pricing on AI-specific modules.

Practical example (illustration only — not a universal estimate)

A 12-module AI content workflow running 1,000 times per month = roughly 1,000 n8n executions vs roughly 12,000 Make credits.At n8n Starter (€20/month for 2,500 executions), that run volume fits comfortably. At Make Core ($9/month for 10,000 credits), it would exceed the plan. Whether n8n or Make costs less in practice depends on your specific workflow step count, run frequency, and external API calls.

The upshot: n8n's pricing rewards complex, step-heavy workflows run at moderate volume. Make's pricing rewards simpler, shorter scenarios that don't run thousands of times per month. Neither is universally cheaper. Model your own numbers before committing.

Pricing at Different Volumes

Volume scenario	n8n cost (cloud)	Make cost	Who pays less
Low volume 5-step workflow, 200 runs/month	€20/month Starter covers it (2,500 executions)	Free plan covers it (~1,000 credits)	Make (free tier)
Medium volume 15-step workflow, 500 runs/month	€20/month (500 executions used of 2,500)	~7,500 credits — Core plan ($9/month) for 10,000 credits	Comparable — depends on actual plan fit
High volume 20-step AI workflow, 2,000 runs/month	€50/month Pro (10,000 executions — 2,000 used)	~10,000 credits — likely Team tier ($29/month) or higher	n8n tends to be cheaper at this step count
Self-hosted n8n Any volume	$0 platform fee (Community Edition) + infrastructure costs	Not available	n8n (if you manage your own server)

Note: All prices stated are from official plan pages as of June 2026 and billed annually unless noted. n8n prices listed in EUR. Make prices in USD. External AI API costs (OpenAI, Anthropic, Gemini, vector databases) are separate from platform fees on both tools.

Illustrative Usage at Different Volumes

Illustrative scenario	n8n usage	Estimated Make usage	What to check
5-action workflow 200 runs/month	About 200 executions	About 1,000 credits	This may fit Make’s free allowance only if each run uses exactly five billable actions and there are no retries, iterators, or higher-cost modules.
15-action workflow 500 runs/month	About 500 executions	About 7,500 credits	Compare n8n Starter with Make’s current 10K-credit plans, then account for code runtime, bundles, and retries.
20-action workflow 2,000 runs/month	About 2,000 executions	About 40,000 credits	Check the current price of Make’s 40K-credit allowance rather than applying the advertised 10K-credit Teams price.
Self-hosted n8n Community Edition	No Community Edition subscription fee	Not applicable	Include server, database, backup, security, monitoring, maintenance, and licensing requirements in the total cost.

Note: These examples illustrate billing behavior rather than guaranteed monthly costs. Actual Make usage can increase when scenarios process multiple bundles, iterate over records, retry actions, use the Code App, or call AI features with different credit rates. External model and database fees are separate on both platforms.

Which Is Better for AI Automation Workflows?

AI Agent Support

• n8n provides native AI Agent nodes alongside components for models, memory, tools, retrievers, vector stores, and structured output. This gives technical builders granular control over how an agent reasons, calls tools, stores context, and moves through deterministic workflow logic.
• Make AI Agents are available across Make plans and are built directly into its visual canvas. They can make decisions, trigger scenarios, and orchestrate work across Make’s library of more than 3,000 apps. Make also provides visibility into agent decisions and supports approval steps and deterministic rules around agent actions.

n8n is generally the stronger fit when you want to assemble a highly customized agent from individual models, tools, memory systems, and retrieval components. Make is especially attractive when the agent’s main purpose is to coordinate actions across business applications through a visual interface.

RAG and Knowledge-Base Workflows

• n8n has dedicated documentation for RAG workflows, with built-in retriever nodes, embedding generation, and connections to multiple vector store services. The building blocks are explicit — you can see and configure every step of the chunk → embed → store → retrieve → generate pipeline.
• Make can build RAG workflows through connected apps, APIs, and AI tools. The difference isn't capability — it's native building blocks. In n8n, the RAG components are first-class workflow nodes. In Make, you're assembling the same result through connectors and HTTP calls, which requires more design work and is harder to debug when a retrieval step misbehaves.

For a simple RAG support chatbot, Make can get you there. For a production RAG pipeline with custom chunking logic, reranking, and confidence thresholds, n8n's native tools are considerably cleaner to work with.

LLM Integrations and Local Models

Both platforms connect to the major LLM APIs — OpenAI, Anthropic, Google Gemini. n8n goes further by supporting local model deployments (Ollama, LM Studio) in self-hosted environments. If you're running a local LLM for data-privacy reasons or to avoid per-token costs, n8n is the only practical option here.

MCP and Tool Orchestration

Both platforms now support the Model Context Protocol, but they approach it from different directions.

• n8n includes MCP client and server capabilities, allowing workflows and AI agents to call external MCP tools or expose n8n workflows through an MCP-compatible interface. Its MCP support fits naturally with n8n’s broader collection of agent, retrieval, model, and custom-code nodes.
• Make offers both Make MCP Server and Make MCP Client. The server can expose Make scenarios as structured tools for Claude, ChatGPT, Cursor, and other MCP-compatible clients, while the client lets Make scenarios and agents use tools provided by external MCP servers.

Choose n8n when MCP is one part of a deeply customized AI pipeline with granular model and retrieval control. Choose Make when you want to expose business automations across thousands of applications as reusable tools for AI assistants.

Ease of Use and Learning Curve

• Make wins on onboarding speed. The visual canvas is cleaner, the app connectors are preconfigured with OAuth flows, and a non-developer can build a useful automation in under an hour. Templates cover most common marketing, CRM, and content scenarios. If you need automation working this week and don't want to read documentation, Make is where you start.
• n8n's canvas is also visual, but the data flow, node configuration, and debugging require more understanding. You need to know what you're passing between nodes, how error workflows connect, and what a webhook response object looks like. That learning curve pays off once you need to build something custom — but the cost of entry is real.

Before you assume Make is easier: More pre-built connectors means fewer decisions, but it also means less flexibility when a connector doesn't behave the way you need. If you hit a wall in Make, the workaround is usually an HTTP module — at which point you're doing exactly what n8n users do by default.

API Flexibility, Custom Code, and Integrations

• n8n treats HTTP requests and custom code as first-class citizens. Every workflow can include JavaScript or Python steps, raw HTTP/GraphQL calls, and webhook triggers without jumping through extra hoops. On self-hosted instances, you can write custom nodes in Node.js and install any NPM package the workflow needs.
• Make's Code App supports JavaScript and Python. Code execution consumes credits based on runtime, so heavy custom-code steps have a direct billing impact. Make also provides API access on paid plans. The difference is ergonomics — n8n is built from the ground up for technical flexibility, while Make's code capability is an add-on to what's primarily an app-connector platform.

For integrations by count, Make wins handily — 3,000+ pre-built connectors versus n8n's about 1,900. But n8n's HTTP node can reach any REST or GraphQL API, so the practical gap closes for builders comfortable with API calls. The pre-built connector count matters most when your workflows hit obscure SaaS apps that you don't want to configure manually.

Error Handling, Debugging, and Maintenance

Debugging is where the two platforms feel most different day-to-day.

• n8n shows you exactly what data is passed through each node on every execution. You can replay a failed execution with the original input data, set up error workflows that trigger when something goes wrong, and configure retries with exponential backoff. Higher-tier plans add log streaming, Git version control for workflows, and environment separation (dev/staging/prod).
• Make provides execution history with visual scenario inspection, error handlers, and plan-gated search through execution logs. The visual inspection is useful — you can see which module failed and what data it received. What you get depends on your plan tier.

For AI workflows specifically, error handling matters more than in standard automation because LLM outputs are unpredictable. A response that's too long, returned in the wrong format, or missing an expected field can break downstream steps. n8n's granular error workflows and retry logic handle this more expressively. In Make, you can configure error routes, but the options are narrower at lower plan tiers.

Self-Hosting, Privacy, Security, and Licensing

• n8n's self-hosted Community Edition is free. You run it on your own server, your own VPS, or your own Kubernetes cluster. All workflow data stays inside your infrastructure — no execution logs sent to n8n servers, no credentials stored on a third-party platform. For healthcare, legal, fintech, or any team working under strict data residency requirements, this is the deciding factor.

One thing to be clear about: n8n describes its model as fair-code / source-available under the Sustainable Use License. It explicitly does not claim to be open source under the OSI definition. You can inspect and modify the code, but commercial use of the self-hosted version has licensing conditions worth reading before you build a client product on top of it.

• Make is a fully managed SaaS platform. Your data lives on Make's servers. That's fine for most business workflows, and the security posture of an established SaaS provider is actually better than most self-managed servers for teams without dedicated infrastructure experience. But it's not self-hosting, and it doesn't give you the same data control.

Self-hosting does not automatically guarantee compliance or complete data isolation. Review telemetry settings, connected third-party services, access controls, backups, encryption, and the requirements of your industry before using it for sensitive workloads.

Three Real-World AI Workflow Scenarios

Scenario 1: AI Content Repurposing Workflow

The workflow: Blog post or video transcript → LLM → LinkedIn, X, Telegram, and email drafts → human approval step → publishing queue.

n8n

Around 10–14 nodes, depending on output channels. LLM prompt templates go in Code or Set nodes. Human approval via a Wait node + webhook callback. Conditional routing handles channel-specific formatting. Moderate setup complexity — a developer or technical marketer can configure it in a few hours. Debugging is clean: if the LinkedIn formatter fails, you replay the execution from that node with the original LLM output. Each run counts as one execution regardless of output count. Cost is predictable at any volume.

Make

Roughly 14–20 modules with separate action modules per output channel. Make's visual builder makes the branching logic easier to read at a glance. The human approval step needs a webhook or a form-based wait state — workable but requires extra modules. Each module action counts as a credit, so a 20-module scenario running 200 times a month uses ~4,000 credits. Faster to set up initially; harder to audit if the LLM returns an unexpected format.

Best fit: Either platform works. Make is faster to build for a non-technical content team. n8n is easier to modify when the LLM output format changes.

Scenario 2: Lead Qualification and Enrichment Workflow

The workflow: Form submission → enrichment API → AI scoring → CRM update → Slack notification → human review for uncertain cases.

n8n

Around 8–12 nodes. The AI scoring step is a straightforward LLM node. Conditional branching routes uncertain leads to a human-review queue via Slack with an interactive callback webhook. Error handling catches failed enrichment API calls and queues them for retry. A developer can wire this in a couple of hours. Execution logging makes it easy to diagnose why a specific lead scored incorrectly. One execution per form submission, regardless of step count.

Make

Around 10–15 modules. Make's pre-built connectors for popular CRMs (HubSpot, Salesforce, Pipedrive) speed up the CRM update step considerably — often faster than configuring an HTTP request in n8n. The AI scoring step uses a connected AI tool or HTTP call. Human review routing via Slack is straightforward with Make's Slack connector. Credit consumption is per module, so higher-volume lead flows add up quickly. This scenario is a good fit for Make when the CRM is already in Make's connector library.

Best fit: Make if your CRM is in its connector library and lead volume is moderate. n8n if volume is high, if you need detailed audit logging, or if the enrichment API needs custom authentication.

Scenario 3: RAG Support Assistant with Human Escalation

The workflow: Documents → chunking and embeddings → vector store → user question → grounded answer → confidence check → human escalation for low-confidence results.

n8n

Around 12–18 nodes using dedicated RAG components: document loaders, text splitters, embedding nodes, vector store upsert/query nodes, an AI Agent or LLM chain, and a confidence-routing condition. The official n8n documentation covers this exact pattern with working examples. The confidence check and escalation route are native workflow logic. This is where n8n's native RAG building blocks pay off — each component is explicit, auditable, and replaceable. Privacy-sensitive: run self-hosted with a local embedding model and zero data leaves your server.

Make

Buildable through connected vector database services, OpenAI API calls for embeddings, and HTTP modules for retrieval. The main difference is that you're assembling the RAG pipeline from general-purpose modules rather than dedicated nodes, which means more configuration work and fewer guard rails if a step misbehaves. Confidence scoring requires custom logic in the Code App. Make's connector to OpenAI simplifies the embedding step. Data flows through Make's servers unless routed through an on-prem data connector — worth checking if data residency is a concern.

Best fit: n8n — by a meaningful margin for production RAG deployments. The dedicated components, audit logging, and self-hosting option make it the more practical choice for anything beyond a basic proof of concept.

Best Platform by User Type

Who you are	Recommended platform	Why
Non-technical marketer or solopreneur	Make	Faster onboarding, visual builder, large app library, minimal configuration
Content team automating repurposing	Make or n8n	Make for speed; n8n if volume is high or LLM formatting needs iteration
Marketing agency running client workflows	Make	Pre-built connectors for common CRMs, ad platforms, and social tools; multi-scenario management
Developer building custom AI tools	n8n	Custom nodes, JavaScript/Python steps, HTTP flexibility, stronger debugging
Team with data privacy or compliance requirements	n8n	Self-hosted Community Edition keeps data on your infrastructure
High-complexity AI agent builder	n8n	Native agent nodes, RAG components, MCP support, mature error handling
Startup automating ops at low volume	Make	Free plan covers up to 1,000 credits/month; fast to iterate
Team running high-volume, multi-step AI workflows	n8n	Per-execution billing becomes significantly cheaper at scale vs per-credit

Choose n8n If… / Choose Make If…

Choose n8n if…

You need to build and maintain AI agents or multi-step RAG pipelines. Your workflows have 10+ steps and run frequently. You work with sensitive data that can't leave your infrastructure. You need local LLM support. You write JavaScript or Python and want full control over data transformations. You need detailed execution logs and environment separation. You want to avoid per-credit billing at scale.

Choose Make if…

You need automation running this week without a steep learning curve. Your workflows connect common SaaS apps that Make already has connectors for. You're a marketer, ops manager, or solopreneur who doesn't want to touch code. Your workflows are short (under 10 modules) and run at moderate volume. You want a managed platform with no infrastructure overhead. Budget is tight and the free plan covers your monthly volume.

Related Reading on AI Promix

• n8n Workflows for AI Content Automation: 5 Templates
• How to Automate Social Media Posting With AI
• How to Automate Work With AI
• Best AI Content Repurposing Tools in 2026
• n8n Workflow Examples You Can Copy-Paste Right Now
• ChatGPT vs Gemini vs Claude (2026): Which AI Wins?

Frequently Asked Questions

What is the main difference between n8n and Make?

The biggest difference is in how they charge and how flexible they are. n8n bills by workflow executions (one per complete run, regardless of steps), while Make bills by credits (roughly one per module action). n8n gives developers more control over custom code, API calls, and self-hosting. Make has a larger pre-built app library and is faster to get started with for non-technical users.

Which is better for AI automation workflows: n8n or Make?

For complex AI workflows — agents, RAG pipelines, local LLMs, custom logic — n8n is generally the stronger fit. It has dedicated AI Agent nodes, native RAG components, and more flexible error handling. Make is better for AI steps embedded in broader business automation, especially when connecting to CRMs, marketing tools, and other SaaS apps that already have Make connectors.

Is n8n or Make easier for beginners?

Make is easier for most beginners. Its visual canvas is cleaner, app connectors come pre-configured, and a working automation can be running in under an hour. n8n has a steeper curve — it rewards users who understand data structures, API responses, and workflow logic. If you're comfortable with those concepts, n8n's learning curve flattens out quickly. If you're not, start with Make.

Which platform is better for AI agents?

n8n currently has a more mature AI agent system, with native Agent nodes, tool components, memory, and retriever support. Make AI Agents are available across Make plans and are integrated into the platform’s visual automation environment. For production AI agents that need reliable tool orchestration and granular error handling, n8n is the safer choice right now.

Can both n8n and Make build RAG workflows?

Yes, both can. n8n has dedicated RAG documentation and first-class nodes for embeddings, vector stores, and retrievers. Make can build RAG workflows through connected services and HTTP/API modules. The difference is that n8n's native components make RAG pipelines easier to configure, audit, and maintain. For a production RAG deployment, n8n's building blocks are more practical.

Which is cheaper at scale: n8n or Make?

It depends on workflow structure. n8n's per-execution billing tends to favor step-heavy workflows at high run volumes — a 20-step workflow running 2,000 times costs 2,000 executions in n8n but roughly 40,000 credits in Make. At low volume and low step count, Make's free tier can be cheaper. Self-hosted n8n Community Edition removes the platform fee entirely, though you pay for server infrastructure and maintenance time.

Does n8n support self-hosting, and is it open source?

n8n offers a free self-hosted Community Edition. It is not open source under the OSI definition — n8n uses a fair-code / source-available model under the Sustainable Use License. You can inspect and modify the code, but commercial use has licensing conditions. Self-hosting gives you full data control and no platform execution fees, but you're responsible for infrastructure, updates, and uptime.

Can Make use custom JavaScript or Python?

Yes. Make's Code App supports both JavaScript and Python. Code execution consumes credits based on runtime. It's a capable option for custom logic, though for workflows that rely heavily on custom code, n8n's native code steps are more deeply integrated and don't carry separate runtime credit costs.

Which platform is better for API-heavy workflows and local LLMs?

n8n. Its HTTP/GraphQL nodes, custom node support, and JavaScript/Python steps are built for API-heavy workflows. For local LLMs, n8n's self-hosted deployment supports Ollama, LM Studio, and other local model servers — your data never leaves your infrastructure. Make's architecture is optimized for cloud-connected SaaS scenarios rather than local model deployments.

Final Verdict

There's no single winner here — and that's not a cop-out. It's the honest answer.

• Choose n8n when your workflow depends on advanced AI agents, RAG pipelines, local LLMs, custom code, self-hosting, or complex multi-step automations that run at scale. n8n currently lists roughly 1,905 integrations and nodes, but its main advantage is not the raw count. It is the ability to extend workflows through HTTP and GraphQL requests, community nodes, custom nodes, and code-based data transformations.
• Choose Make when speed, visual simplicity, and ease of adoption matter most. Make advertises more than 3,000 pre-built app integrations, which is especially useful for marketers, operations teams, and non-technical users connecting common SaaS tools. Its larger ready-made app catalog, visual canvas, and guided setup can make everyday business automations faster to build and easier to maintain.

Before picking either, map out one real workflow: count the steps, estimate the monthly run volume, and check whether the apps you need have native connectors. That exercise will tell you more than any comparison table.

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Written by AI Promix Editorial Team AI Promix is your hub for AI tools, prompt engineering, automation workflows, tutorials, and AI trends designed to boost productivity.