n8n vs Make: Testing 10 AI Workflows to See Which Comes Out on Top

When automation platforms start carrying LLM calls, vector retrieval, and multi-step Agents, traditional comparison tests no longer work. The YouTube channel Venture Void ran a more hardcore validation: spending 47 hours building the same 10 AI-native workflows on both n8n and Make, from lead scoring to proposal generation, to see which one doesn't break down under real business loads.

Why simple automation comparisons are meaningless

Most n8n vs Make comparison videos on the market test linear processes like "send a Gmail after a form submission." These tests can't expose the real differences between platforms in AI-native scenarios—multi-step LLM chains, dynamic prompt routing, vector database writes, browser Agents, and Webhook orchestration across five SaaS tools.

The 10 workflows Venture Void built cover the core scenarios of a solo business: AI lead scoring, content repurposing pipelines, customer onboarding, GPT-4 inbox triage, competitor intelligence briefings, AI data cleaning from Notion to CRM, dynamic pricing assistants, podcast-to-Newsletter conversion, automated proposal generators, and a revenue dashboard automatically pushed every Monday at 7 AM.

LLM chains and multi-step Agents: An LLM chain refers to linking multiple large language model calls in sequence, where the output of the previous step becomes the input of the next, forming a reasoning pipeline (e.g., first extract information, then classify, and finally generate a response). Multi-step Agents go further—the model can autonomously decide which external tools to call (search, databases, APIs) and dynamically adjust subsequent steps based on intermediate results. These workflows are characterized by an uncertain number of LLM calls per run and many branching paths, requiring far more from a platform's loop control, context management, and error recovery than linear automation.

Vector retrieval: Vector retrieval converts text into high-dimensional vectors (embeddings) and finds semantically similar content by calculating cosine similarity between vectors, commonly used for knowledge base Q&A, semantic search, and recommendations. In automation platforms, vector retrieval usually requires connecting to a vector database (such as Pinecone or Weaviate) or implementing it through third-party services, which directly tests the platform's native integration capabilities.

Make: Fastest to get started, but operation-based billing is an invisible ceiling

Make's visual interface is genuinely beautiful. Modules are drag-and-drop, the canvas is clean, and first-time automation builders can get their first workflow running fastest on Make. For linear tasks like podcast-to-Newsletter, the Whisper integration was set up in 20 minutes, the GPT module was done by drag-and-drop, and the end-to-end build from audio file to Ghost draft took only 40 minutes.

The problem lies in the billing model. Make charges by operation—every executed module in a workflow counts as one operation. When AI workflows involve loops, iterators, and multiple LLM calls, the operation count explodes. The lead scoring workflow consumes an average of 34 operations per lead, because it has to iterate through scoring rules, call GPT twice, and write to three target locations. Make's Pro plan costs $9 per month with a cap of 10,000 operations—this workflow would hit the ceiling before processing 300 leads, and the next plan at $16 only adds another 10,000 operations.

More serious is stability. The competitor intelligence briefing workflow needs to scrape five URLs, pass them to Claude for summarization, and then format and send an email. On the third run, the HTTP module timed out on one scraping call, and the entire scenario failed and stopped—no partial output, and no retry logic by default. The author had to add an error handling module, adding four more operations to every run. This exposes Make's design orientation: it was built for clean, predictable linear data flows, not for the messy reality of AI calls and external HTTP requests.

Operation billing model: Make counts each executed module in a workflow (such as HTTP requests, GPT calls, data transformations) as one operation. For AI workflows containing loops, iterators, and conditional branches, a single business run may trigger dozens of operations. This per-execution billing model is cost-controllable in simple linear workflows, but in AI-native scenarios, because LLM calls and external API requests are frequent, the operation count grows non-linearly with complexity, forming an invisible cost ceiling.

n8n: Steep learning curve, but built for AI complexity

n8n can be self-hosted and is open source, with cloud plans starting at $20 per month, offering unlimited workflows and unlimited executions. No operation counting, no ceiling. Running the same lead scoring workflow 10,000 times a month doesn't change the bill. For AI-native businesses that call LLMs on every run, this difference in business model is fundamental.

The cost is setup time. n8n's interface isn't as polished as Make's, and node configuration is heavier. Its AI nodes now have formal Agent nodes, Chain nodes, and direct tool-calling integrations, but they require you to truly understand what you're building—memory mechanisms, context windows, tool binding—you can't just drag and drop your way through. The inbox triage system took three and a half hours on n8n versus one hour on Make, but the n8n version is clearly more capable: persistent memory through a simple Postgres node, the ability to call external tools mid-conversation, and processing by email thread rather than individual messages.

The proposal generator is where n8n truly surprises. Using AI Agent nodes connected to a vector store, a Typeform submission triggers a semantic search of the historical proposal knowledge base, extracts relevant passages to inject into the GPT-4 prompt, and combines customer context to generate a structured proposal draft—the entire process takes less than 90 seconds per submission. Doing the same thing on Make requires chaining eight separate modules, and vector search isn't possible without a third-party service, adding another $5 to $12 per month.

Self-hosting and open source: n8n uses the Sustainable Use License, allowing users to self-host for free with data remaining entirely on their own servers, suitable for scenarios sensitive to data privacy and cost. Cloud plans provide hosted services, billed by execution count (not operation), with no upper limit on executions (fair use policy). This model is friendlier for high-frequency AI calls, because a workflow run counts as one execution no matter how many LLM calls it makes internally.

AI Agent nodes and tool calling: n8n's AI Agent nodes have a built-in ReAct (Reasoning-Acting) loop, allowing the LLM to autonomously select and call bound tools (such as HTTP requests, database queries, vector searches), while supporting memory mechanisms (such as Postgres persistence) to maintain multi-turn conversation context. This contrasts with Make, which requires manually chaining multiple modules to achieve similar functionality, lowering the barrier to building complex AI workflows.

The cost math: Up to $840 difference per year

After 47 hours of testing, the author migrated his entire tech stack to n8n, carrying all 10 core workflows. The monthly cost is the $20 cloud plan, or close to zero when self-hosted. The same workflows on Make, at his run volume, would cost $60 to $90 per week—a difference of up to $840 per year.

n8n also offers more fine-grained error handling: retry and wait behavior can be configured per node, making it more resilient to unpredictable external APIs. Make's strengths are concentrated in low-volume, simple, predictable scenarios—if you process fewer than 500 moderately complex runs per month, the $9 Pro plan is indeed a good deal.

Total cost of ownership (TCO) considerations: When choosing a platform, beyond subscription fees you also need to factor in learning costs, maintenance costs, and opportunity costs. Make's low barrier to entry can quickly generate revenue, but as the business grows, operation-based billing can become a growth tax; n8n's initial learning curve is steep, but its marginal cost is extremely low, making it suitable for carrying AI-native workloads over the long term. Calculating at a $200 hourly rate, the author estimates the extra 6 hours of learning on n8n equals $1,200 in opportunity cost—a reminder that teams should weigh decisions based on their current stage and expected growth curve.

Time cost is also a cost

There's one easily overlooked variable: learning time. At $200 per hour, spending 6 extra hours learning n8n equals $1,200 in opportunity cost. The author is candid about this—it depends on what stage of automation you're at. Early on, using Make to quickly get workflows running and generate revenue while slowly upgrading skills is a perfectly reasonable trade-off.

But if your system depends on LLM calls, vector databases, dynamic routing, and multi-step Agents, Make's operation-based billing model will eventually become a growth tax. n8n's edges are rough, but its design won't punish you for making your system bigger. Choose the tool that fits your current complexity, but build your architecture toward the platform with no ceiling.

One-line Summary

47 hours testing 10 AI workflows: n8n wins on unlimited executions and AI-native capabilities, Make wins on ease of use but has an obvious billing ceiling.

Paragraph Summary

The author spent 47 hours building the same 10 AI-native workflows (lead scoring, proposal generation, etc.) on n8n and Make, finding that Make is quick to learn but charges by operation—loops and multiple LLM calls in AI workflows cause operation counts to explode, with lead scoring consuming 34 operations per lead and the 10,000-operation cap running out before 300 leads; plus HTTP timeouts and other errors cause entire scenarios to fail. n8n has a steep learning curve, but its $20 cloud plan offers unlimited executions, AI Agent nodes can connect directly to vector stores, proposal generation completes in under 90 seconds, and error handling is more fine-grained. The author ultimately migrated all 10 core workflows to n8n, with a yearly cost difference of up to $840, but acknowledges that Make remains a reasonable choice for quickly generating revenue in low-volume, simple scenarios.