n8n vs Zapier vs Make for LLM workflow automation

When you stack n8n vs Zapier vs Make LLM automation side by side, the decision comes down to who owns the runtime, how much code you’re willing to write, and what your per-step economics look like at scale. All three can trigger an LLM call from a webhook or a database row, but they diverge hard on branching logic, self-hosting, and throughput ceilings.

Capabilities

n8n

n8n is open-source (MIT for community edition) and gives you a Node-based editor plus full code nodes. You can write JavaScript or Python, call any HTTP endpoint, and persist state in workflow variables. For LLM work, the HTTP Request node or the dedicated OpenAI node covers most needs, but the real power is orchestration: loop over a dataset, branch on token counts, and merge results without leaving the canvas.

// n8n Function node: call an OpenAI-compatible chat endpoint
const response = await fetch('https://api.openai.com/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.OPENAI_API_KEY}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: 'Extract keywords from: ' + $json.input }]
  })
});
const data = await response.json();
return [{ json: { keywords: data.choices[0].message.content } }];

n8n handles RAG-style pipelines well: ingest a doc, chunk it in a code node, embed via batch HTTP, and upsert to a vector DB using the same canvas. Tool calling is just JSON parsing. Streaming responses are not native in the UI, but a code node can consume an SSE stream if you need it.

Zapier

Zapier shines on app coverage (6,000+ integrations) and zero-infra setup. Its “OpenAI” action or “Code by Zapier” step can invoke a model, but you’re boxed into Zapier’s step model: each LLM call is a task, loops are paid multi-tasks, and complex branching requires paid Paths. You can run a JavaScript or Python snippet in a Code step, but execution time is capped and you can’t import arbitrary packages.

# Zapier Code step (Python) - limited to standard library + requests
import requests
resp = requests.post(
  'https://api.openai.com/v1/chat/completions',
  headers={'Authorization': f'Bearer {input_data["api_key"]}'},
  json={'model':'gpt-4o-mini','messages':[{'role':'user','content':input_data['text']}]}
)
return {'output': resp.json()['choices'][0]['message']['content']}

Zapier has no native vector store connector, so RAG means chaining a Code step with an external API. Its strength is triggering on SaaS events (new Salesforce lead, new Calendly invite) and writing back without writing glue code.

Make

Make (formerly Integromat) uses scenarios with modules and visual routers. It’s more flexible than Zapier for data transformation and error handling, and its HTTP module can talk to any LLM API. You get data stores for scratch state and the ability to export scenario blueprints as JSON, but it remains a closed SaaS with operation-based metering.

Make supports iterators and aggregators that suit batch embedding jobs, and its error directives let you retry a failed LLM call with exponential backoff configured visually. Still, you cannot run a persistent worker or custom middleware inside the platform.

Price / Cost Model

n8n’s community edition is free if you self-host. n8n Cloud charges a flat monthly subscription with execution allowances; you are not billed per LLM call. Zapier bills per task—a single LLM workflow that reads a row, calls the model, and writes back is three tasks, and a loop over 100 items is 300 tasks. Make bills per operation; every module execution counts, so a similar loop costs 100+ operations. At high LLM volumes, Zapier’s task model gets painful fast; Make is cheaper but still metered; n8n is effectively fixed-cost at self-hosted scale.

Do the math for a daily batch: 10,000 support tickets summarized nightly. Zapier: 30k tasks/day = ~900k/month, likely enterprise tier. Make: ~20 ops per ticket = 200k operations/day, mid-tier. n8n self-hosted: zero marginal platform cost, just compute and model tokens.

Latency / Throughput

Platform overhead matters. A self-hosted n8n instance on a VPC talks directly to the model endpoint; round-trip is dominated by the LLM provider. Zapier and Make add a platform hop, queueing, and concurrency limits that can serialize your calls. If a provider rate-limits you mid-workflow, Zapier’s native OpenAI action simply fails. Routing through an OpenAI-compatible gateway that supports automatic fallback across providers—such as n4n.ai’s endpoint covering 240+ models with per-token metering—keeps the scenario moving, but that’s a design choice independent of the orchestrator.

Concurrency caps: Zapier tier limits concurrent Zaps; Make limits concurrent scenarios; n8n self-hosted is bounded only by your worker count and database connections. For LLM inference, where a single call may take 2–20 seconds, serialization kills throughput. n8n’s queue mode lets you spin multiple workers; the SaaS tools make you pay to raise the cap.

Ergonomics

n8n feels like a lightweight IDE with a canvas. You see raw JSON, can pin test data, and version the export in Git. Zapier is the most approachable for non-engineers: pick a trigger, map fields, done. Make sits between: visual but with formula language and blueprint JSON for diffs. For an engineer maintaining LLM pipelines, n8n’s code nodes and expression editor beat clicking through Zapier’s modal maze.

Zapier’s mapper is friendly until you need to transform a nested JSON array from an LLM response—then you’re in a Code step anyway. Make’s formula language handles mapping elegantly but has a learning curve. n8n expresses transformation as {{ $json.foo }} or a full function; both are greppable.

Ecosystem

• n8n: 400+ native nodes, community node repo, self-host or cloud.
• Zapier: 6,000+ app connectors, largest marketplace.
• Make: 1,000+ apps, rich module types (routers, aggregators).

If your workflow is “Google Sheet → LLM → Slack,” all three qualify. If you need a niche internal API, n8n’s HTTP + code or Make’s HTTP module win; Zapier may lack the connector. n8n’s community publishes nodes for Pinecone, Supabase, and LangChain-style tools, which matters for LLM stacks.

Limits

Zapier: task caps per plan, 30-second timeout on Code steps (or 80s on higher tiers), no self-host, no arbitrary package imports. Make: operation quotas, scenario size limits (max modules), 40-second HTTP timeout default. n8n: community edition lacks advanced auth, queue mode, and SSO; you own uptime and scaling.

The n8n vs Zapier vs Make LLM automation debate often starts with pricing but ends with these hard limits. A Zapier plan that looks cheap at 1k tasks/month breaks at 100k. Make’s operation count hides the fact that a single LLM retry doubles your bill. n8n pushes the limit onto your Kubernetes cluster—predictable for infra people, scary for ops-averse teams.

Comparison Table

Which to Choose

Prototype with a non-technical team: Zapier. If the job is “watch a Typeform, summarize with GPT, post to Slack,” its UI gets you live in an hour. Accept the task-based cost and the fact that you’ll rewrite it if volume grows.

Complex visual orchestration with some code: Make. When you need routers, error handlers, and data stores without managing servers, Make’s scenarios scale better than Zapier and stay cheaper per step. Good for agencies shipping client LLM bots.

Engineering-owned LLM pipelines at scale: n8n self-hosted. You get loops, code nodes, and no per-call tax. Pair it with a model gateway if you need fallback—routing through an OpenAI-compatible endpoint that honors cache-control and provides automatic provider failover removes a class of incidents.

High-volume, multi-provider LLM calls: n8n plus a gateway. At thousands of summaries per minute, the fixed cost of n8n workers beats metered tasks, and decoupling model routing from the workflow logic keeps your canvas clean.

Pick the orchestrator that matches who will maintain it at 3 a.m., not the one with the smoothest demo.