How much does n8n actually cost vs OpenClaw?

Two of the most common setups for running AI agents: n8n and OpenClaw. They solve different problems, but both hit the same Anthropic or OpenAI bill at the end of the month.

Here is how the costs actually compare.

How they differ architecturally

n8n is a workflow automation platform. You build visual pipelines. Each node that calls an LLM is a discrete API call. Context does not persist between workflow runs by default.

OpenClaw is a persistent agent runtime. It loads workspace context every turn, runs heartbeats on a schedule, and maintains memory across sessions.

The cost profile is completely different.

n8n token usage

In a typical n8n AI workflow:

• Each LLM node call is independent. No shared session context.
• Context per call is usually small: the node's input data plus a short system prompt.
• A 5-node pipeline that each call an LLM = 5 API calls per workflow run.
• No heartbeats. No background agents. Runs are triggered externally.

Typical per-turn token usage: 1,000 to 2,000 tokens input, 500 to 1,000 tokens output. Low context overhead because there is no persistent workspace.

But n8n workflows run frequently. If you trigger a workflow 100 times per day with 3 LLM nodes each, that is 300 API calls per day.

At 1,500 tokens input and 600 tokens output per call, on GPT-4o ($2.50/$10 per MTok):

• Input: 300 * 1,500 / 1,000,000 * $2.50 = $1.13/day
• Output: 300 * 600 / 1,000,000 * $10 = $1.80/day
• Total: $2.93/day = $88/month

No heartbeat overhead. No workspace context overhead. You pay for exactly what you run.

OpenClaw token usage

OpenClaw has higher per-turn overhead due to persistent context. Every turn loads workspace files: SOUL.md, MEMORY.md, AGENTS.md, HEARTBEAT.md, skill descriptions. Roughly 4,000 to 9,600 tokens before the user's message.

Plus heartbeats. At 30-minute intervals on a single channel, that is 48 additional API calls per day, each loading the full workspace context.

At 50 turns/day, 3,000 tokens/turn, on Claude Sonnet 4 ($3/$15 per MTok):

• Main turns: 50 * (4,000 + 3,000) / 1,000,000 * $3 = $1.05/day input + output
• Heartbeats: 48 * 6,000 / 1,000,000 * $3 = $0.86/day input
• Total: roughly $1.91/day = $57/month

Lower than the n8n estimate above, but with a different structure. The fixed overhead is high; the per-message cost is more predictable.

Where each platform hurts you

n8n: Costs scale with workflow execution volume. A high-trigger workflow (webhooks, polling, frequent cron) can rack up hundreds of API calls per hour. No heartbeat overhead, but no natural throttle either.

OpenClaw: Costs scale with workspace context size and heartbeat frequency. A heavy MEMORY.md or aggressive heartbeat schedule adds fixed daily cost regardless of actual usage. The overhead is predictable but unavoidable.

Which is cheaper?

It depends on what you are building.

If your use case is high-frequency, low-context automation, n8n wins. Webhook triggers, data transformation, simple classification. Each call is cheap and isolated.

If your use case is a persistent assistant with memory, scheduling, and multi-channel support, OpenClaw is purpose-built for it. The overhead buys you features that n8n does not have natively.

Running a side-by-side comparison at similar workloads:

• 100 workflow runs/day, 3 LLM nodes each on GPT-4o: ~$88/month (n8n)
• 50 agent turns/day, 30-min heartbeats on Sonnet: ~$57/month (OpenClaw)

OpenClaw is cheaper here, but the n8n workload is doing 300 LLM calls vs 50 turns. Normalize for actual work done and the costs are closer than they look.

The optimization playbook by platform

n8n:

• Use gpt-4o-mini for classification and simple transformation nodes. Save gpt-4o for generation.
• Cache workflow outputs where possible. Do not re-run expensive chains on identical inputs.
• Set execution rate limits on polling triggers. Every extra poll is an API call.

OpenClaw:

• Switch heartbeat model to Haiku. Classification at Opus prices is the biggest waste.
• Trim workspace files. Every token in MEMORY.md costs you on every heartbeat and every message.
• Increase heartbeat interval if you do not need sub-30-minute response time.

Tools

Use the framework comparison page to see estimated costs for n8n, OpenClaw, CrewAI, LangChain, and AutoGPT side by side. Or model your specific setup in the Clawback calculator.

For OpenClaw configs, paste your openclaw.json into the Config Analyzer for exact per-line cost breakdowns.