The Core Comparison: Platform Pricing Models at Scale

The fundamental difference in pricing lies in what each platform counts: Zapier counts “Tasks,” Make counts “Operations,” and n8n counts “Executions.” Understanding this distinction is the first step to avoiding overage fees. In this section of our Zapier vs Make vs n8n cost comparison 2026, we’ll break down how a single workflow can result in vastly different costs across the three platforms.

Zapier (Task-Based)

Zapier’s model is built around “Tasks.” A task is counted every time a Zap successfully performs an action. For example, if you have a 10-step Zap that runs 100 times, you consume 1,000 tasks. This model is intuitive but can become expensive as workflows grow in complexity. Zapier pricing is structured to scale with usage, meaning that sophisticated multi-step automations—common in enterprise environments—can quickly push you into higher pricing tiers.[5]

Make (Operation-Based)

Make.com (formerly Integromat) uses an “Operations” model. An operation is counted for every individual action a module performs. This is more granular than Zapier’s task model. A single trigger or action might consume multiple operations depending on the data processed. While make.com pricing often appears lower for entry-level tiers, complex scenarios that iterate through large datasets can burn through operations faster than expected.[6]

n8n (Execution-Based)

n8n counts “Executions.” An execution is a single run of a workflow, regardless of how many steps or nodes it contains (with some exceptions for specific looping or data-splitting nodes). This model is particularly powerful for complex, multi-step processes where a single trigger initiates a long chain of events. n8n pricing favors complex workflows, as you are not penalized for adding more steps to a single process.[7]

Cost Simulation: 10,000 Workflow Runs Per Month (2026)

To visualize the difference, we have modeled the cost for a standard mid-complexity workflow running 10,000 times per month.

Platform Pricing Model Approx. Monthly Cost for 10k Runs Best For…
Zapier Per Task ~$250 – $400 (Professional Tier) Simple, linear workflows & non-technical teams
Make.com Per Operation ~$100 – $180 (Core/Pro Tier) Complex logic & visual data mapping
n8n (Cloud) Per Execution ~$50 – $120 (Pro Tier) heavy-duty data processing & developers
n8n (Self-Hosted) License + Infra $0 (License) + ~$274 (Infra/Labor) Enterprise security & full data control

Note: Costs are estimates based on standard 2026 pricing tiers. “Runs” assumes a 5-step workflow. For Zapier, this equals 50,000 tasks. For Make, roughly 50,000 operations. For n8n, 10,000 executions.

While the table provides a baseline, the real cost emerges when integrating generative AI. The next section will analyze the hidden API costs that are rarely included in these comparisons.

AI Gap #1: The Hidden Cost of AI Tokens & “Token Inflation”

AI overviews and competitor blogs often show you the subscription cost, but they ignore the single largest variable: the cost of the AI model itself. Every call to GPT-4o or Claude 3.5 Sonnet consumes tokens, and this cost is passed directly to you. We’ll explain the concept of “Token Inflation”—how chat history context windows can exponentially increase your input token costs over time, leading to massive, unforeseen bills.

Deep Dive: Cost Scenario

Let’s look at a practical scenario: “Calculating the cost of automating 1,000 customer support tickets per month.”

In this scenario, we assume the following:

  • Volume: 1,000 tickets per month.
  • Average Ticket Length: 500 input tokens (the customer’s query).
  • Average Response Length: 250 output tokens (the AI’s answer).
  • Context Overhead: A growing chat history adds an average of 1,000 tokens to the input per interaction to maintain context.

Calculation for GPT-4o

Using openai api pricing estimates for early 2026 (~$5.00 per million input tokens / ~$15.00 per million output tokens)[3]:

  1. Input Cost:
  • (1,000 tickets * (500 prompt + 1,000 history tokens)) = 1,500,000 input tokens.
  • 1.5 million tokens * $5.00 = $7.50
  1. Output Cost:
  • (1,000 tickets * 250 output tokens) = 250,000 output tokens.
  • 0.25 million tokens * $15.00 = $3.75
  1. Total Monthly API Cost: $7.50 + $3.75 = $11.25

Calculation for Claude 3.5 Sonnet

Using claude 3.5 sonnet pricing which is generally structured to be competitive (estimates based on [Anthropic API Pricing Page][4]):

  1. Input Cost:
  • 1.5 million tokens * ~$3.00 (Example rate) = $4.50
  1. Output Cost:
  • 0.25 million tokens * ~$12.00 (Example rate) = $3.00
  1. Total Monthly API Cost: $4.50 + $3.00 = $7.50

While $11.25 sounds negligible, apply “Token Inflation” to a chatbot that handles 10 turns per ticket. The input context grows with every turn. By turn 10, you might be sending 10,000 tokens of history per call. Suddenly, that $11.25 monthly cost can balloon to $100+ per month for the same number of tickets.

Why Calculators Fail

This dynamic cost is why a simple llm token cost calculator isn’t enough. Businesses, especially in tech hubs like Austin, need to model costs based on scaling conversation histories. This is a critical budgeting blind spot that AI-generated summaries cannot account for, as they lack the context of real-world application and data flow. The cost of running gpt-4o is not static; it is a variable that scales with the verbosity of your customers and the memory of your bot.

AI Gap #3: The “Free” Trap: Real Cost of Self-Hosting n8n

The promise of a “free” and powerful automation platform with n8n is compelling, especially for bootstrapping startups. However, “free” only applies to the software license. The Total Cost of Ownership includes tangible infrastructure expenses and, more importantly, the cost of human labor for maintenance, security, and updates.

Itemized Cost Breakdown

1. Server Costs

A basic, reliable server is not free. You cannot run a production-grade automation server on a laptop. A standard DigitalOcean Droplet suitable for moderate workloads (2 vCPU, 4 GB RAM) starts around $24/month.[8] This provides the necessary uptime and processing power for n8n self hosting cost calculations.

2. Maintenance Labor (The Human Cost)

The server requires setup, updates, troubleshooting, and security patching. A conservative estimate is 5 hours per month for these tasks.

Using averaged us tech salary data for a DevOps specialist in a market like Austin (~$50/hr freelance rate), this translates to:

  • 5 hours/month * $50/hour = $250/month.

3. Security & Compliance Overhead

Ensuring your self-hosted instance meets security standards is a non-trivial task. Adhering to frameworks like the NIST AI Risk Management Framework[2] and ethical guidelines from the IEEE[1] requires expertise and time, which carries an implicit cost. Neglecting this can lead to data breaches, which are far more expensive than any subscription fee.

The Real Cost of “Free”

When you sum these figures, the picture changes:

  • Server: $24
  • Labor: $250
  • Total Monthly Cost: $274

This calculation reveals that for many SMBs, a paid cloud plan from n8n or even Make.com can be more predictable and cost-effective than managing a self-hosted instance, especially when factoring in the opportunity cost of developer time.

Frequently Asked Questions

Is n8n cheaper than Zapier for enterprise?

For enterprise scale, n8n is almost always cheaper than Zapier in raw software cost. Zapier’s enterprise pricing is task-based and can run into thousands per month for high volumes. An n8n self-hosted instance has no software cost, only infrastructure and labor, while its enterprise cloud plan is typically more cost-effective than Zapier’s equivalent tier. However, enterprises must factor in the cost of dedicated DevOps support for n8n.

How much does it cost to self-host n8n?

The true cost to self-host n8n is a combination of server fees and labor, typically starting around $274/month. This includes approximately $24/month for a basic cloud server (like a DigitalOcean droplet) and an estimated $250/month for 5 hours of skilled developer time for setup, maintenance, and security updates. The software itself is free, but the operational costs are not.

Zapier vs Make vs n8n cost comparison 2026

In 2026, the Zapier vs Make vs n8n cost comparison 2026 breakdown is: Zapier is the most expensive at scale due to its per-task model. Make.com is a mid-range option with granular per-operation pricing. n8n is the cheapest, with a free self-hosted version (plus infrastructure costs) and affordable cloud plans. Your final cost depends on workflow volume, complexity, and whether you have the technical resources to manage your own server.

Hidden costs of AI automation agencies

The primary hidden costs of AI automation agencies are ongoing retainers, platform subscription markups, and variable API usage fees. Many agencies bill for maintenance and support even if no changes are made. They may also bundle the cost of Zapier or Make into their fee at a higher rate. It is advisable to ask for a clear breakdown of their service fee versus the direct costs of the tools and AI models being used.

OpenAI API pricing vs Claude 3.5 Sonnet cost

OpenAI’s GPT-4o and Anthropic’s Claude 3.5 Sonnet have different pricing structures. As of early 2026, GPT-4o’s pricing is approximately $5.00 per million input tokens and $15.00 per million output tokens. Claude 3.5 Sonnet is generally more cost-effective for similar tasks, priced lower for both input and output. Always check their official pricing pages for the latest rates, as these change frequently.

Is Make.com free tier enough for small business?

Make.com’s free tier is excellent for testing but is often insufficient for a small business’s daily operations. The limit of 1,000 operations per month can be exhausted quickly by just a few active, multi-step scenarios. It’s best used for very low-volume tasks or to build and validate workflows before upgrading to a paid plan like the Core tier.

How to calculate LLM token costs for automation?

To calculate LLM token costs, you must sum the input and output tokens for each API call and multiply by the model’s price per token. The formula is: (Total Input Tokens Price per Input Token) + (Total Output Tokens Price per Output Token). Remember that “input tokens” includes not just your prompt but also any chat history or context provided in the call.

Best AI automation tools for freelancers 2026

For freelancers in 2026, Make.com and n8n (Cloud) offer the best balance of power and affordability. Make.com’s Core plan provides significant capabilities at a low entry price. n8n’s starter cloud plan is also very competitive. Zapier’s free and starter tiers are often too restrictive, making it less ideal for freelancers who need to build robust workflows for clients without a large budget.

Zapier task limit workaround

A common workaround for Zapier’s task limits is to consolidate steps using built-in tools like “Code by Zapier” or “Formatter.” By writing a small JavaScript or Python script, you can perform multiple data transformations in a single task. Another strategy is to use webhooks to send data to another service (like n8n) that can process it more cheaply before sending a result back to Zapier.

Cost of running GPT-4o in n8n workflows

The cost of running GPT-4o in n8n is solely the OpenAI API fee; n8n itself does not add a surcharge. You pay n8n for the workflow execution (on cloud plans) or server costs (if self-hosted), and you pay OpenAI directly for the token consumption. This separation makes it a very transparent and often cheaper way to integrate powerful AI models compared to platforms that bundle AI into their pricing.

Limitations, Alternatives & Professional Guidance

The cost calculations in this article are estimates based on specific scenarios and publicly available data as of January 2026. Actual costs will vary based on your workflow complexity, data volume, and future price changes from these platforms and API providers. Token costs are particularly volatile and can increase with more complex AI interactions. These models should be used as a budgetary guide, not a final quote.

Beyond these three platforms, consider alternatives like custom Python scripts using services like AWS Lambda for highly specific, high-volume tasks, which can be cheaper but require significant development expertise. Other platforms like alternatives like Pabbly Connect or Integrately offer competitive pricing, though often with a smaller library of integrations. The best approach depends entirely on your team’s technical skills and specific integration needs.

If you are projecting over 100,000 tasks/operations per month or require complex, mission-critical workflows, it is wise to consult an AI automation expert. They can help you architect a scalable system, accurately forecast costs, and determine whether a managed platform or a custom-built solution is the right long-term investment for your business.

Conclusion

Choosing the right automation tool in 2026 requires looking beyond the sticker price. Zapier offers simplicity at a premium, Make.com provides granular control for a moderate cost, and n8n delivers unparalleled power for those willing to manage the technical overhead. The most critical takeaway is that scaling with AI demands a budget for both the platform and the variable cost of API tokens. Ultimately, the best Zapier vs Make vs n8n cost comparison 2026 is one that includes all these factors.

Understanding the financial cost of automation is the first step. The next is understanding its impact on your team and workflow. As you integrate these powerful tools, it’s important to consider the human element and the risk of over-reliance on AI. To explore this further, consider reading our analysis on the signs of AI dependency. Learn more in our guide: ChatGPT Overload! When Does Daily Use Turn Into Addiction?

References

  1. IEEE Standards Association – Autonomous Intelligence Systems Standards
  2. NIST AI Risk Management Framework
  3. OpenAI API Pricing
  4. Anthropic API Pricing
  5. Zapier Pricing
  6. Make.com Pricing
  7. n8n Pricing
  8. DigitalOcean Droplet Pricing