Introduction
Organizations in the present day depend on automation
systems and AI agent platforms to optimize their operations at scale. The
platforms differ from each other through their design methods which range
between visual construction of workflows and development of conversational AI
applications. This page assesses n8n, OpenAI Agent Platform, Google Opal and
Make.com through their pricing models, core functionality, hosting flexibility and
LLM options.
Pricing💰
The n8n platform provides unlimited workflows and steps and users to all plans while charging customers based on the number of workflow executions. The plans starts from $20 per month with 2,500 executions, up to $50 per month with higher execution limits. The Community Edition of n8n operates as a free version for users who want to host it themselves. There is a 50% discounted plan for startups with fewer than 20 employees and less than $5M revenue.
The OpenAI Agent Platform uses API-based token pricing
instead of subscription-based pricing for its services. The Standard model
pricing structure applies to GPT-5 models which cost between $2.50 and $10 per
million input tokens based on the model version.
The Google Opal service operates without cost during its
experimental US-only public beta phase as a Google Labs project. It is
difficult to predict its future pricing structure because no commercial rates
have been disclosed.
Make.com operates with an operations-based pricing system which includes a free plan that allows 1,000 operations per month. The platform offers different pricing plans which start with hobby use and progress to enterprise solutions through operation-based pricing instead of workflow-based pricing.
The n8n UI.
Hosting Options 🏠
n8n provides users with complete hosting flexibility since
it supports both self-hosted deployments and cloud-based options. The Community
Edition of n8n allows users to deploy n8n on their infrastructure on Docker or
VPS or Kubernetes systems for full control over their operations. All plans
from n8n support deployment through cloud hosting and self-hosting options for
users who want to keep their data more private.
The OpenAI Agent Platform operates exclusively in the cloud
through API requests that direct data to OpenAI's processing infrastructure.
Organizations need to transmit their data to OpenAI servers because there are
no available self-hosting options.
The entire system of Google Opal operates within Google
Cloud infrastructure. Users cannot host the web-based Google Labs experiment
because it only operates through opal.withgoogle.com and requires Google
account authentication.
Make.com operates as a cloud-based SaaS platform which does not support self-hosting capabilities. Users access Make.com through their web interface while the service runs all workflows on Make's infrastructure.
The OpenAI Agent Platform UI.
Core Features ⚙
n8n stands out through its 400+ pre-built integrations and
its visual node-based editor which supports complex logic, conditional
branching and custom JavaScript/Python code execution. The platform features
built-in database functionality and headless browser automation which makes
external service usage optional.
The OpenAI Agent Platform enables developers to access
OpenAI models through its API endpoints. Developers can create custom agents
through function calling and assistants API and retrieval-augmented generation
methods. The platform requires developers to write code for implementation
instead of using visual interfaces.
Users can describe workflow requirements through natural
language in Google Opal which then creates visual workflow designs
automatically. The platform allows users to work in both conversational and
visual modes while supporting file uploads and Google Drive connections and
YouTube URL processing. The platform includes pre-built templates which help
users complete common tasks and finished applications can be distributed
through Google account sharing.
The platform Make.com provides 1,500+ integrated applications through its visual scenario building interface. The platform provides simple access to users who lack technical skills yet offers complex features for data transformation and error management and scheduling capabilities. The platform added AI functionality to its recent software updates.
The Google Opal UI.
Flexibility (LLM Choices) 💪
The LLM flexibility of n8n stands out because it provides
users with access to a wide range of integration options. Users can access
OpenAI, Anthropic Claude, Google PaLM, Hugging Face models and custom
API endpoints through the platform. Organizations can select their preferred
LLM models for specific use cases because the platform supports multiple models
and allows seamless provider transitions without workflow reconstruction. The OpenAI
Agent Platform operates exclusively with OpenAI models from the GPT family.
The Google Opal platform operates exclusively with Google AI
infrastructure and models. The platform utilizes Google multimodal features but
users cannot add third-party LLM providers or select different models.
The AI modules of Make.com now support multiple providers but the level of LLM flexibility depends on the specific integration. The platform initially focused on app-to-app automation but AI features became part of its offerings after its initial launch.
The Make.com UI.
Conclusions ❗
These platforms operate for different functional requirements.
n8n provides technical teams with flexibility and self-hosting capabilities (with increased privacy and security), and LLM provider independence which makes it suitable for organizations needing complex automation and compliance solutions.
The OpenAI Agent Platform provides developers with access to advanced models through programmatic interfaces while requiring them to work within OpenAI's system framework.
The experimental phase of Google Opal provides non-technical users with easy AI application development for prototyping, yet its production readiness remains uncertain.
The platform Make.com provides businesses with user-friendly automation features and broad integration options while delivering average AI functionality.
The critical differentiators are control versus convenience.
Self-hosting and LLM choice make n8n most adaptable, while managed platforms (Make.com, Opal) lower technical barriers.
Organizations should weigh
infrastructure preferences, required integrations, LLM strategy, and team
technical depth when selecting their automation platform.
Book a call to learn more on how to choose between these platforms.
Watch videos of these platforms below.
n8n
OpenAI Agent Platform
Frequently Asked Questions (FAQ)
Q: What platforms are compared in this article?
A: The post compares four major platforms: n8n, OpenAI Agent Platform, Google Opal and Make.com. The comparison covers pricing models, core functionality, hosting/flexibility and support for large language models (LLMs).
Q: Why compare n8n vs OpenAI Agent Platform vs Google Opal vs Make.com?
A: Because businesses and automation practitioners need to choose the right automation/AI-agent platform based on their specific needs: whether visual workflow building, multi-LLM support, self-hosting, or enterprise scale. The post helps highlight where each tool excels or lags.
Q: What are key differentiators between these platforms?
A: Key differentiators include:
- Hosting/flexibility: whether you can self-host or require platform-managed environment.
- LLM options: support for models beyond one provider (e.g., OpenAI, Google, custom).
- Workflow automation vs true agentic behaviour (tool selection, memory, decision making).
- Visual builder and integration ecosystem: how easy it is for non-developers to build.
- Pricing and execution limits (e.g., workflow runs, agents, steps) as noted in the article.
Q: When is n8n the best choice?
A: n8n is ideal if you want: self-hosting or open-source flexibility, the ability to integrate many systems/tools, multi-LLM support, and you don’t mind a bit more technical setup or logic wiring. The article highlights n8n’s strength in integration and agentic work.
Q: When might OpenAI Agent Platform or Google Opal be better choices?
A: If you prefer a more managed, simplified interface, or you are already embedded in the provider’s ecosystem (OpenAI or Google) and value rapid deployment rather than maximum flexibility, these options may be more suitable. The article touches on how the platforms differ in ease of use and hosting model.
Q: What should I check before choosing an AI-agent or workflow platform?
A: You should consider:
- Do you need self-hosting or managed hosting?
- What LLMs and tools do you need to integrate?
- How many executions / workflows / agents will you run (pricing/execution limits)?
- How technical your team is (visual no-code vs low-code vs code).
- What integrations and automation logic you need (e.g., conditional logic, memory, tool chaining).
The article lays out these criteria for evaluation.
Q: Are there trade-offs between “agentic AI” platforms and standard workflow automation tools?
A: Yes. Agentic AI platforms (those labelled “agent” rather than “just workflow”) tend to focus on tool selection, memory/context management, reasoning and decision-making, whereas traditional workflow automation tools focus on triggers/actions/integrations. The article points out that if you need only “agent” behaviour (autonomous decision making) you may pick a different platform than if you just need “automate tasks”. However, the flexibility of automation platforms allows to connect any agent to specific workflow tasks.
Q: How does the article address pricing among these platforms?
A: It provides an overview of the pricing model for n8n (e.g., unlimited workflows/steps in plans, self-hosted community edition free) and discusses how pricing may differ across the platforms.
Q: Will these platforms lock me into a single AI model or provider?
A: It depends. Some platforms are tightly coupled to a provider’s model (e.g., OpenAI) whereas others (like n8n) emphasise model-agnostic flexibility and integrations with many LLMs. The article mentions the importance of avoiding vendor lock-in if flexibility is critical.
Q: Can non-technical users adopt these platforms easily?
A: The ease of adoption varies: platforms offering no-code visual builders are easier but may sacrifice flexibility; platforms like n8n offer more power but require deeper logic/flow setup. The article assesses these tradeoffs.
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