An AI agent is a system capable of receiving instructions, making decisions, and performing tasks autonomously or semi-autonomously. Unlike a simple chatbot that answers predefined questions, an agent can connect to databases, use external tools, generate documents, send emails, or interact with other applications.
Interest in these systems grew significantly in 2024 and 2025, driven by platforms such as n8n, LangChain, OpenAI Assistants, CrewAI, and other alternatives that make it easy to build them without writing code from scratch.
The problem is that many people start exploring this technology without a clear idea of the actual costs. It is assumed that “using the ChatGPT API” is cheap, or that setting everything up in the cloud is free. In practice, costs depend on several factors that should be understood before committing to an architecture.
To better understand how the benefits of AI for entrepreneurs can translate into concrete and profitable projects, it is useful to first know what it entails, technically and financially, to build one of these agents.
The components that determine the price of an AI agent
There is no single price. The total cost of an AI agent is built up from several layers:
1. The language model (LLM)
This is the engine that processes language and generates responses. The most commonly used are:
- OpenAI GPT-4o: approximately $5 per million input tokens and $15 per million output tokens (approximate prices, subject to change)
- Claude 3.5 Sonnet (Anthropic): similar or slightly higher prices depending on the plan
- Gemini 1.5 Pro (Google): pricing scheme per million tokens with free tiers for low usage
- Open-source models (Llama 3, Mistral, DeepSeek): no license fee, but require your own infrastructure to host them
Model costs depend on usage volume. An internal agent processing 100 queries per day can cost less than $10 per month. One serving thousands of users can exceed $500 per month in token consumption alone.
2. The platform or development framework
This defines how the agent is orchestrated: what tools it can use, how it chains actions, how it manages memory and context.
The main options are: If the platform is self-hosted, the cost of the platform itself is zero, but the cost of the server is added.
n8n (self-hosted or in the cloud): from free in the self-hosted version to paid plans on n8n Cloud
LangChain / LangGraph: open source, no license fee, but requires development
OpenAI Assistants API: included in the API cost, with file storage at an additional charge
Make (formerly Integromat): starting at $9/month for basic automations
Flowise or Botpress: open source, self-hosted
3. Integrations and external tools
A useful agent almost always needs to connect to external services: email, CRM, databases, calendars, third-party APIs. Each integration may have its own cost:
- Google Workspace: starting at $6/user/month
- HubSpot CRM: free version available, paid plans starting at $20/month
- Databases (PostgreSQL, Supabase, Pinecone for vectors): from $0 to hundreds of dollars depending on volume
4. Vector storage
Agents that need “memory” or access to their own documents use vector databases to retrieve relevant information. Options like Pinecone offer limited free plans, while hosting an instance of Qdrant or Weaviate on your own VPS may be more cost-effective in the medium term.
5. Initial development or setup
If you hire someone to build the agent, the cost varies significantly: A simple agent (answers questions about your own documents, connects to a spreadsheet) can be built in 10 to 20 hours of work. One with multiple coordinated agents, complex logic, and an admin panel may require 100 hours or more.
- Junior freelancer in Latin America: $20–$50/hour
- Freelancer specializing in AI: $50–$120/hour
- Specialized agency: quotes ranging from $2,000 to $30,000 or more, depending on complexity.
These figures assume the use of a third-party model via API. If you choose to host your own model, such as DeepSeek or Llama 3, fixed costs increase but variable costs per query disappear. For high-volume projects, the break-even point between using an API and hosting your own model typically falls around 50,000–100,000 tokens per day.
Hosting costs: an underestimated factor
One of the most common mistakes when calculating the cost of an AI agent is ignoring hosting. If the agent runs on a third-party cloud (such as n8n Cloud or Flowise Cloud), that cost is already included in the subscription. But if you choose to self-host, you need a server capable of running the framework, the vector database, and, in some cases, the model itself.
What kind of server do you need?For projects using n8n or Flowise connected to external APIs, a mid-range VPS hosting is more than sufficient. Neolo offers VPS servers with consistent uptime, technical support provided by real people (no bots or automated responses), and affordable prices for SMEs and entrepreneurs. It’s a solid option for those who need control over the environment without the costs of a dedicated server.
- For an agent that orchestrates external tools (does not run the LLM locally): a VPS with 2–4 GB of RAM is sufficient to get started
- To host a small model such as Mistral 7B or quantized DeepSeek 7B: at least 16 GB of RAM is recommended, ideally with GPU support
- For larger models (70B): dedicated servers with GPUs are required
The monthly cost of a VPS suitable for this type of project ranges from $10 to $40 USD per month, depending on the allocated resources. If you sign up in advance (available for up to 3 years), you can get a significant discount on that price.
What hosting should you use with Docker?
If the agent is deployed in Docker containers, this video explains what type of hosting is best:
Common mistakes when budgeting for an AI agent
Underestimating the cost of tokens
The cost per token seems negligible at first. The problem arises when the agent includes long context in each query: attached documents, conversation history, extensive system instructions. In production, a single query can consume 3,000–10,000 tokens if the prompt isn’t optimized properly.
What really happens: many projects start with a cost of $5/month and end up paying $150/month when scaling because no one checked the average context size.
Confusing the prototype with the product
A prototype works on a laptop with Ollama and a few test documents. Taking that prototype to production involves addressing authentication, error handling, monitoring, backups, acceptable latency, and availability. That leap comes with technical and financial costs that should be anticipated.
Not considering maintenance
AI agents are not systems you set up once and forget about. Models change (APIs evolve, prices vary), prompts need tweaking, integrations break. Monthly maintenance can account for 15–25% of the initial development cost, depending on complexity.
Choosing an insufficient hosting plan
Hosting n8n or Flowise on a generic shared hosting plan can cause performance issues. These applications require specific ports, persistent processes, and, in some cases, Docker. A VPS is the most suitable option for this type of workload.
Not accounting for vector storage costs
If the agent uses embeddings to retrieve information from its own documents, vector storage is required. Pinecone, for example, charges from $0 (free plan with limitations) up to $70/month for its basic paid plan. Self-hosted Qdrant may be free in terms of licensing, but it requires server resources.
Little-known tips for reducing costs
Use prompt caching: Some providers like Anthropic and OpenAI offer discounts for cached prompts (parts of the context that repeat across queries). Enabling this feature can reduce token costs by 50% to 90% for reusable portions.
Choose the model based on the task: Not all queries require GPT-4o. A well-designed agent can use an economical model (such as GPT-4o mini or Claude Haiku) for simple classification or extraction tasks, and reserve the more powerful models only for final responses.
Limit the conversation history: Passing the entire conversation history in every turn multiplies token consumption. An effective strategy is to compress the history every few turns, keeping only a summary of previous exchanges.
Self-host the framework, not the model: Self-hosting n8n or Flowise on your own VPS is cost-effective and gives you full control. It’s not necessary to self-host the LLM to achieve cost benefits in the early stages. The model can remain an external API while the framework runs on your own infrastructure.
Monitor usage from day one: tools like LangSmith (for LangChain) or OpenAI’s dashboards let you see exactly how many tokens are consumed per query type. Without monitoring, costs spiral out of control silently.
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Frequently Asked Questions
How much does it cost to create an AI agent from scratch without knowing how to code?
With visual tools like n8n or Flowise, it’s possible to build a functional agent without writing code. The initial cost can be close to zero if you use the self-hosted version and a model with a free tier. The actual monthly cost would depend on hosting (starting at $10/month with a basic VPS) and the model’s API usage (which varies depending on usage).
Is it cheaper to use an open-source model than to pay for the OpenAI API?
It depends on the volume. For projects with low or moderate usage, the OpenAI API is usually more cost-effective because it doesn’t require dedicated infrastructure. Once query volume reaches a certain threshold, hosting your own model such as DeepSeek or Llama 3 can be more cost-effective. The break-even point varies, but is generally reached when monthly API spending exceeds $80–$100.
What hosting do I need to run an AI agent with n8n?
A VPS with at least 2 GB of RAM is sufficient for n8n with moderate workloads. If you’re also hosting a vector database or the orchestration framework is more resource-intensive, it’s advisable to scale up to 4–8 GB of RAM. Shared hosting isn’t suitable for this type of application because it doesn’t allow persistent processes or port configuration.
Does the cost of an AI agent scale linearly with the number of users?
Not necessarily. The main variable cost is token consumption, which depends more on the number of queries and their complexity than on the number of registered users. An active user making 50 queries a day can cost more than 10 users each making 2 queries. Server costs, on the other hand, scale with concurrent load.
What is a RAG agent, and how much more does it cost compared to a simple chatbot?
RAG (Retrieval-Augmented Generation) is an architecture that allows the agent to search for information in its own documents before generating a response. Compared to a simple chatbot, it adds the cost of vector storage and the embedding process (converting documents into numerical vectors). In terms of APIs, the cost of embeddings is low (OpenAI charges $0.02 per million tokens with the text-embedding-3-small model), but vector storage in the cloud can add up to $20–$70/month depending on volume.
Can I create an AI agent for my business for less than $50 a month?
Yes, for internal use cases or those with low query volumes. A typical setup might include: self-hosted n8n on a VPS (~$15/month), an API from an affordable model (~$10–$20/month with moderate usage), and basic storage. With good prompt optimization practices, it’s perfectly possible to stay under that budget.
How much does it cost to hire someone to build the agent?
A freelancer with experience in tools like n8n, LangChain, or Flowise can charge between $30 and $80/hour in Latin America. A functional agent of medium complexity requires between 15 and 40 hours of work, putting the development cost between $450 and $3,200. Agencies specializing in AI may charge considerably more, but they typically offer documentation, ongoing support, and greater quality assurance.
Conclusion
Creating an AI agent has a very wide range of costs, from nearly free projects to five-figure developments. What defines the price is not the technology itself, but the level of customization, the volume of use, the necessary integrations, and where everything is hosted.
For most SMEs and entrepreneurs, the smartest starting point is to build a simple agent, self-host the framework on your own server, and use the API of an external model. This allows you to keep costs low while validating whether the agent brings real value to the business.
For that infrastructure, a Neolo VPS hosting plan is a concrete option to consider: over 20 years in the market, a company funded by its own customers (without investment funds), fast-response technical support, and a 30-day money-back guarantee if the service doesn’t meet expectations. A solid starting point for projects that are just beginning to grow.