What a custom AI agent costs to build for a B2B company

Building a custom AI agent for a B2B company in 2026 typically runs between €5,000 and €45,000, depending on complexity, the number of integrations and how much autonomy the agent has. A simple single-task agent starts around €5,000 to €10,000. A complex multi-agent system wired into internal systems with its own RAG infrastructure starts at €18,000 and goes up. On top of that, expect monthly running costs between €150 and €1,500 depending on volume.

These ranges are an orientation for the SK/CZ market in 2026, not a price list. A concrete quote comes from a scope that can only be described after analysis.

What sets the final price

The cost of an AI agent is not a single number. It is made up of several variables that companies usually do not know when they first contact a supplier.

Scope of the agent's autonomy

The difference between these levels is covered in what an AI agent is and how it differs from a chatbot.

Integrations with existing systems

Every integration with an ERP, CRM, internal API or legacy database adds time for analysis, data mapping and testing. Companies with a clean, documented API layer pay considerably less than companies with legacy systems and no API access. In that case a middleware layer often has to be built first, which can raise the budget by 20 to 40 per cent.

Data infrastructure (RAG)

If the agent is to work with internal documents or a knowledge base, it needs a RAG architecture: a vector database, a document processing pipeline and a mechanism for keeping the data current. This is a separate budget line, typically €3,500 to €12,000 depending on the volume and variety of the source data.

Compliance and security

B2B companies in the EU need a solution that satisfies GDPR and the relevant requirements of the EU AI Act, particularly where personal or sensitive data is processed. An on-premise or EU-hosted deployment instead of a standard cloud API raises the price, but is often unavoidable for healthcare, legal and financial services. What exactly has to be handled is covered in the security risks of deploying an LLM.

Running costs after go-live

This is the line suppliers often leave out of the conversation:

• LLM API costs – you pay per token, and the cost rises with how much the agent is used.
• Hosting and monitoring – particularly for agents with their own vector database.
• Maintenance and prompt tuning – the agent is tuned continuously against real usage.

A custom agent versus no-code automation

No-code tools such as Zapier, Make or n8n are cheaper to start with, in the low hundreds of euros a month, but they hit limits as a company grows: constrained decision logic, awkward scaling at high volume, weaker security for sensitive data, and dependence on someone else's platform. A custom agent costs more up front, but the company owns the architecture, the data and the logic. Over time that pays off for processes that are business-critical or demand strict control over data. The same question at a wider scope is covered in custom software versus off-the-shelf SaaS.

How to reduce the cost without reducing quality

• Start with one specific use case rather than a full multi-agent system. Prove the value, then scale.
• Use the data infrastructure you already have instead of building a new RAG pipeline from scratch, where that is possible.
• Define the exact boundaries of the agent's task before the project starts. An unclear scope is the most common reason budgets are exceeded.

If you are comparing quotes right now, how to choose a supplier covers the rest.