What AI actually costs your business: real 2026 pricing

“How much does it cost to add AI to my business?” is the question we get asked most at Evicron, an AI and custom software studio based in Barcelona with 200+ projects delivered across 12 industries since 2019 — and the one fewest providers answer with a real number instead of an undeveloped “it depends.” It does depend, but on specific factors that can be laid out before the first meeting. This guide covers realistic price ranges by type of applied AI project, what moves the cost within each range, and the four-phase process any serious quote should follow.

Why “implementing AI” isn’t one thing

The same label covers very different projects: subscribing to ChatGPT Enterprise for the team, building a chatbot that answers common questions using the company’s own documentation, or deploying an agent that queries the CRM, books appointments and triggers actions across several systems at once. The first is a software purchase billed per user per month; the other two are applied AI development, quoted as a fixed budget scoped to the project. Confusing the two is the single most common reason an SME gets a price shock halfway through.

Realistic price ranges by project type

As a reference point for what we work with at Evicron before closing a quote:

• Applied AI (agents, automations and language-model integrations built on the company’s own data): starting at €6,000. This is the widest range, because the ceiling depends on how many integrations and how complex the workflow is, not just which AI model sits behind it.
• Team AI training: starting at €1,500 per day, so the team gets real value out of the tools it already has, either before or alongside building something custom.

Within the applied AI range, a chatbot answering FAQs from a knowledge base (RAG) built on company documentation sits at the low end; an agent that also queries an external CRM or ERP, books slots on a shared calendar and executes actions in real time — like the voice agents we build at Elop — climbs depending on how many systems need connecting and how reliable each action has to be. In every case, the quote is only closed after a free discovery phase: any figure given before that step is a starting point for negotiation, not a commitment.

What moves the price within each range

Three factors explain most of the variation between a “cheap” and an expensive AI project inside the same range:

• Quality and volume of the company’s own data. An agent that answers well needs a clean, up-to-date knowledge base. If a company’s documentation is scattered across PDFs, emails and loose spreadsheets, cleaning it up before connecting it to a model is real work, even if it never shows up in the demo.
• Number of integrations with existing systems. Connecting an agent to a CRM, an ERP, a calendar or a phone system multiplies the testing needed compared with a chatbot that only answers isolated text queries.
• Real-time voice versus text. An agent that answers or makes real phone calls — like Elop’s — needs minimal latency plus automatic transcription and scoring of every conversation, adding a technical layer a text-only chatbot doesn’t need.

What almost no provider mentions: upkeep

An AI agent isn’t a build-and-forget project: models get new versions, API pricing shifts, and real user questions eventually surface gaps that never showed up in initial testing. A quote with no minimum tuning phase built in after launch usually looks cheaper on paper and gets more expensive six months later, once the agent has been giving mediocre answers for a while with nobody reviewing it since handover.

The 4-phase process

At Evicron we follow the same structure whether the project is a simple chatbot or an agent with complex integrations:

1. Discovery (free): we define exactly which process gets automated, which systems need connecting, and the final quote gets locked in.
2. Weekly sprints from day one: development moves forward with visible deliverables every week, not a single milestone at the end.
3. A working demo in week 2: the client tests a functional version of the agent or chatbot, not a mockup or a description of what it will eventually do.
4. Handover with the client’s own code, no lock-in: the client owns the prompts, workflows and integrations, and can take them to another team if needed.

Questions to ask before signing an AI quote

• Does the quote include a tuning phase after launch, or does it end the day the agent “works” in the demo?
• Who owns the prompts, the knowledge base and the integrations once the project is delivered?
• What happens if the underlying AI model’s API pricing goes up mid-contract?
• Does the provider explain exactly which company data the agent uses and where it’s stored?
• When will I see a working demo with my own data, not a generic example?

If your company already pays for generic AI licenses and wants to move to something that automates a specific process, our guide to AI consulting covers in more detail when it’s worth hiring one and what to ask before signing. And if what you need is custom software with no AI component, we cover the same pricing breakdown in our custom software cost guide.

Bottom line

Implementing AI in a business runs from €6,000 for a simple agent with limited integrations up to higher figures depending on how many systems need connecting and how reliable each action has to be, with team training starting at €1,500 per day. A quote only makes sense after a free discovery phase that defines the real scope, and it should always include a tuning phase after launch: an AI agent nobody reviews after delivery goes stale within months.

Want a real price range for your AI project? At applied AI we start with a free discovery call and reply within 24 hours. Get in touch and we’ll give you an actual figure, not a generic rate.