What Does It Cost to Add an AI Feature to Your Product? Real Numbers from Our Projects

Introduction

Every second client conversation we have at MG Software now includes the same question: "Can we add AI to this?" The answer is almost always yes. The follow-up question is harder: "What will it cost?" Most businesses have no frame of reference. They hear about ChatGPT, see competitors launching AI features, and assume it either costs a fortune or is basically free.

Neither is true. After integrating AI features into dozens of client projects over the past 18 months, we have concrete numbers. This article breaks down what AI actually costs in production, from API pricing to development hours, with real examples from our recent work.

The Two Costs Most Businesses Confuse

There are two completely separate cost categories for AI features, and mixing them up leads to bad decisions. The first is the development cost: building the feature, connecting the AI model, handling edge cases, testing, and deploying. This is a one-time investment, similar to building any other software feature. The second is the ongoing API cost: what you pay per request to the AI model provider every month.

Development cost depends on complexity and is measured in weeks of engineering time. API cost depends on usage volume and model choice, and is measured in euros per month. A feature can be expensive to build but cheap to run, or quick to build but expensive at scale. Understanding both is essential for making the right decision.

We will cover both in detail below, with actual numbers from our project calculator and from production deployments.

AI Model Pricing in April 2026: The Landscape

The AI model market has stratified dramatically. You are no longer choosing between "ChatGPT or not." There are now dozens of models at radically different price points, each optimized for different tasks. Here are the current rates for the models we use most at MG Software.

GPT-5.4 (OpenAI's flagship) costs $2.50 per million input tokens and $15 per million output tokens. It is the most capable general-purpose model for complex reasoning. GPT-5.4 mini runs at $0.40 per million input and $1.60 per million output, solid for customer-facing chat. GPT-5.4 nano costs just $0.05 per million input and $0.40 per million output, built for classification, routing, and high-volume tasks.

On the Anthropic side, Claude Sonnet 4.6 costs $3 per million input and $15 per million output, excellent for code generation and nuanced writing. Claude Haiku is the budget option at $0.25/$1.25 per million tokens. For businesses that want to avoid API dependency entirely, open-source models like Gemma 4 run on your own hardware at compute cost only.

Real Project: AI Classification Layer (Cost Savings of 73%)

One of our recent clients runs a customer portal that processes about 8,000 support messages per month. Previously, every message went into a single queue. Support staff manually categorized and routed each one. Average handling time: 4 minutes per message just for triage.

We built an AI classification layer using GPT-5.4 nano. The system reads each incoming message, classifies it into one of 12 categories, extracts key entities (order numbers, product names, urgency signals), and routes it to the correct team. Development time: 8 days. Total development cost: approximately 6,400 euros.

The monthly API cost surprised even us. At 8,000 messages averaging 200 input tokens and 50 output tokens each, the total comes to 1.6 million input tokens and 400,000 output tokens per month. With nano pricing, that is $0.08 for input and $0.16 for output. Under 25 euro cents per month for the AI itself. The client's previous setup using GPT-4o cost roughly 90 cents per month for the same volume, but the real saving is in staff time: the 4 minutes of manual triage per message dropped to zero. At 8,000 messages, that is 533 hours per month returned to actual support work.

Real Project: Conversational AI Assistant (Moderate Cost)

A second client wanted an AI assistant embedded in their B2B platform. Customers would ask questions about their account, order status, and product specifications. The assistant needed to access a database of 50,000 products and maintain conversation context across multiple exchanges.

This project was more complex. We used Claude Sonnet 4.6 for the conversational layer because it handles nuanced, context-rich dialogue better than the cheaper alternatives. A RAG (Retrieval-Augmented Generation) pipeline pulls relevant product data before each response. Development time: 4 weeks. Total development cost: approximately 14,000 euros.

Monthly API cost at their current volume of 3,000 conversations per month (averaging 6 turns each, roughly 800 tokens per turn): about 180 euros. That breaks down to roughly 6 cents per conversation. The client previously had two full-time staff members handling the same queries. The AI now resolves 70% of questions autonomously. The remaining 30% get routed to a human agent with full context attached, cutting their resolution time in half.

Real Project: Multi-Agent Document Processing (Higher Investment)

The most complex AI project we delivered recently was a document processing pipeline for a logistics company. They receive 2,000 invoices, packing lists, and customs declarations per week in varying formats: PDF, email attachments, scanned images, and sometimes handwritten notes.

We built an agentic workflow with three layers. A vision model (GPT-5.4 with image input) extracts text and structure from documents. A classification agent (nano) sorts them by type. A processing agent (Sonnet) validates the extracted data against business rules, flags anomalies, and prepares structured output for their ERP system. Development time: 9 weeks. Total development cost: approximately 28,000 euros.

Monthly API cost: approximately 340 euros at current volume. The vision processing is the most expensive component at roughly 220 euros, with classification and validation adding the remaining 120 euros. The client previously employed three data entry specialists for this work. Two have been reassigned to higher-value tasks. The third now handles only the 8% of documents that the AI flags as uncertain, a quality control role rather than data entry.

The Decision Framework: When AI Pays for Itself

Not every feature justifies AI. Based on our experience across dozens of projects, here is when it makes financial sense. If the task is repetitive, rule-based, and high-volume, AI pays for itself quickly. Classification, routing, data extraction, and simple Q&A all fit this profile. Development costs are moderate (5,000 to 10,000 euros) and monthly API costs are negligible.

If the task requires contextual understanding and conversation, the economics still work but the breakeven takes longer. Expect development costs of 10,000 to 20,000 euros and monthly API costs of 100 to 300 euros. The return comes from staff time savings and improved customer experience.

If the task requires multi-step reasoning, tool integration, and autonomous decision-making, you are in the 20,000 to 40,000 euro range for development with higher ongoing costs. This makes sense when the alternative is multiple full-time employees doing manual knowledge work. Use our project calculator to get a tailored estimate for your specific use case.

Five Mistakes That Make AI Features Expensive

First: using the biggest model for everything. We see this constantly. A team starts with GPT-5.4 or Claude Opus for every task, including classification and routing that nano handles at 50x lower cost. Always start with the cheapest model that solves the problem and only upgrade when quality demands it.

Second: ignoring caching. If your AI feature answers the same 200 questions repeatedly, cache the responses. A simple Redis layer can cut API costs by 60 to 80% for FAQ-style workloads. Third: building without measuring. You need token counters, cost dashboards, and usage alerts from day one. Without visibility, costs drift upward invisibly.

Fourth: over-engineering the first version. Start with a single AI feature, measure its impact, then expand. The worst outcome is spending 30,000 euros on a complex multi-agent system that your users do not actually need. Fifth: not considering open-source models. For high-volume, privacy-sensitive workloads, self-hosting Gemma 4 or a similar model eliminates per-token API costs entirely. The breakeven versus API pricing typically hits around 100,000 requests per month.

What We Recommend as a Starting Point

If you have never built an AI feature before, start small. Pick one workflow in your business that is repetitive, time-consuming, and well-defined. Email classification. Invoice data extraction. FAQ answering. Document summarization. These are problems that AI solves reliably today at low cost.

Budget 5,000 to 8,000 euros for the initial build, expect to spend under 50 euros per month on API costs, and measure the time savings over 90 days. In our experience, these first projects consistently deliver a return within three months, and they give your team the confidence and understanding to tackle larger AI initiatives.

At MG Software, we help businesses navigate exactly this decision. Which feature first? Which model? Build or buy? Self-host or API? The answers depend on your specific situation: your volume, your data sensitivity, and your team's technical capabilities. Get in touch for a free consultation, and we will help you find the highest-ROI starting point.

Conclusion

AI features are neither as expensive nor as cheap as most businesses assume. The real cost depends on three variables: model choice, task complexity, and usage volume. For most mid-sized businesses, the first AI feature costs between 5,000 and 15,000 euros to build, runs for under 100 euros per month, and pays for itself within a quarter through staff time savings.

The mistake is not investing in AI. The mistake is investing without clear numbers. Every project we build starts with a cost model. If yours does not have one yet, that is the place to start.