An AI project costs between 5,000 euros and 250,000 euros depending on ambition — knowing how to break down this budget is the key
The cost of an AI project in SMBs and mid-market companies varies considerably depending on the scenario: 5,000 to 15,000 euros for a no-code POC, 20,000 to 80,000 euros for an MVP with custom development, 80,000 to 250,000 euros for an industrialized solution in production. Yet 62% of SMB leaders overestimate the entry cost and delay projects that could generate ROI as early as the first quarter. The most underestimated line item? Data preparation, which alone represents 30 to 40% of the total budget.
The problem
When an SMB or mid-market company leader considers an AI project, the budget question comes up immediately — and the answers they find are rarely satisfactory. Solution vendors advertise attractive entry-level prices but forget integration costs. Consulting firms propose six-figure quotes that seem disproportionate. Online articles give ranges so wide that they don't help with the decision.
This lack of cost transparency creates three concrete problems:
- Decision paralysis: without clear budget visibility, projects stay at the idea stage for months. The management committee keeps postponing the decision "until things become clearer."
- Poor resource allocation: some SMBs invest 80% of the budget in technology and neglect data preparation and change management, two critical success factors.
- Mid-project surprises: hidden costs (data, infrastructure, maintenance) appear during the deployment phase and blow up the initial budget, sometimes by 50 to 100%.
- Impossible comparison: without a common framework, comparing proposals from different providers is like comparing apples and oranges.
The result? Based on our field observations, a significant share of AI projects in SMBs exceed their initial budget, mainly due to a lack of upfront cost scoping. This is not inevitable: with a clear framework, you can anticipate every cost line item and steer your investment with confidence.
The AI solution
To keep an AI project budget under control, you first need to understand the three main project categories and the cost line items associated with each. Here is our framework, tested with more than 50 SMBs we have supported.
POC scenario (5,000 – 15,000 euros)
Validation of a single use case with no-code or low-code tools. Duration: 2 to 4 weeks. Examples: internal FAQ chatbot, automatic email classification, meeting minutes generation. Main line items: tool configuration (40%), LLM API (20%), consultant support (40%).
MVP scenario (20,000 – 80,000 euros)
Development of a custom solution integrated with the existing IT system. Duration: 2 to 4 months. Examples: sales assistant connected to the CRM, predictive demand analysis, intelligent document extraction. Line items: development (45%), data (25%), infrastructure (15%), change management (15%).
Production scenario (80,000 – 250,000 euros)
Industrialized solution with monitoring, scalability and maintenance. Duration: 4 to 8 months. Examples: e-commerce recommendation engine, fraud detection system, large-scale document processing platform. Line items: development and MLOps (40%), data and quality (20%), infrastructure (20%), annual maintenance (20%).
Implementation
To build a realistic AI budget, follow this three-step method that will save you from unpleasant surprises. Each step is designed for an SMB or mid-market company without a dedicated data team.
Scope the use case and estimate the value
Before pricing the costs, quantify the expected gain. An AI project should target a minimum ROI of 3x over 12 months. Identify the target process, measure the current time and cost, estimate the potential gain. For example: if your salespeople spend 15 hours/week qualifying leads manually and AI can reduce that time by 60%, the annual gain is around 35,000 to 50,000 euros. Our AI maturity audit helps you identify and quantify these opportunities.
Break down the budget by cost line item
Use this typical breakdown for an MVP project: data preparation (cleaning, structuring, labeling): 25-35%; development and integration: 30-40%; cloud infrastructure and APIs: 10-15%; testing and validation: 5-10%; training and change management: 10-15%. Note: data preparation is the most frequently under-budgeted line item. Always plan a 20% margin on this item.
Plan for recurring costs
An AI project doesn't stop at deployment. Annual maintenance costs represent 15 to 25% of the initial cost: monitoring and fixes (5-10%), API and infrastructure costs (5-10%), functional enhancements (5-10%). Factor these costs into your business case from the start to avoid surprises. Contact us for a personalized estimate tailored to your context.
Results
Frequently asked questions
What is the minimum budget to launch an AI project in an SMB?
A first AI project can start from 5,000 to 10,000 euros with a no-code or low-code approach. This covers a POC on a targeted use case (internal chatbot, document classification, automation of a simple process) using existing APIs such as GPT-4o or Mistral.
What are the hidden costs of an AI project?
The most often underestimated costs are data preparation (cleaning, labeling: 30 to 40% of the total budget), change management and user training (10 to 15%), maintenance and monitoring in production (15 to 25% of the initial cost per year), and cloud infrastructure costs that grow with usage.
Do you need to hire a data scientist for an AI project?
Not necessarily for the first projects. No-code tools (n8n, Make) and LLM APIs make it possible to deploy effective solutions without data science expertise. An external consultant can support the scoping and technical choices. Hiring becomes relevant once you have 3-4 simultaneous AI projects.
How can you reduce the cost of an AI project without sacrificing quality?
Three main levers: start with a targeted POC to validate the value before investing heavily, use pre-trained models and APIs rather than developing from scratch, and adopt an iterative MVP → V1 → V2 approach that spreads investments and reduces risks.
For technical profiles
Here is a detailed comparison of infrastructure and AI service costs across three technical approaches:
| Criterion | Cloud API (OpenAI / Mistral) | Self-hosted open-source model | Turnkey SaaS solution |
|---|---|---|---|
| Entry cost | Low (0 euros) | Medium (500-2,000 euros) | Low (50-200 euros/month) |
| Cost per request | 0.002 – 0.06 euros / 1K tokens | 0.001 – 0.01 euros (GPU included) | Fixed monthly plan |
| Monthly cost (1,000 req/day) | 60 – 500 euros | 200 – 800 euros (cloud GPU) | 200 – 1,000 euros |
| Customization | Limited (prompt engineering) | Full (fine-tuning, RAG) | Very limited |
| Data confidentiality | Cloud provider | On-premise possible | Data held by the vendor |
| Skills required | API developer | ML Engineer / DevOps | Business user |
| Deployment time | 1-2 weeks | 4-8 weeks | 1-3 days |
| Scalability | Automatic | Manual (infra to manage) | Automatic |
Our technical recommendation: for a first project, start with a Cloud API approach using prompt engineering and RAG. The entry cost is near zero, the deployment time is short, and you can migrate to a self-hosted open-source model if volumes or confidentiality requirements justify it.