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Artificial Intelligence Cost Calculator

AI Development Costs: Transparent Calculator and Ranges

Calculate the costs for your AI project with our interactive calculator.

In brief

Investment frame: AI introduction

The investment frame is EUR 2,240 – 89,376 excl. VAT. Chatbots, RAG and custom ML change the scope, not three price lists.

Investment frame for AI introduction

AI introduction: read the range

AI cost calculator

What does your AI project cost?

Calculate the cost of your AI solution

Step 1 of 520%

What do you want to implement?

How the investment frame for AI introduction is built

The calculation follows six visible stages. All figures remain planning values until scope and assumptions have been reviewed.

  1. 1. Capture inputs

    The calculator records the visible project, volume, complexity and operating parameters selected on this page.

  2. 2. Add fixed components

    Fixed base modules and selected add-ons are added without mixing them with recurring charges.

  3. 3. Apply multipliers

    Quantity, complexity and scope factors multiply only the cost components identified for them in the calculator model.

  4. 4. Create the range

    The calculator applies its documented lower and upper uncertainty factors to the base result; ROI views keep investment and savings visible separately.

  5. 5. Round and calibrate

    Monetary result fields are rounded to the nearest whole euro. Public project-cost components are calibrated with the centrally maintained display factor.

  6. 6. Classify the result

    The result is shown as non-binding guidance. A binding quote requires scope, data, integrations, risks and acceptance criteria to be reviewed.

Included

  • Inputs shown in the calculator
  • Calculator-specific base values and factors
  • Displayed one-off and recurring result components

Not included

  • Requirements not selected in the calculator
  • Unknown data migration and third-party licence costs
  • Taxes, legal advice and a binding delivery commitment

Efficiency or discount factor: A central display factor of 0.7 is applied to public project-cost components to keep all calculators aligned with the currently reviewed pricing basis. It is not a customer-specific discount; customer-entered wages, revenues and existing operating costs are not reduced.

Why this is not a binding quote: The calculator cannot verify complete requirements, third-party dependencies, data quality, legal constraints or acceptance criteria.

Technical responsibility and review

Technical owner
Björn Groenewold
Role
Managing Director and software engineer
Expertise
Software development and software estimation
First published
Last technical review
Price basis
September 2026

Sample calculations & scenarios

Concrete project profiles with assumptions and indicative budgets—useful for internal alignment alongside the calculator.

Cost examples

All cost examples for this calculator

The upfront cost can be spread over 72 months. Terms are on the software financing page.

Tip: Is AI worth it for your business?

Our ROI calculator takes into account time savings, error reduction, and ongoing AI operating costs.

Calculate AI ROI

AI Development Pricing Overview

AI project costs vary widely – from simple API integration to custom ML models. Every tier stays inside the same investment frame.

LLM Integration
Inside the investment frame
  • Connect GPT-4, Claude, etc.
  • Prompt engineering
  • Simple chatbots
  • 1–4 weeks development
Popular
AI Knowledge Base / RAG
Inside the investment frame
  • Your documents as knowledge source
  • Intelligent Q&A systems
  • Enterprise chatbots
  • 4–12 weeks development
Custom Machine Learning
Inside the investment frame
  • Train custom ML models
  • Computer vision, NLP
  • Predictive analytics
  • 3–12+ months development

LLM Integration: The Fastest Entry Point

With modern Large Language Models (LLMs) like GPT-4, Claude, or Gemini, you can bring AI features to your software within just a few weeks. Whether text generation, summarization, translation, or customer service – the APIs are powerful and well-documented.

The effort mainly lies in prompt engineering (asking the right question) and integration into your existing software. For many use cases, this is the most affordable path to AI.

RAG: Your Data + LLM = Real Knowledge

Retrieval Augmented Generation (RAG) combines LLMs with your own knowledge base. Instead of only answering based on general training, the AI accesses your documents, FAQs, and internal information.

This drastically reduces hallucinations (incorrect answers) and delivers reliable, verifiable information. Ideal for customer service, internal knowledge management, and sales support.

When to Choose Custom ML?

Training custom machine learning models is worthwhile for:

  • Specialized data: Images, sensor data, industry-specific texts
  • Data privacy: No data sent to external APIs
  • Domain expertise: Train models on your specialized terminology
  • Edge deployment: Run models locally/offline

Plan for Ongoing Costs

AI projects have significant ongoing costs:

  • API costs: API cost follows token volume and stays monthly, separate from the investment frame
  • Infrastructure: Vector databases, GPU compute, hosting: monthly and separate from the investment frame
  • Maintenance: Prompt optimization, model updates, monitoring: monthly and separate from the investment frame

AI for Your Business

Intelligent Automation

AI takes over complex tasks that previously required human intelligence.

Data Insights

Identify patterns and correlations in large datasets.

Competitive Advantage

Faster decisions and better customer service through AI.

Related Topics

AI ServicesAI Knowledge Base CostsAutomation CostsSoftware Development Costs

Frequently asked questions

AI Development Costs

Costs & budget

Which factors determine AI costs for businesses?

AI costs for businesses depend on four main factors: (1) project type (LLM integration vs custom ML), (2) data quality and preparation, (3) interface complexity (ERP, CRM, third-party systems) and (4) privacy and compliance requirements.

Add conception and testing, plus ongoing automation and operations. Introduction costs rise with training and change management. Total cost of ownership before project start avoids budget surprises.

How much does an AI chatbot cost?

The investment frame is EUR 2,240 – 89,376 excl.

VAT. An FAQ bot, an LLM chatbot and an enterprise knowledge base differ in scope, not in three price lists.

How much does machine learning development cost?

Proof of concept: EUR 2,240 – 89,376 excl.

VAT. Production-ready ML model: EUR 2,240 – 89,376 excl. VAT. Deep learning with custom training: EUR 2,240 – 89,376 excl. VAT – depending on data quality and domain.

What is a sensible starter budget for AI adoption in SMEs?

A proof of concept for a defined use case is typically achievable for EUR 2,240 – 89,376 excl.

VAT. That validates feasibility and business value before full development. Introduction costs rise with complexity, data quality and compliance.

How does the EU AI Act affect AI project costs?

For high-risk AI, extensive documentation and transparency obligations apply; compliance costs are roughly EUR 2,240 – 89,376 excl.

VATone-off plus ongoing monitoring depending on risk class. Simple chatbots are usually low-risk.

Björn Groenewold – Geschäftsführer Groenewold IT Solutions

Calculate AI costs

Use our calculator for an initial estimate of your AI project.

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Alternatively:Book a consultation

Technology & approaches

Can you integrate AI into existing software?

Yes – text generation, classification, image recognition or recommendations can be integrated via API into modern software.

Typical integration costs: EUR 2,240 – 89,376 excl. VAT.

When is custom ML better than pre-built models?

Pre-built LLMs (GPT, Claude) are fast and affordable for text and speech.

Custom ML pays off for specialised data, domain knowledge, privacy or on-premise requirements. We recommend the right mix per use case.

What is RAG (Retrieval Augmented Generation)?

RAG combines LLMs with your knowledge base: the AI answers from your documents instead of general training only – reducing hallucinations and suiting knowledge bases and support.

Björn Groenewold – Geschäftsführer Groenewold IT Solutions

Request a consultation

Which solution fits your project? We advise you without obligation.

Get in touch

Funding & timeline

Are there grants for AI projects?

Funding options must be checked before the project starts.

go-digital has ended; depending on the project, current KfW products or active German state programs may be relevant. We review the rules then in force and support suitable applications without promising a general funding rate.

How long does an AI project take?

LLM integration: 1–4 weeks.

RAG/knowledge base: 4–12 weeks. Custom machine learning: 3–12+ months depending on data and complexity.

Privacy & operations

What are the ongoing costs of an AI solution?

LLM APIs (e.g.

OpenAI, Anthropic): typically monthly and separate from the investment frame at SME volume. Cloud infrastructure: monthly and separate from the investment frame. Maintenance and monitoring: monthly and separate from the investment frame – depending on usage and operating model.

We calculate total cost of ownership transparently before project start.

How do we ensure GDPR compliance for AI solutions?

Through EU data processing, data processing agreements with AI providers, privacy by design and, for sensitive data, on-premise models or dedicated EU cloud instances.

How often do AI models need updating or retraining?

LLM-based solutions do not need proprietary retraining – updates come from the provider.

Own models (classification, forecasting) may need quarterly to annual updates depending on data drift.

More about AI development

AI introduction: realistic cost estimation

How we reliably calculate the cost of introducing AI

Use-case evaluation, model and hosting choice, pilot, and operations—so AI enthusiasm doesn't become a budget hole.

  1. 1. Use-case evaluation with data check

    We check which use cases really pay off and where data is sufficient. Without data, AI has no chance—that flows directly into the estimate.

  2. 2. Model and hosting choice

    OpenAI, Azure OpenAI, on-prem open source, Mistral—each variant has different costs (tokens, GPU, license). We recommend what fits use case and risk profile.

  3. 3. Pilot and validation cost

    Pilot budget with clear success metrics instead of an open-ended PoC. You pay for insights, not for experiments.

  4. 4. Scaling and operations

    We calculate inference, monitoring, drift detection, and retraining. Only then do you see the real 3-year TCO of your AI solution.

Typical pricing models (overview)

Comparison: typical pricing models for software and IT projects
ModelWhen it fitsBudget & flexibilityTypical risks
Fixed price (fixed scope)Clearly defined scope, stable requirements, repeatable delivery.Predictable total cost; little room for change without a change order.Scope creep leads to change orders or quality trade-offs.
Time & MaterialDiscovery, legacy, evolving requirements, or close collaboration.Maximum flexibility; budget transparent via hourly or daily rates.Without prioritisation, effort can grow—backlog and reviews matter.
Retainer / maintenance packageOngoing operations, updates, small features, and support.Agreed capacity per month; predictable follow-on cost.Large changes may still need a separate estimate.
Hybrid (milestone + T&M)MVP or phased releases with clear go-lives, then iterate.Core delivery fixed price; extensions on a time-and-materials basis.Define contractually what is in scope vs. extra work.

Calculators on this page provide indicative ranges; we choose the right model with you based on risk, scope, and planning horizon.

What determines the costs, and what comes next?

The ranges shown are indicative. For a binding quote we discuss scope, priorities and funding options in a free intro call. Many digitalization projects qualify for grants – try our funding calculator.

Browse all cost calculators, explore services and typical solutions. Questions about artificial intelligence? Contact us.

The calculator result for artificial intelligence is indicative only – a binding budget follows scope alignment, data review, and quality targets.

Plan follow-on costs

  • Operations and maintenance separate from the initial build
  • Internal key users and training
  • Monitoring and support after go-live

Next steps after the calculator

  • Intro call: funding and phased delivery
  • Discovery, pilot, or rollout matched to risk
  • Documented assumptions and exclusions in the quote

Compare related calculators in the costs hub for edge cases (integrations, compliance, parallel run).