What AI Really Costs: TCO Comparison for Enterprises

Token Prices Are Not Your AI Costs

When organizations discuss AI costs, the conversation almost always begins with token prices. That is understandable: providers market their models with input and output prices per million tokens, and these numbers are easy to compare. A flagship model costs $5 per million input tokens, a budget model $0.25 — the difference appears dramatic.

Yet token prices account for only 20 to 35 percent of actual costs in practice. Anyone who reduces their AI budget planning to token prices underestimates the total costs by a factor of three to five. The real question is not: “What does a token cost?” The question is: “What does it cost to operate AI productively, securely, and compliantly in my organization?”

This article presents the four cost categories that every enterprise AI deployment encompasses, compares three scenarios from 26,000 to 410,000 euros in the first year, and explains how model switching can reduce token costs by 40 to 60 percent.

The Four Cost Categories

Every enterprise AI deployment distributes across four cost categories. The relative weighting varies by scenario, but the structure remains the same.

1. Model Costs: Tokens and Hosting (20—35%)

The most visible category: API fees for cloud models or hosting costs for self-hosted models. With cloud APIs, you pay per token — input and output separately. With self-hosting, you pay GPU rental, electricity, and maintenance. Costs depend directly on usage volume: a chatbot with 50 users generates different token volumes than ten specialized agents with 1,000 users.

What is frequently overlooked: self-hosting is cheaper than cloud APIs above a certain volume, but the entry costs are higher. A single GPU with 80 GB VRAM costs approximately 1,200 euros per month from a European hosting provider — regardless of whether it is fully utilized or not. For details on the hosting decision, see AI Hosting Strategies for Enterprise.

2. Infrastructure and Integration (25—35%)

The largest and most frequently underestimated category. It encompasses everything required to integrate a language model into your existing IT landscape:

• API gateway and routing layer: A central component that routes requests to the appropriate model, enforces rate limits, and tracks costs.
• RAG pipeline: If your AI is to access internal knowledge, you need a Retrieval-Augmented Generation pipeline: vector database, embedding model, chunking strategy, indexing.
• System integration: Integration with existing systems — ERP, CRM, document management, ticketing. Each interface requires development effort.
• Enterprise AI portal: An interface through which employees actually use the AI — with SSO, permissions management, and audit trail.

These costs are largely one-time. They occur primarily in the first three to six months and amortize over the operating period. But they must be planned and budgeted — otherwise hidden costs arise from workarounds and rework.

3. Governance and Compliance (15—20%)

Since the EU AI Act, governance is no longer an optional luxury. The costs in this category include:

• Risk classification: Assessment of all AI systems according to EU AI Act categories. For high-risk systems, a formal conformity assessment is required.
• Technical documentation: The EU AI Act requires comprehensive documentation of data provenance, training procedures, performance metrics, and risk mitigation measures.
• Audit trail and monitoring: Ongoing logging of all AI decisions, particularly in automated decision-making processes.
• Data protection: GDPR-compliant data processing, data processing agreements, data protection impact assessments for processing personal data.
• External advisory: Legal counsel for regulatory questions, data protection officer, and where applicable a conformity assessment body.

The governance share increases with the complexity of the AI deployment. A single chatbot for general knowledge queries has lower governance requirements than an AI system that pre-screens job applications.

4. Personnel and Capability Building (20—30%)

AI systems must be operated, maintained, and further developed. Simultaneously, employees must be equipped to use the systems. This category includes:

• ML Ops / AI engineering: At minimum one person responsible for model management, prompt optimization, monitoring, and troubleshooting. In the enterprise scenario, a dedicated team.
• AI literacy: Training for all users — legally mandated since February 2025 under the EU AI Act. Includes initial training and regular refreshers.
• Change management: Supporting the organization through the transition. New processes, new roles, new responsibilities.

In smaller scenarios, capability building can happen internally — without additional personnel costs, but with opportunity costs. In larger scenarios, you need dedicated staff or external support.

Cost Distribution at a Glance

Model Costs (Tokens/Hosting)          ████████░░░░░░░░░░░░  20–35%
Infrastructure & Integration          ██████████░░░░░░░░░░  25–35%
Governance & Compliance               ██████░░░░░░░░░░░░░░  15–20%
Personnel & Capability Building       ████████░░░░░░░░░░░░  20–30%

The distribution shifts over time: in the first year, infrastructure and integration dominate. From the second year onward, the relative shares of model costs and personnel increase as the one-time integration costs drop away.

Three Scenarios Compared

The following three scenarios represent typical entry points. The figures are reference values based on project experience with organizations of varying sizes. Your actual costs depend on your existing IT infrastructure, your integration requirements, and your chosen operating model.

Scenario	Model Setup	Monthly Token/Hosting	Integration	Governance	Personnel	Total 12 Months
Entry: 1 chatbot, 50 users	Sonnet API	~€500	€15,000	€5,000	€0 (internal)	~€26,000
Standard: 3 agents, 200 users	Sonnet + Llama self-hosted	~€4,000	€60,000	€20,000	1 ML Ops (partial)	~€148,000
Enterprise: 10+ agents, 1,000+ users	Multi-model, dedicated GPU	~€12,000	€150,000	€50,000	2 FTE	~€410,000

Scenario 1: Entry (approx. €26,000 / 12 months)

A clearly defined use case: an internal knowledge chatbot for a single department, based on a cloud API. 50 users, moderate query volume, no system integration beyond document upload. Governance is limited to GDPR-compliant data processing and basic documentation. Personnel costs are zero because the internal IT team manages operations alongside their day-to-day responsibilities.

This scenario is the typical proof of concept. It demonstrates value, validates the technology, and delivers empirical data for scaling. A clean PoC with a clearly defined use case typically costs 15,000 to 30,000 euros and is achievable in four to six weeks.

Scenario 2: Standard (approx. €148,000 / 12 months)

Three specialized agents for different processes — for example, document analysis, customer communication, and internal knowledge management. 200 users, hybrid hosting: non-sensitive requests via cloud API, sensitive data via a self-hosted model. Integration with at least one existing system. Governance includes EU AI Act risk classification and formal documentation. One ML Ops engineer handles model management and monitoring on a part-time basis.

This scenario represents the productive entry point. The organization has completed the PoC and is scaling across multiple departments. The infrastructure is designed for growth.

Scenario 3: Enterprise (approx. €410,000 / 12 months)

Ten or more specialized agents across multiple business units. Over 1,000 users. Multi-model architecture with dedicated GPUs. Deep integration into ERP, CRM, HR systems, and document management. Enterprise-grade governance: formal conformity assessment for high-risk systems, audit trail, governance dashboard. Two full-time ML Ops engineers for operations and ongoing development.

This scenario assumes the organization has completed the experimentation phase and operates AI as strategic infrastructure. The 410,000 euros sounds like a significant investment — and it is. But it distributes across a system that accelerates hundreds of processes, reduces error rates, and improves the quality of decision-making.

Context: What Do the Alternatives Cost?

The cost of an AI system should never be evaluated in isolation. The relevant comparison is: What do the processes cost without AI? If three clerks each spend two hours per day on document classification, that amounts to approximately 180,000 euros per year at fully loaded cost — for a task a trained agent handles in seconds. ROI is rarely the question. The question is how quickly it materializes.

Cost Optimization Through Model Switching

The most effective lever for model costs is not choosing a cheaper model, but the differentiated use of multiple models. This principle is called model switching or model routing.

The Principle

Not every request requires a flagship model. The majority of enterprise requests — standard responses, simple classification, data extraction from structured documents — can be answered at sufficient quality by budget models. Only for complex tasks — multi-step reasoning, contract analysis, decision preparation — is a flagship model necessary.

A routing logic automatically determines which model handles a request. The criteria are configurable:

• Complexity: Simple requests to budget models, complex ones to flagship models.
• Data sensitivity: Requests containing personal data to self-hosted models, non-sensitive requests to cloud APIs.
• Latency requirements: Real-time applications to fast, small models. Batch processing to powerful models without time pressure.
• Cost limits: Automatic throttling when a team or department budget is reached.

Savings Potential

In practice, enterprise requests typically distribute as follows:

• 60—70% standard requests: Simple classification, FAQ, data extraction. Budget models suffice.
• 20—30% medium complexity: Summaries, structured analysis, drafts. Price-performance models.
• 5—15% high complexity: Multi-step reasoning, contract analysis, strategic documents. Flagship models.

When 65 percent of requests use a budget model costing one-twentieth of a flagship model instead, token costs drop by 40 to 60 percent — with equivalent result quality for overall usage. Details on model selection and the performance profiles of current models can be found in the corresponding article in this series.

Implementation

Model switching requires three components:

1. Routing engine: A central logic that analyzes incoming requests and forwards them to the appropriate model. This can be implemented rule-based (keyword detection, user role, data classification) or model-based (a small classification model evaluates complexity).
2. Model registry: A central directory of all available models with their performance profiles, costs, and availability.
3. Cost monitoring: A dashboard that makes token consumption transparent per model, per team, and per use case. Without transparency, there is no optimization.

The implementation effort for model switching is manageable — typically two to four weeks. The savings begin immediately.

Budget Planning: Three Recommendations

First: Plan with TCO, not with token prices. When a provider presents token costs, at least 65 percent of the budget is missing. Demand a TCO calculation that covers all four categories.

Second: Start with a PoC, but plan for scaling. A PoC for 15,000 to 30,000 euros demonstrates value. But the PoC architecture must be built so that it scales without requiring a rebuild. Otherwise, you pay the integration costs twice.

Third: Implement model switching from the start. The routing layer has a low one-time cost and saves substantially over time. Anyone who routes differentially from the beginning avoids lock-in to a single model and retains cost control.

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📘 Enterprise AI Infrastructure Blueprint 2026 – Article Series

← Previous	Overview	Next →
Decision Layer & Shadow AI: Control Instead of Chaos	Overview	EU AI Act 2026: What Applies Now, What’s Coming, What You Must Do

All articles in this series: Enterprise AI Infrastructure Blueprint 2026

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