This article has been authored by Dan Rickards, Head of Strategic Cloud Consulting | 7th May 2025

 

Many of the organisations I work with are exploring the implementation of AI in their technology ecosystem. Whether this is building new AI-powered applications, integrating agents into workflows, or experimenting with foundational models to discover new use cases, AI adoption is accelerating across industries.

 

However, without a clear AI business case that measures the true cost of AI implementation, these experiments quickly add up.
Budgets get blown, lessons are missed, and finance teams often question the ROI, viewing these projects as chasing hype rather than delivering value.

 

Understanding your AI costs matters. This is not just about reducing spend, but about making sure organisations are being responsible with how they incubate AI to build both innovative and financially sustainable solutions.
Effective AI cost management helps ensure that innovation is paired with long-term financial accountability.

 

Below are four areas that should be considered when calculating the true cost of AI implementation.

 

AI Model Costs

The cost of running and training AI models is challenging. AI services often follow complex and inconsistent pricing structures due to their bespoke nature, with multiple model variants available. Models are typically priced on a per-request or per-unit-of-time basis.  For example:

 

  • Google’s Vertex AI: $0.0001 per image generation or $1.375 per node hour for data training.
  • AWS Bedrock: On-demand pricing and provisioned throughput (1–6 month commitments); Claude 2.0 costs $0.02 per 1,000 output tokens.
  • Azure OpenAI: GPT-4.5 pricing includes £59.07 per 1M tokens (input), £29.54 (cached input), and £118.15 (output).

 

The ongoing cost of training and tuning AI models for optimal performance must also be factored in. If cloud pricing is complex, AI pricing is even more complex making it essential to include model training and inference costs in your AI project budgeting.

 

Data Costs

Everyone knows the saying: ‘garbage in, garbage out’. In AI, the quality of training data directly affects output—but the costs of preparing or acquiring data are often underestimated.

 

AI cost forecasting must include the cost of:

  • Cleaning and curating internal data (e.g., reviewing, retaining, or deleting datasets).
  • Purchasing external datasets from third-party vendors to improve quality and accuracy.
  • Storing and accessing data via centralised platforms—especially if your models continuously ingest or generate large volumes of data.

Data readiness can significantly impact the overall cost of AI adoption, especially for enterprise organisations with legacy data systems.

 

Cloud Platform Costs

The best AI models typically run on the public cloud, which introduces a range of associated infrastructure costs.

 

This includes:

  • Platform services such as networking, API gateways, monitoring, load balancers, and security.
  • Direct costs tied to AI deployment: compute (GPUs, TPUs, CPUs), storage, data pipelines, and integrations.

 

If AI applications are deployed at scale, they can create a ripple effect across your cloud bill. Understanding cloud AI pricing models is crucial to building a sustainable and scalable AI architecture.

 

Operational Costs

Hiring and retaining AI talent is one of the largest hidden costs in AI implementation. Beyond the initial development phase, organisations must consider the ongoing operational costs of maintaining and evolving AI solutions.

 

This includes:

  • In-demand roles: data scientists, ML engineers, AI engineers, data analysts.
  • Third-party consultants or managed services (which may reduce hiring overhead but create long-term dependencies).
  • The time and cost to monitor model performance, handle issues, and retrain models over time.

Building a skilled, sustainable AI operations team is essential—not optional.  This includes controlling both the environmental impact and cost of AI.

 

FinOps Plus for AI

Being financially aware of the total cost of AI implementation allows organisations to innovate with confidence. A well-structured AI investment case doesn’t just justify spend—it demonstrates ROI and creates a clear path to long-term value. As adoption continues to grow, so does the need for proactive AI cost management strategies.

 

This is where FinOps becomes crucial. Without clear cross-functional accountability and continual optimisation for cloud and AI spend, innovation projects and AI investments can lose trust before they deliver impact – that’s where we come in; helping organisations evolve their IT cost management capabilities to align AI ambitions with cloud performance, cost visibility, and constant optimisation to deliver long-term value.

 

Looking to continually optimise your AI investments?

Get in touch to discuss how we can help you structure an AI business case and build an enhanced FinOps capability which balances innovation and commercial value.