Calculating the cost of generative AI—and how to keep it under control

Over the past few months, we’ve already told you how generative artificial intelligence (Gen AI) compares to traditional AI and what pros and cons the technology has. The ITRex generative AI consulting team has also delved into Gen AI use cases across several industries, including healthcare, life sciences, banking, retail, and supply chains.

Additionally, we’ve evaluated the cost of building artificial intelligence systems, infrastructure and all, and zoomed in on machine learning (ML) costs, calculating the expenses associated with preparing training data, fine-tuning models, and deploying ML-powered solutions.

Now it’s time to answer another burning question: how much does it cost to implement generative AI in business?

The answer depends heavily on your goals, use cases, and technical choices. That’s why, in this article, we’ll use our hands-on experience to break down Gen AI pricing models, compare implementation paths, and highlight the key cost drivers.

Whether you’re just getting started or planning a large-scale deployment, this guide will help you make smarter, faster, and more cost-effective decisions in today’s fast-paced Gen AI landscape.

Let’s dive in.

One of the most important factors influencing generative AI costs is your model selection and implementation strategy. Choose the wrong combination, and you risk overspending on unnecessary performance—or investing in a solution that fails to deliver business value.

To make an informed decision, it’s critical to match your model and deployment strategy to your specific business objectives, use case complexity, and data sensitivity. We will go over the two main categories of generative AI models, as well as the four primary implementation paths and their cost implications.

At the heart of any generative AI system, whether it’s a simple chatbot or a full-fledged AI agent, lies a foundation model—a large, pre-trained model capable of tasks like text generation, image creation, or code completion. Large language models (LLMs), such as GPT, PaLM, and LLaMA, are built on massive datasets and can be tailored to a variety of enterprise use cases.

A key factor influencing both the capability and cost of a generative AI solution is the number of parameters a model has. Parameters are the internal weights a model learns during the LLM training process to make predictions or generate content. In general, more parameters lead to better performance and higher Gen AI costs—but not always.

Model performance also depends on other variables, such as the quality and diversity of training data, architecture efficiency, and how well the model is aligned to the specific task. In some cases, a smaller, well-tuned model may outperform a larger generic model while keeping Gen AI costs lower.

Generative AI models are divided into two broad categories based on their accessibility and licensing:

While open-source foundation models can be powerful, they frequently require significant computation and tuning to meet enterprise requirements. Small language models (SLMs) are an emerging alternative—compact, fine-tuned models that provide many of the benefits of large models but require significantly less infrastructure and training. These models can be deployed on-premises, run efficiently on smaller GPU setups, and are well-suited for domain-specific tasks where full-scale LLMs can be overkill.

Once you select a model type (closed-source or open-source), there are four common paths for implementation. Each approach comes with distinct trade-offs in terms of Gen AI cost, customization, performance, and operational complexity:

Best for: Quick pilots, non-critical tasks, and content generation workflows

Example tools: OpenAI’s ChatGPT, Google Bard, Anthropic’s Claude, Synthesia

Estimated Gen AI cost: Starts at $0.0005 per 1,000 characters (Google PaLM 2) or $0.001–$0.03 per 1,000 tokens (OpenAI GPT-3.5/GPT-4)

Early adopters and small teams often choose closed-source, commercially available models to create marketing content, automatically reply to customer inquiries, or process internal documentation faster. However, vendor lock-in and limited domain alignment may become a problem as Gen AI usage grows.

Best for: Organizations with domain-specific needs that still want vendor infrastructure

Example providers: OpenAI’s fine-tuning for GPT-3.5, Salesforce Einstein Copilot (limited support for tuning), Google Vertex AI (PaLM tuning)

Estimated Gen AI cost: Ranges from $10,000 to $50,000 depending on scale, volume, and model type

This approach strikes a balance between faster time-to-market and improved performance—but comes with recurring costs and limited portability.

Best for: Companies with internal infrastructure and light customization needs

Example models: GPT-2, RoBERTa, GPT-Neo, DistilGPT

Estimated Gen AI cost: Typically $20,000–$50,000 for infrastructure, integration, and basic operations

By using an open-source model without customization, your company can reduce the total cost of ownership and improve data governance in internal-facing systems. That being said, general-purpose models may struggle with specialized business content without tuning.

Best for: Enterprises prioritizing full control, high accuracy, and data privacy

Example models: LLaMA 2, GPT-J, Falcon, Mistral (via Hugging Face), BLOOM

Estimated Gen AI cost: $80,000–$190,000+, factoring in infrastructure setup, development, tuning, and internal support

This route is most common for regulated industries like healthcare and finance, IP-heavy domains, and organizations investing in long-term AI capabilities. Despite its high initial cost, the custom approach provides the greatest strategic flexibility.

Before selecting one of the options, you must first answer several questions:

In the next section, we’ll break down the specific generative AI cost components and pricing models for each approach to help you allocate a realistic Gen AI implementation budget.

To help you understand the associated costs, the ITRex team ran a high-level generative AI cost calculation for enterprise projects using common implementation patterns.

Off-the-shelf text processing and generation services typically charge businesses based on the number of characters or tokens—basic units of text ranging from punctuation marks to words and other syntax elements—in input or output text.

Here’s how this works in practice:

Gen AI services for visual content creation, meanwhile, tend to charge users per generated image, with fees tied to image size and quality. A single 1024-by-1024-pixel image produced by DALL·E 3 in standard quality would cost you $0.04. For larger images (1024×1792 pixels), as well as high-definition images, the price would go up to $0.08–0.12 apiece.

Also, don’t forget about turnkey Gen AI platforms, such as Synthesia.io, which take a more traditional approach to pricing. If your marketing team is looking to speed up the video creation process, you can try the tool for as little as $804 per year. Other emerging tools, including OpenAI’s Sora and Google DeepMind’s Veo, are part of ChatGPT Pro and Gemini Advanced, respectively; in this case, AI video costs are covered by the premium subscription.

As you can see from the previous section, the majority of ready-made Gen AI products leverage the pay-as-you-go monetization strategy.

While their pricing models look fairly straightforward at first glance, it could be challenging to predict how many queries your employees will run, especially if you seek to explore multiple generative AI use cases in various departments.

This brings about confusion regarding Gen AI tools’ pricing and total cost of ownership, as it was in the early days of cloud computing.

Another disadvantage of using commercial Gen AI solutions is that general-purpose products like ChatGPT lack contextual knowledge, such as familiarity with your company’s structure, products, and services. This makes it difficult to augment operations like customer support and report generation with AI capabilities, even if you master prompt engineering.

According to Eric Lamarre, senior partner at McKinsey, to solve this problem, organizations “need to create a data environment that can be consumed by the model.” In other words, you’ll have to retrain commercially available Gen AI tools on your corporate data, as well as information pulled from external sources via APIs. To improve model accuracy and reliability, we strongly recommend enhancing your Gen AI solution with a retrieval-augmented generation (RAG) architecture.

There are two ways to tailor Gen AI models to your unique business needs—and several factors that will impact the cost of generative AI in each scenario:

Disclaimer: We’re not suggesting that you build a custom foundation model akin to ChatGPT from the ground up—that’s a venture best left to those with substantial backing, like OpenAI’s support from Microsoft to offset their $540 million losses.

Even more basic foundation models, like GPT-3, can rack up initial training and deployment costs exceeding $4 million. Furthermore, the complexity of these foundation models has skyrocketed at an astonishing rate in recent years.

The amount of computing resources required for training large AI models doubles every 3.5 months. The foundation models’ complexity is changing, too. For instance, in 2016, BERT-Large was trained with 340 million parameters. In comparison, OpenAI’s GPT-3 model was trained with around 175 billion parameters.

The good news is that foundation models are there already, which makes it relatively easy for businesses to start experimenting with them while optimizing generative AI implementation costs.

Essentially, we could treat foundation models as a toolkit for AI software engineers since they provide a starting point for solving complex problems while still leaving room for customization.

We could loosely divide existing foundation models into three categories:

Once you’ve selected an open-source model that best suits your needs, you can integrate it with your software using APIs and utilize your own server infrastructure.

This approach involves the following generative AI costs:

Ultimately, how much could it cost your company to adopt a generative AI foundation model “as is,” deploying it on your own infrastructure?

For a mid-sized enterprise aiming to use a moderately large model like GPT-2 on-premises, the associated generative AI costs could span:

The total cost of setting up and operating a generative AI solution would include the following:

These ballpark estimates can vary significantly based on specific requirements, location, and market conditions. It is always preferable to speak with a professional for a more personalized and accurate estimate. It’s also a good idea to check current market rates for hardware and cloud services to get the most recent pricing.

If your company is thinking about adjusting an open-source foundation model, it’s important to consider the factors that can affect the cost of implementing generative AI.

Such factors encompass:

Let’s proceed to calculating the cost of generative AI implementation in business based on the factors mentioned above.

For a mid-sized enterprise looking to fine-tune a moderately large model like GPT-2, the associated generative AI implementation costs could span:

The total cost of setting up and operating a generative AI solution would include the following:

Actual Gen AI development and implementation costs will vary depending on the specific project requirements, availability of training data and in-house AI talent, and the location of your outsourcing partner. For the most accurate and up-to-date pricing, contact professionals or service providers directly.

Before ChatGPT gained attention, Latitude, a pioneering startup responsible for the AI-based adventure game called AI Dungeon, had been utilizing OpenAI’s GPT model for text generation.

As their user base grew, so did OpenAI’s bills and Amazon infrastructure expenses. At some point, the company was paying $200,000 per month in associated costs to handle the increasing number of user queries.

After switching to a new generative AI provider, the company reduced operating costs to $100,000 per month and adjusted its monetization strategy, introducing a monthly subscription for advanced AI-powered features.

At ITRex, we’ve helped numerous companies implement Gen AI in education, healthcare, retail, and enterprise productivity, frequently combining pre-trained models with domain-specific logic, RAG, and cloud-native architectures. Below, we break down real-world examples from our portfolio to help you better understand the cost of a Gen AI initiative.

A US-based SaaS company that specializes in corporate education teamed up with ITRex to reduce onboarding time for sales reps through generative AI. Traditional sales onboarding can take up to six months and costs companies more than $100,000 per representative. Our client required a solution that would significantly shorten the cycle while scaling across teams and customers.

To tackle this challenge, we engineered a modular Gen AI training platform using a custom RAG pipeline and OpenAI’s GPT-4 model. Our team first built a high-quality knowledge base by parsing internal content—PDFs, presentations, documents, and subtitles—into structured text, then converting it into embeddings using OpenAI and SentenceTransformers. An intelligent chunking mechanism and adaptive retrieval strategy ensured relevant, low-latency responses during real-time interactions.

We improved model accuracy with few-shot learning and added features like personalized lesson generation based on resumes and job descriptions, dynamic difficulty calibration by company, and a live Q&A module. Infrastructure-wise, the ITRex team used Microsoft Azure (Service Bus, SQL Server, and Blob Storage) while keeping LLM services modular for future model swaps.

Melody Sage is an internal R&D project launched by ITRex’s Gen AI team to investigate how generative AI can transform personalized education. The initiative sought to reimagine how adult learners engage with music theory by replacing static lessons with AI-generated content and adaptive support, rather than relying on human tutors. The goal was to create a fully autonomous, Gen AI-based music tutor that could develop custom curricula and intelligently respond to learner queries in real time.

Our engineers built the platform entirely on Google Cloud Platform (GCP), combining Google’s Gemini 2.5 Pro and Imagen3 models with a custom RAG pipeline and AI agent flow. Melody Sage enables users to upload learning materials (PDFs, DOCXs, etc.) that are automatically parsed and segmented into chunks by Document AI and Vertex AI. The system then generates structured lessons and quizzes with illustrated covers using Gemini and Imagen3. Learners access their personalized curriculum via a secure front end, while Firestore monitors progress and adjusts content delivery on the fly.

What truly distinguishes Melody Sage is its consultation agent, which augments internal knowledge with real-time web search via the Google Search API. This agent can respond to open-ended learner questions by reasoning across multiple sources, utilizing a self-assessment step to evaluate conflicting data and prioritize trustworthy inputs. To reduce manual overhead, we created a two-step prompting strategy in which Gemini generates quiz questions and verified answers based on the lesson content.

Choosing the appropriate LLM was also an important decision. Early experiments with Claude 3.5 produced high-quality results but introduced latency and integration complexity. Gemini 2.5 Pro eventually proved more efficient, better aligning with GCP infrastructure and providing faster response times—essential for real-time user interactions.

Now that you know what to expect from generative AI cost-wise, it’s time to talk about the technology’s implementation pitfalls and considerations:

Also, there are several questions that your company needs to answer before getting started with generative AI implementation:

Having a well-thought-out implementation plan will not only help you adopt the technology in a risk-free way and reap the benefits faster but also reduce the cost of generative AI.