Artificial intelligence (AI) is considered by many to be a must-have for a modern, productive workplace. Upwork data reflects this, with a 25% growth in gross services volume (GSV) for AI-related work between 2024 and 2025. If you're one of the many business owners interested in launching an AI initiative, though, you’ll want to know what costs you might face.

AI-related costs can add up quickly, often in ways that you weren't expecting. The true cost of AI includes usage fees, training expenses, hardware costs, and even time.

Primary cost factors associated with AI adoption

AI costs vary based on how you plan to use the technology. They can encompass up-front, implementation, operational, and ongoing expenses. And some costs aren't paid in dollars — they're more hidden and paid in time or lost productivity.

AI Cost Breakdown Table
Cost category	What is it?	Actual cost	Type of expense
Licensing	Paying to use an off-the-shelf AI model	$25 per month and up	Ongoing operational cost
Infrastructure	Purchasing a GPU to build an AI model	$40,000 per unit or more	Up-front cost
Compute	Renting GPU space by the hour to build an AI model	$600,000+ per year	Ongoing operational cost
Training	Obtaining data, people, and utilities for AI model training	Varies based on freelance experts' rates	Up-front implementation cost, ongoing maintenance cost
Time	Investing time into building or learning how to use an AI algorithm	Varies based on team readiness for AI development and implementation	Hidden cost, ongoing cost

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Let's take a closer look at each of these costs and walk through which ones your business operations and generative AI projects could incur and consider possible alternatives

Licensing costs

Most companies don't have their own, fully bespoke large language model (LLM). Instead, they use a pretrained model from an AI company — think OpenAI's ChatGPT or Anthropic's Claude. This AI model is either accessed directly through the default chat interface provided by the AI company or via an application programming interface (API).

Both of these AI implementation options cost money. Companies that want to give AI tools to every employee have to pay for a license. Free AI accounts usually have restrictions on how much you can use the tool, and what control you get over how your data is used. A business will very quickly hit the limit on any free account; it's usually just a few messages at a time.

Unlimited use of an AI chat website, like chatgpt.com or claude.ai, requires a monthly subscription fee, while API access costs are based on how much you use the tool.

Example #1: Company-licensed AI chat tool

Let's say I have a business with 100 employees and I want them to all start using ChatGPT at work. ChatGPT's free tier cuts users off for at least five hours each time they hit their message limit, so I need to buy a paid plan. If I pay for a year of access up front, it works out to $25 per user, per month.

ChatGPT Pricing for Business and Enterprise plans

This means that I'll spend $30,000 on OpenAI licenses for all of my employees over the course of a year. But I could also reasonably spend $30,000 to hire another part-time employee or contract one or more freelancers to bring specific skills into my organization.

Think about what you want to get out of the AI solution and how well it might perform the intended tasks. For example, if I'm not happy with the marketing emails that my team is sending to customers, but I don't have any kind of style guide or brand guidelines in place, then an AI-driven tool won't magically fix my problems. It may wind up amplifying the issues I already have. In this case, I'd be better served by hiring a freelancer to help me get my company's communications policies in order.

But if I'm interested in giving my experienced in-house software development team access to AI to help them potentially ship new code and fix small bugs faster, then it might make sense for me to pay for the ChatGPT license and see if my team likes its functionality. In this scenario, I could get a return on investment without further costs.

Example #2: API use

But what if I don't want to give my team blanket access to a general chat tool like out-of-the box ChatGPT or Claude? What if I want to build something that's more customized to the way we already work or integrate AI into one of our existing systems?

In this case, I'd want to use an API. The API would allow me to connect an AI to tools like Slack or build custom workflows for my team using an automation platform like n8n.

Secure access to the Anthropic Claude API

In this scenario, I'll have to pay for Claude based on how much my team uses it in the connected tools and workflows. AI API costs are calculated in tokens, which you can think of like credits. Different actions that you take with the AI use varying amounts of tokens.

You can find multiple discussions related to token use on the r/AI_Agents subreddit, and everyone's answers are different — total token use really depends on what you want to do and how many times you wish to revise the AI's outputs. Some example token use rates from these Redditors include:

User 1: 1 billion tokens in two months ($1,000–$5,000)
User 2: 50,000,000 tokens a day ($50–$250)
User 3: 30,000 tokens per minute ($0.30–$1.50)

The following table estimates how much each of those users would spend per day, per workweek, and per month.

Token Cost Table
	User 1	User 2	User 3
Token cost in one day	$16–$83	$50–$250	$432–$2,160
Token cost in 5 days	$80–$415	$250–$1,250	$2,160–$10,800
Token cost in 30 days	$480–$2,490	$1,500–$7,500	$12,960–$64,800

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Spending $83 in one day to experiment with API access might not be that big a deal for your business. But if you're facing a $64,800 bill, closely evaluate how you're using those credits and consider if there are other options that may be more cost-effective.

If you're spending that money to power the AI component of an app you sell to customers at a premium, the costs may work out in your favor. But if you're burning through that many tokens to try to replicate human skills, you'd be much better served by hiring specific people to bring needed skills into your company. (For instance, companies hire me to write articles just like this one — and I've never charged any of my clients $64,000 in one month!)

Infrastructure costs

But let's say you want to build and train your own custom AI technology to act as an internal company chatbot. That's one way to avoid paying subscription and token fees every month — but it's not entirely free, either. You'll need to pay for hardware that can handle the computing demands of training, hosting, and using an AI model.

While some very small AI models can run on a regular computer, the large, commercial-ready AI systems (like something you may want to license to your own customers or use across a sizable team of employees) require specialized graphics processing units (GPUs).

Two companies, NVIDIA and AMD, make most of the GPUs that are used in LLM development and deployment. Every time you access a cloud-based AI tool like ChatGPT or Gemini, you're drawing upon GPUs housed in a large data center like this Google property in Ireland:

Every time you access a cloud-based AI tool like ChatGPT or Gemini, you're drawing upon GPUs housed in a large data center like this Google property in Ireland — Image via Google

Buying your own physical NVIDIA or AMD GPU can cost up to $40,000 per unit — and multiple units are typically required for powerful AI computation. Because the total cost of hardware, required physical space, and utilities to buy and store GPUs is prohibitive for many companies, entire businesses have been built around renting GPU space in data centers.

Compute costs

Renting a GPU, also called purchasing AI compute, allows you to tap into the necessary computational resources to build your own LLM at a fraction of the cost of buying a GPU outright.

CoreWeave is one such AI services company. They sell compute for $6.50 per hour and up; the total cost depends on how many resources you need as well as which type of NVIDIA GPU you want to access.

Here's what the cost of one instance looks like over time for CoreWeave's cheapest and most expensive options:

GPU Cost Comparison
	One hour	24 hours	40 hours	One year
NVIDIA GH200	$6.50	$156	$260	$56,940
NVIDIA B200	$68.80	$1,651.20	$2,752	$602,688

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As you can see, the cost of purchasing compute can be cheaper than paying for API access to a fully pretrained, cloud-hosted AI model. But if you plan to build your own LLM, the costs don't stop at hardware. You also have to pay for the data, people, and time to train the model and turn it into a usable, effective AI-powered tool.

Training and development costs

Companies like Appen and BrightData sell curated datasets that you can purchase and use for training an AI model. These ready-made data packages can include information ranging from dictionary definitions to LinkedIn users' post histories. You may have to buy more than one dataset, depending on how you want to train your model — and for what purpose. As a result, dataset pricing is often customized and available upon request.

But what if you've engaged in your own high-quality data collection process and want to use that information for building an AI software application? In this case, you'll need to hire freelance experts like:

These freelancers can help to ensure that your project approaches data preparation, model development, fine-tuning, and deployment in a responsible and accurate way.

Time costs

One of the most hidden costs in AI adoption is time. Every AI application can behave slightly differently; that’s true across platforms and for individual users if they're engaging with a predictive, generative model. Getting familiar with a tool, adapting existing workflows to include AI, learning to structure prompts, and checking for output errors are all elements of the process and take time.

And adapting to AI isn't just about technical skills, either. Yes, team members need to learn how to work with AI tools and incorporate AI agents into their projects. But the conversational nature of many AI tools means that this process often requires uniquely human "power" skills like:

Emotional intelligence
Ethical decision-making
Conflict resolution
Adaptability
Empathy
Critical thinking
Interpersonal communication
Collaboration

As such, your team members may need both training on how to work directly with an AI (prompting, Python, deep learning frameworks) and support to strengthen their power skills.

Also consider the cost of time invested into planning how to use AI across your operations, too. Tasks can be divided into four zones based on how well they're suited to automation from both an interpersonal and technical perspective:

Teams have a high desire to automate a task and the technical capability exists
Teams have a low desire to automate the task even though the technical capability exists
Teams have a high desire to automate a task but the technical frameworks must be developed further
Teams have a low desire to automate the task and the technical frameworks aren't ready yet either

Pinpointing what portions of your processes fall into zone number one can help you get the most out of your AI solutions directly after implementation.

How much will you spend?

Your total AI implementation cost will depend on how you plan to use the tools. For example, if you want to use Claude to help your team of five people code your an AI application to offer to customers, you’ll need to:

Buy a team subscription for five people over 12 months = $1,500
Rent NVIDIA GH200 GPU compute from CoreWeave for a year = $56,940
Pay for AI API tokens = $5,760

You’ll be looking at a bill of $64,200 just for the technical and AI components you need — and the cost could be more depending on how many tokens you use. Plus, you’ll still need to account for your team’s salaries and any payments you make to freelancers who bring specific skills to the table.

Considering that the cost of team licenses and compute can each be equivalent to a full-time salary depending on how much you use, it’s not a bad idea to consider the cost of AI as being akin to adding another person to your team.

Even on a small team, you could decide between paying for a year of ChatGPT licenses or paying that money to a freelancer who provides the AI support your company needs.

Frequently asked questions

Calculating the cost of AI integration is complex and depends on a variety of factors that are unique to your operations. The answers to these common questions may help.

What's the biggest cost in AI adoption?

If you're buying a license to use an off-the-shelf AI tool, your biggest costs may be in the time spent training your team to use the tool, hiring engineers to build different integrations, and in monthly fees or token costs.

If you're building your own AI model from scratch, however, your biggest cost is likely to come from obtaining the necessary infrastructure and compute.

How much does it cost to maintain an AI system?

The cost to maintain an off-the-shelf AI system is fairly low — you pay for the cost of your subscription or tokens, and the AI company you're working with maintains the infrastructure.

Costs to maintain your own AI infrastructure can be very high when you combine the up-front hardware cost along with the cost of the utilities (primarily power and water) to run the hardware. You can save money by buying compute from a company that already has the hardware in place. They're responsible for maintaining the servers and datacenter, not you.

Do free pretrained AI models exist?

Yes, you can download and use several open-source, free AI models including Deepseek and Llama. Many of these models are far too resource heavy to run on standard legacy infrastructure in your workplace, though, so you'll need to purchase compute in order to deploy and use your model of choice.

Do productivity gains offset the cost of AI adoption?

AI isn't improving productivity across the board in every sector just yet, in part due to the learning curve associated with some implementations. METR, a nonprofit research organization studying AI capabilities, found that skilled developers using AI tools could actually wind up spending 19% more time on a project, not less.

Of course, this isn't always the case — and using specially trained AI models for very specific tasks (like using Uma, Upwork's Mindful AI™ to write job posts and curate lists of skilled freelancers) can be a faster, more efficient way to use the technology.

AI productivity gains may increase, too, as more people become comfortable using the technology. Penn Wharton forecasts that AI's biggest boost to productivity will come in the early 2030s. You can potentially get there faster with the help of skilled AI consultants and engineers, though.

How much does it cost to hire an AI consultant?

Every consultant on Upwork sets their own rates; you can set up initial consultations with skilled AI professionals for $10 and up. These one-time consultations are a great starting point to learn what you need the most support for and how a freelance AI expert or machine learning engineer can help.

Find the right AI consultant faster with Upwork Business Plus

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