Not long ago, running a capable AI model at scale was something only the largest technology companies could afford. That is no longer true. The price of using AI has fallen so fast that the question for most businesses has shifted from whether they can afford AI to whether they can afford to ignore it.

This is the great acceleration: when a technology becomes dramatically cheaper, it spreads into places no one planned for. Falling AI costs are pulling automation into small businesses, back offices and industries that were never early adopters. But cheaper per use does not automatically mean cheaper overall, and the organizations that benefit most will be the ones that understand the economics rather than just the hype.

What the numbers show

The clearest data comes from Stanford’s 2025 AI Index. It found that the cost of querying a model performing at the level of GPT-3.5 on a standard benchmark fell from about $20 per million tokens in November 2022 to about $0.07 per million tokens by October 2024, a drop of more than 280-fold in roughly two years. The same report noted that hardware costs had been falling by around 30% a year while energy efficiency improved by about 40% a year.

Adoption followed. The AI Index, drawing on McKinsey survey data, reported that 78% of organizations said they used AI in 2024, up from 55% the year before. That is a jump of 23 percentage points in a single year, one of the fastest adoption curves for any business technology.

Why AI got so much cheaper

Several forces pushed costs down at once.

Better hardware and cloud infrastructure. Newer GPUs and specialized AI chips deliver more computation per dollar and per watt, and cloud providers such as AWS, Microsoft Azure and Google Cloud compete aggressively on AI pricing.

More efficient models. Techniques such as distillation, quantization and improved architectures let smaller models match what much larger ones could do a year or two earlier. The capability once reserved for the biggest models keeps moving down into models that cost a fraction as much to run.

Open-weight competition. Freely available models from companies such as Meta, Mistral and others narrowed the gap with closed commercial models, giving businesses the option to run capable models on their own infrastructure and putting pressure on prices across the market.

Small language models for specific jobs. For focused tasks such as classifying documents, extracting fields or routing support tickets, a small, fine-tuned model can perform well at a very low cost, often without sending data outside the organization.

What cheaper AI makes possible

Falling costs change which projects make financial sense. Uses that were uneconomic at 2022 prices now pay for themselves:

• Always-on customer support that handles routine questions around the clock and hands complex cases to staff.
• Document processing at volume, such as invoices, contracts, claims and forms, where AI extracts and checks information that used to be keyed in by hand.
• Personalization at scale in marketing and sales, where every message can reflect the customer’s history.
• Analysis for everyone, letting staff query business data in plain language instead of waiting for a report.
• Agents that complete multi-step tasks, such as researching a lead, updating the CRM and drafting follow-up, which only become practical when each step is cheap.

The common thread is volume. When each AI action costs a fraction of a cent, the business case no longer depends on finding one dramatic use case. It can come from thousands of small, routine tasks that each save a few minutes of someone’s day.

The catch: cheaper per use can still mean bigger bills

There is an old pattern in economics, often called the Jevons paradox: when using a resource becomes more efficient, total consumption can rise enough to outweigh the savings. AI follows the same pattern. As per-token prices fall, organizations use far more tokens, deploy more use cases and adopt more capable models that use more computation per answer.

Agentic workflows amplify this. An agent that plans, calls tools and checks its own work can use many times more computation than a single chatbot reply. Multiply that across thousands of tasks and a pilot that cost almost nothing can become a significant line item. We look at how that happens in AI Cost Overruns in 2026.

Falling costs lower the barrier to entry. They do not remove the need for cost discipline.

How to turn falling costs into advantage

The organizations pulling ahead are not the ones spending the most. They are the ones matching the right model to each job and measuring results. A practical approach:

1. Start with one process that is high-volume and repetitive, and measure its current cost and cycle time.
2. Match the model to the task. Use smaller, cheaper models for simple steps and reserve larger models for work that genuinely needs them.
3. Set budgets and alerts on AI usage from day one, by team and by application.
4. Measure value, not activity. Track hours saved, errors avoided or revenue influenced, not just the number of AI requests.
5. Revisit choices regularly. Prices and models change every few months; a design that was optimal last year may be overpaying today.
6. Keep security and data governance in step, since cheaper tools spread faster, including tools nobody approved.

Frequently asked questions

Is AI now affordable for small businesses?

For many uses, yes. Per-use costs have fallen so far that small businesses can automate customer support, document handling and analysis without large budgets. The main costs are now integration, data preparation and change management rather than the model itself.

Should we wait for AI to get even cheaper?

Prices will likely keep falling, but waiting has its own cost: competitors build experience, data and workflows in the meantime. Start with a focused, measurable project, and design it so you can switch to cheaper models as they arrive.

Are small language models good enough for business use?

For narrow, well-defined tasks, often yes, and they can be cheaper, faster and easier to run privately. For open-ended reasoning or complex writing, larger models usually still perform better. Many organizations use both.

Making the acceleration work for you

Delana Technologies helps businesses choose the right AI models for each task, integrate them into existing systems and keep costs, security and results under control. Learn about our AI consulting and agentic AI solutions and workflow automation and systems integration. To find your first high-value AI project, call 239.414.5126 or contact us.

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Sources: Stanford HAI, The 2025 AI Index Report (including McKinsey survey data on organizational AI use); Tom’s Hardware coverage of the 2025 AI Index (April 2025).