By Steve Cole in cost optimization — 14 Nov 2025

Smart AI Usage for Businesses: A Cost-Focused Guide

A good friend of mine once said to me "like it or not, AI is here to stay." He couldn't have been more correct. It's almost impossible to have a business conversation lately with the topic coming up. Massive enterprises are downsizing to reduce costs and expedite results. Developers are using it for coding, marketing teams are generating content, and leadership is exploring agentic workflows. The benefits of AI are real, and so are the costs.

Perhaps your company is considering AI. If so, do the research and make informed decisions. Or maybe your company is already utilizing AI. If so, you're probably aware of the costs already, but it never hurts to revisit finances, especially with investments of this magnitude. For many organizations, AI costs are spiraling out of control—not because they're using AI wrong, but because they don't understand where the real costs hide.

This guide cuts through the hype to show you exactly where your AI budget goes and how to optimize it. Whether you're running inference workloads in the cloud or managing employee subscriptions, understanding these cost dynamics is essential for sustainable AI adoption.

Understanding AI Cost Models

AI compute costs come in three primary models. Each serves a different purpose, but ultimately rely on the same final resource.

Subscription Model

Most people familiar with AI are familiar with subscription models. These are end-user tools that people use to help write documents, vibe code, or get recommendations for wedding gifts and other mundane tasks they used to use search engines for.

Products like ChatGPT Plus, Claude Pro, and GitHub Copilot offer fixed monthly fees ($20-200 per user) for individuals or teams with varying usage limits. These are great for predictable employee usage but can become expensive at scale. For example, a 50-person team on these types of subscriptions could cost anywhere $10,000-100,000 annually.

API Model

Behind the scenes, AI tools and applications rely on API services, which are also used for building services like chatbots, and agentic AI. API services are typically used by developers building applications of their own, or for very heavy code assistance.

AI services like OpenAI, Anthropic, and Google offer API access to their models, charging per token (e.g. words, or portions thereof). This includes both inputs that sent (prompts) and outputs received (response) through units called tokens, which you can think of as words (or portions thereof). Prices vary dramatically by model: smaller models might cost $0.15 per million tokens, while frontier models can cost $15 per million tokens or more.

Compute Model

The conventional model, from before the rise of API tools and services, was use to use computers with the same high-powered GPUs that are used to provide the tools and services mentioned above. Business with long-running and persistent needs like inference at scale, training, or fine-tuning models require this.

Compute access for this is typically provided directly with a computer equipped with high-powered GPUs (requiring setup and configuration) or through high level services, billed by the hour. Even a single training run on GPU machines can cost hundreds or thousands of dollars. Most businesses won't need this initially, but it's critical for specialized applications. It can also be a challenge as you compete with analytic and other consumers of GPU resources.

Cloud AI: Where the Big Money Lives

When AI moves from employee tools to production systems, costs change dramatically. Here's where businesses often get caught off guard:

Model Inference at Scale

Running AI inference for customer-facing applications is where costs compound quickly. Consider a chatbot handling 10,000 customer queries daily. At an average of 1,000 tokens per interaction (both input and output), that's 10 million tokens daily or 300 million monthly. At $3 per million tokens for a mid-tier model, you're spending $900 monthly on inference alone. Scale that to enterprise levels—100,000 daily interactions—and you're at $9,000 monthly or $108,000 annually.

The real danger is in the multiplication effect: more users × more features × longer conversations = exponential cost growth. One enterprise customer reported their AI chatbot costs jumped from $2,000 to $15,000 monthly after adding conversation history retention, which tripled average token consumption.

Agentic Workflows: The Cost Multiplier

This is the biggest cost trap in modern AI. Agentic systems—where AI makes decisions, uses tools, and chains multiple actions—create compound costs that can spiral quickly.

Here's why: A simple query might trigger 5-10 separate AI calls. Each call includes:

The full conversation context (often thousands of tokens)
Tool descriptions and instructions
Previous tool results
The agent's reasoning process

A single user question might consume 50,000 tokens across multiple agentic calls, compared to 2,000 tokens for a simple chatbot response. That's a 25x cost multiplier. At scale, agentic workflows can become your largest AI expense category.

Real-world example: A company building an AI research assistant found that each research task triggered an average of 8 agent calls, consuming 40,000 tokens. With 500 research tasks daily, they were processing 20 million tokens daily—$1,800 monthly at $3 per million tokens. Optimizing their agent architecture reduced calls to 4 per task, cutting costs in half.

Training and Fine-Tuning

Training custom models is expensive but often overestimated as a cost driver. Most businesses won't train from scratch but may fine-tune existing models for specific domains. Fine-tuning often costs $20-200 per run depending on dataset size and model complexity. It's a one-time or occasional expense, not ongoing like inference.

The bigger consideration: fine-tuned models often require more expensive hosting. You're trading training costs for potentially higher inference costs if the fine-tuned model is larger or slower than alternatives.

Analytics and Data Processing

Using AI for document analysis, data extraction, or batch processing creates different cost patterns. Processing large PDFs or datasets means high input token counts. A 50-page document might be 30,000 tokens. Processing 1,000 documents monthly costs $90 in input tokens alone (at $3 per million).

However, analytics workloads are often predictable and batchable, making them easier to budget than interactive applications.

Employee AI Usage: The Hidden Costs

While cloud AI dominates headlines, employee AI tool costs add up quickly and are often overlooked in budgeting.

Individual vs. Team Subscriptions

The math is deceptively simple but impactful:

Individual subscriptions: $20-2000 per user monthly
Enterprise plans: Often $25-35 per user with volume discounts
GitHub Copilot: $10-19 per user for code completion

For a 50-person company where everyone uses AI tools, you're looking at $1,000-2,000 monthly or $12,000-24,000 annually. That's before any API usage for custom applications.

The hidden cost: underutilization. Many companies buy licenses for all employees but find only 30-40% are active users. You're paying for seats that generate no value. Better approach: start with power users, measure usage, then expand.

Usage Patterns That Drive Costs

Not all employee AI usage costs the same. A developer using Copilot generates constant but low-cost code completions. A marketer generating long-form content might hit rate limits quickly. An analyst processing spreadsheets with AI might trigger expensive data operations.

Monitor which teams consume the most resources. Often, 20% of users account for 80% of costs. Understanding these power users helps you allocate budget and potentially negotiate custom pricing.

Smart Strategies for Managing AI Costs

Now for the practical part: how to control costs without sacrificing AI's value.

Model Selection: The 80/20 Rule

Not every task needs GPT-4 or Claude Opus. Smaller models cost 10-50x less and handle most tasks well:

Simple classification or extraction: Use smaller models ($0.15-0.50 per million tokens)
Complex reasoning or coding: Use frontier models ($3-15 per million tokens)
Structured data processing: Consider specialized models optimized for your domain

Implement a tiered approach: route simple queries to cheaper models, escalate complex ones. This alone can reduce inference costs by 60-70%.

Caching and Context Management

Many AI providers now offer prompt caching—storing repeated context to reduce input token costs. If your application sends the same 10,000-token system prompt with every request, caching eliminates 95% of those input costs.

For agentic workflows, aggressive context management is critical. Don't send the entire conversation history with every agent call. Summarize previous interactions, remove irrelevant tool results, and maintain only essential context.

Batch Processing

Many AI providers offer batch processing at 50% lower costs in exchange for slower turnaround (typically 24 hours). Perfect for:

Document analysis and extraction
Data classification
Content generation pipelines
Model evaluation and testing

Not suitable for interactive applications, but ideal for backend workflows.

Monitoring and Optimization

You can't optimize what you don't measure. Implement cost tracking at the feature level:

Track token usage per endpoint/feature
Monitor average tokens per request
Identify outlier requests (unusually high token consumption)
Set up alerts for cost spikes

Many companies discover that a single poorly-optimized feature accounts for 40-50% of costs. Quick wins come from identifying and fixing these outliers.

Distillation: The Long-Term Play

Once you've validated a use case with expensive frontier models, consider model distillation: training a smaller, cheaper model to replicate the larger model's performance on your specific task. This requires upfront investment but can reduce inference costs by 10-20x for high-volume applications.

Best for: stable, high-volume use cases where quality requirements are well-defined. Not suitable for rapidly evolving features or low-volume applications.

The Bottom Line

Smart AI usage isn't about spending less—it's about spending strategically. The companies winning with AI understand where costs concentrate and optimize accordingly:

Agentic workflows are your biggest cost risk. Optimize agent calls aggressively.
Model selection matters more than you think. Use smaller models wherever possible.
Employee subscriptions need active management. Monitor utilization and adjust.
Caching and batch processing are low-hanging fruit for 40-50% cost savings.

It can be argued that the AI hype is slowing down and that there's a bubble bound to burst, but that doesn't mean what we have today won't get better and cheaper and that your business shouldn't use it. Just be sure to make conscious decisions about how you use it and what your expectations are. Consider the 80/20 rule... AI may be able to do 80% of the work in 20% of the time, but the inverse is true as well; experienced people are going to spend 80% of the time checking and correcting 20% of that work.

I use AI every day. I have multiple subscriptions to multiple services. Each subscription was a conscious decision based on what I need, how much I need it, and what value it brings to my productivity.

If you're interested in optimizing cloud spend, migrating workloads, or modernizing systems, my team and I can help. Learn more at thesteveco.us.