Ameya Kanitkar is the Co-founder and CTO of Larridin, a Bay Area-based startup building an organizational platform powered by AI.
Artificial intelligence (AI) has quickly become one of the fastest-growing categories of enterprise technology spending. Gartner researchers predict that by 2028, the cost of AI coding tokens alone will surpass the salary of an average software developer.
That projection reflects how quickly AI has become a core operating expense that comes with a challenge: Many organizations still lack visibility into where those tokens are being consumed and whether they’re delivering business value.
Why AI Spending Is Difficult To Track
Why is AI spending hard to track? Because it’s likely to change every day.
Over decades, IT organizations have developed mature practices for managing cloud infrastructure, software licenses and hardware investments. For software, cost per user can be calculated accurately and budgeted for on an annual basis.
AI introduces an entirely different financial model. Every prompt, coding assistant, autonomous agent and API call consumes tokens. Both the prices set by vendors and the way AI is used are changing rapidly. This creates a variable operating expense that changes minute by minute and that often increases sharply over just a few weeks or months.
Cloud computing grew more slowly, giving organizations years to create governance and FinOps practices. AI adoption is happening much faster.
Today, token spending is spread across cloud model providers, AI platform subscriptions, browser plugins, desktop agents, custom connectors and API gateways. Each source has its own billing model, reporting system and usage dashboard. The result is a new technology expense that’s both fast-growing and poorly understood.
A common assumption is that AI spending will mainly come from a handful of enterprise subscriptions. In reality, token consumption is scattered across an organization’s entire AI ecosystem, including unsanctioned, “shadow” AI.
Some costs come from foundation model providers such as OpenAI, Anthropic and Google, as well as large partners such as Microsoft. Others come from coding assistants, enterprise chatbots, browser extensions, desktop AI agents, custom connectors embedded in internal applications and API gateways routing requests across multiple models. Finance teams receive invoices from different vendors, while IT monitors the tools they manage. Meanwhile, leaders see productivity upticks in various parts of their organizations but often have little understanding of the reasons for them or the costs behind them.
What The Data Tells Us
Data collected across Larridin’s enterprise customers shows that Anthropic’s Claude LLM Sonnet model receives 70% of API prompts while accounting for 51% of API spend. Claude’s Opus model handles only 27% of prompts but consumes 48% of the spend because each prompt costs three to four times more than for Sonnet. Looking only at total spending or prompt volume is just one part of a larger story.
AI adoption’s also accelerating faster than many budgeting processes can keep up with. Over a 10-week period, Claude’s active users increased by 41%, and sessions across enterprise customers increased by 76%; both user count and usage per user grew rapidly. A budget built at the beginning of the year can become outdated in a few months or even weeks as new models, agents and workflows are introduced.
AI’s Specific Budgeting Rules
Traditional technology budgets rely on predictable patterns. Companies know roughly how much infrastructure costs are likely to grow as headcount increases. SaaS licenses renew on a fixed schedule. Cloud spending, which grew unpredictably when the technology was new, can now be forecast using years of historical data.
AI doesn’t behave that way.
Usage varies dramatically across employees, teams and workflows. One engineer may occasionally use a coding assistant. Another may rely on multiple AI agents throughout the day, supporting coding, testing, documentation and research. Two departments with similar headcounts can generate very different AI costs depending on the models they use, the complexity of their work and their sophistication in using AI cost-effectively.
Cost alone also tells very little about value. Proprietary data from my company shows that Claude Sonnet 4.5 averages about $0.06 per prompt, while Claude Opus 4.1 averages about $0.22 per prompt. The higher cost doesn’t necessarily represent waste. Many teams intentionally reserve more expensive models for complex work, where better reasoning and higher-quality outputs justify the additional expense.
But users don’t see costs directly when they choose a model or enter a prompt. Without that context, budgeting becomes guesswork. Some organizations have slowed the rate of AI adoption because costs appear to be rising too quickly. Others continue increasing budgets to meet employee and departmental demand without knowing whether additional token spend is producing meaningful business outcomes.
AI Spend Requires A New Playbook
Managing token spend starts with a complete inventory of where AI costs originate. Organizations should track usage across cloud models, AI subscriptions, browser plugins, desktop agents, custom connectors and API gateways, instead of treating each source as a separate expense.
The next step is connecting token spend to the work being performed. Leaders should understand which workflows, teams and projects generate the strongest return, instead of relying on vendor invoices and usage dashboards alone.
Finally, companies should establish ROI guardrails. The goal is to identify the point where additional AI investment stops improving productivity, software quality or business outcomes. That threshold will be different for every organization, but it can’t be measured without understanding both cost and impact.
Token spending is becoming a permanent line item in enterprise budgets. Managing it successfully means understanding where every token is going, why it was used and what value it created. That’s the foundation for making more solid plans and smarter AI investments as adoption and usage continue to grow.
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