What Is an AI Token Cost?
An AI token is a small unit of text that an AI model processes. It can represent part of a word, a whole word, or punctuation. AI providers typically charge based on the number of tokens processed. Input tokens are the information sent to the model, while output tokens are the model’s response. Output tokens often cost significantly more than input tokens. For financial companies, token costs can escalate quickly. A customer chatbot may handle thousands of conversations daily, and an investment research system may process lengthy reports and filings. The number of users is only part of the equation — the amount of information processed matters just as much.
Why AI Token Costs Are Hard to Control
Traditional technology budgets are often easier to forecast. AI consumption can change with every interaction. A simple request might use few tokens, while another could include documents, previous conversations, and detailed instructions. AI agents add another layer of complexity: one user request can trigger multiple model calls, tool calls, and retries, all increasing the final bill. Financial companies also use different models for different tasks. Using the most powerful model everywhere can create unnecessary spending.
How Financial Companies Can Track AI Spending
The first step is creating clear visibility into AI consumption. Companies should know which teams use each model and track usage by application, workflow, and business function. Useful metrics include cost per request, cost per user, and cost per completed task. An AI gateway or proxy can help collect this information and connect API usage with teams, applications, and internal cost centers. The FinOps Foundation recommends API governance and proxy layers for stronger attribution. Financial companies should also establish clear ownership: technology teams control architecture, finance teams manage budgets, and security and procurement have important responsibilities.
Reduce Costs Through Smarter Model Selection
Model selection significantly affects AI spending. Not every financial task needs a premium model. A smaller model can effectively handle document classification or simple customer-service questions. More advanced models should be reserved for complex research or reasoning tasks. This approach is known as model routing — automatically sending requests to the most appropriate model based on complexity. Prompt design also matters: unnecessary instructions and repeated information increase token usage. Companies should remove irrelevant context and summarize older conversations before sending them again.
Use Caching, Batching, and Workflow Controls
Repeated information does not always need to be processed from scratch. Prompt caching can reduce repeated input-token costs, especially for financial applications using stable documents or instructions. Research systems may repeatedly reference the same datasets or regulatory materials. Batch processing can also lower costs for non-urgent workloads by processing large groups of tasks together. Agentic workflows require additional controls: limit unnecessary retries and repeated tool calls, and monitor agent loops and escalation paths. A McKinsey report identified caching, model selection, prompt optimization, and workflow controls as important AI cost levers.
Build AI Cost Management Around Business Value
Financial companies should not treat token spending as only an IT problem. The bigger question is whether that spending creates measurable business value. A cheaper model is not useful if it produces poor results, while an expensive model can be justified for high-value financial analysis. Companies should connect AI costs with measurable outcomes such as processing time, customer resolution rates, or analyst productivity. The emerging discipline of “tokenomics” focuses on this broader relationship, considering model selection, routing, workflows, infrastructure, and business value as a whole. AI costs will remain an important concern as adoption expands. Financial companies need visibility before optimization and governance before AI spending becomes difficult to control. The strongest approach combines finance, technology, security, and business teams. Token costs are only one part of AI economics — managing them effectively requires understanding the entire workflow behind each AI request.
