Token Economics: Understanding and Controlling AI Costs at Scale

AI is priced per token. A token is roughly three-quarters of a word. Every request has input tokens — your prompt — and output tokens — the model’s response. Input and output tokens have different prices. Cached tokens have a third price. Different models have wildly different rates.

At enterprise scale, understanding these economics is the difference between a sustainable AI program and one that gets shut down by finance.

The pricing landscape

Every major model provider prices differently. Input tokens are typically cheaper than output tokens, sometimes by a factor of three to five. Some models charge for “thinking” tokens separately, adding a third pricing dimension that most teams do not account for.

Prices also change frequently. A model that cost $15 per million output tokens last quarter might cost $10 this quarter, or $20. Provider pricing pages update without announcement. If your cost projections are based on a static rate card, they are already wrong.

The result is a matrix of dozens of models across multiple providers, each with its own input rate, output rate, cache rate, and thinking rate. Managing this manually does not work past a handful of requests.

Cache economics

Many providers offer prompt caching, where repeated system prompts are stored and charged at a reduced rate on subsequent requests. This sounds simple. In practice, it introduces meaningful complexity.

Cache creation tokens are charged at a premium — you pay more the first time a prompt is cached. Cache read tokens are charged at a steep discount on subsequent requests. The economics only work if your cache hit rate is high enough for the discounted reads to offset the initial creation cost.

AOSentry tracks cache creation tokens and cache read tokens as separate line items on every request. Knowing your cache hit rate tells you whether your prompt design is cost-efficient or whether you are paying creation premiums without reaping read discounts.

Most teams have no idea what their cache hit rate is. They are flying blind on one of the most impactful cost variables.

The model selection lever

The single biggest cost variable is model selection. Using a frontier model for a task that a mid-tier model handles equally well costs ten to twenty times more. Using a reasoning-optimized model for straightforward summarization wastes money on capabilities the task does not require.

Intelligent routing — sending each request to the cheapest model capable of handling it — is the most impactful cost optimization available. A classification task does not need the same model as a multi-step reasoning chain. A simple extraction does not need the same model as a nuanced content generation request.

Organizations that treat model selection as a one-time architectural decision leave enormous savings on the table. The right approach is dynamic: route by task complexity, fall back to more capable models only when cheaper ones fail quality thresholds.

Per-request cost attribution

AOSentry logs every request with full token detail: prompt tokens, completion tokens, cache creation tokens, cache read tokens. It calculates the cost of each request using current pricing for the specific model variant used, covering more than fifty model variants across all major providers.

This is not aggregate reporting. It is per-request, per-user, per-team attribution. When a single team is responsible for 40% of your AI spend, you know. When a specific workflow is generating outsized costs relative to its value, you see it immediately.

Real-time spend dashboards break costs down by user, team, model, and provider. Finance gets the accountability they need. Engineering gets the data they need to optimize. Leadership gets a clear picture of where AI budgets are going.

Prompt engineering for cost efficiency

AOSentry’s cache tracking creates a feedback loop for prompt design. Teams can see whether their system prompts are generating cache hits or whether each request is paying full price.

Effective prompt caching requires consistency. System prompts that vary slightly between requests defeat the cache. Teams that standardize their system prompts and minimize per-request variation see cache hit rates above 80%, translating directly to lower per-token costs.

This is not theoretical. It is measurable on every request, and AOSentry surfaces the data automatically.

Practical optimizations

Use cheaper models for routine tasks. Classification, extraction, simple summarization, and formatting work do not require frontier models. Reserve expensive, high-capability models for complex reasoning, nuanced generation, and tasks where quality differences are measurable.

Design system prompts for cache hits. Keep the static portion of your prompts large and consistent. Vary only the user-specific portion. The higher your cache hit rate, the lower your effective per-token cost.

Set per-user and per-team budgets. Cost awareness changes behavior. When teams see their own spend, they self-optimize. They stop using expensive models for trivial tasks. They start batching requests. They think about whether a request is worth sending.

Monitor model-level spend to identify optimization opportunities. If 60% of your budget goes to a single model, that is where you focus. If a cheaper model can handle even half of that workload, the savings compound quickly.

AI costs are controllable if you have visibility and the right levers. Most organizations overspend because they lack per-request cost attribution. They see a monthly bill from a provider and have no way to trace it back to specific teams, workflows, or decisions. AOSentry provides both the visibility and the controls — per-request tracking, automatic cost calculation, cache analytics, budget enforcement, and real-time dashboards that turn opaque AI spending into a managed line item.