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AgentNuro · Attribute

TokenOps: token-level accounting and optimization for AI

TokenOps is the practice of managing AI token usage and cost at the level of tokens, the unit AI providers actually bill. It brings FinOps discipline to input, output, cached, and reasoning tokens for every request.

Get your free AI spend mapSee AI cost attribution
One request, token by tokenPer request

Support-Copilot · reasoning model

Input tokens3,200$0.009619%
Cached input8,400$0.00255%
Output tokens650$0.009819%
Reasoning tokens1,900$0.028557%
$0.0504for this requestsample data, for illustration only

Most of this request’s cost is reasoning tokens. They are billed, but they are not part of the visible answer.

What is TokenOps, and how is it different from cloud FinOps?

TokenOps manages AI cost at the level of tokens: how many were used, of which type, by whom, and at what price. Cloud FinOps manages capacity you provision. Token cost is set one request at a time, by how long the prompt is, how long the answer is, and how much the model reasons, so it needs a per-request view instead of a billing export.

Cloud FinOps
TokenOps
What you are billed for
Cloud FinOpsCapacity: VM hours, gigabytes stored
TokenOpsUsage: input, output, cached, and reasoning tokens
What drives cost
Cloud FinOpsProvisioned size and running time
TokenOpsPrompt length, response length, and how much the model reasons, on every request
How predictable it is
Cloud FinOpsFollows what you provision
TokenOpsVaries with every request, even for the same task
How you see it
Cloud FinOpsDaily or hourly billing exports
TokenOpsPer request, as it happens
Main levers
Cloud FinOpsRightsizing, commitments, scheduling
TokenOpsModel choice, caching, context trimming, budgets

TokenOps here means AI tokens, the unit language-model providers bill. It is unrelated to crypto token tooling.

What are the four kinds of tokens, and where does cost hide?

Providers price each kind of token differently. A single total hides which one is driving cost, so AgentNuro tracks all four separately.

01Input tokens
How it is billed

Everything sent to the model: the prompt, instructions, retrieved context, and tool definitions. Billed at the model's input rate.

Where cost hides

Instructions and context that grow over time and ride along on every request.

What AgentNuro shows

Input tokens per request, by model, team, and agent.

02Cached input tokens
How it is billed

Input the provider has already processed and can reuse. Billed at a lower rate on models that support caching.

Where cost hides

Prompts that change early, so the cache never gets a hit.

What AgentNuro shows

Cache hit behavior, and cached versus uncached input.

03Output tokens
How it is billed

Text the model generates. Billed at the model's output rate, which is usually higher than the input rate.

Where cost hides

Responses longer than the task needs.

What AgentNuro shows

Output tokens and cost per request, and the output-to-input ratio.

04Reasoning tokens
How it is billed

Thinking tokens that reasoning models generate before answering. Usually billed at the output rate and not part of the visible answer.

Where cost hides

Heavy reasoning on simple requests.

What AgentNuro shows

Reasoning split out from output, so its share of cost is visible.

How does token telemetry work?

Token telemetry is the per-request record of how many tokens of each type were used, by whom, and at what price. AgentNuro captures it, normalizes it across providers, and ties it to the people and agents behind each call.

  1. 1

    Capture

    Token counts for every request, read from provider and billing data or captured at the Gateway.

  2. 2

    Normalize

    Every provider reports tokens differently. They are mapped to one schema and priced at the rate actually billed.

  3. 3

    Attribute

    Each request is tied to a user, team, project, model, and agent, with no manual tagging.

  4. 4

    Act

    Findings feed budgets, alerts, routing, and caching, so the data changes the bill.

AgentNuro Insight · read-only

Reads token usage and cost through your provider, billing, and identity APIs. Nothing changes in your applications, and no prompt content is read.

How it connects
provider, billing, identity APIs
Changes in your apps
none

AgentNuro Control · per request

Captures every request at the Gateway as it happens, and can act on it by routing, caching, or capping. Each prompt is assessed in real time for complexity to pick the right model, and prompt content is not stored.

How it connects
inline Gateway
Prompt content stored
no

What does token-level visibility look like?

Every view reads from the same token data. Switch between tokens and cost, and between token type, model, team, and agent.

app.cloudnuro.ai/tokenops/usageLive

Token usage · last 7 days

Sample data, for illustration only

Total tokens482M
Token change+18%vs prior 7 days
Total cost$41,260
Cost change+11%vs prior 7 days

How does TokenOps reduce token usage and cost?

Token data shows where spend leaks. Four levers act on it. Insight finds the opportunity read-only, and Control applies it per request.

Right-size the model

Send routine requests to smaller models and keep premium models where they matter.

Insight finds it · Control applies it

Cache repeated work

Serve repeated or near-identical requests from cache, so they cost less or skip the provider call.

Insight finds it · Control applies it

Trim the context

Spot instructions and context that have grown, and the tokens that ride along on every request.

Insight finds it

Cap the spend

Set token budgets, alerts, and limits per team or agent, with a kill switch for runaway usage.

Insight alerts · Control enforces

Each lever holds to a quality and latency bar, so cost comes down without degrading answers.

Who uses TokenOps?

Finance, engineering, and AI leads each ask a different question of token usage. All three read the same normalized token data.

Finance

Which team spent this, and was it planned?

Token cost by team and project, ready for showback or chargeback, with the cost of each token type visible.

Engineering

Why did this request cost so much?

A per-request token breakdown, with cache behavior and reasoning share, so the cause is a number, not a guess.

AI and product leads

Which models and agents earn their cost?

Token usage and cost by model and by agent, with trends over time to guide model and workflow choices.

Where TokenOps fits in AgentNuro

TokenOps is the measurement layer. These capabilities build on it.

AI Cost Attribution

Tie every AI dollar to the user, team, project, model, and agent that generated it.

Learn more

Token & Cost Optimization

Cut waste without slowing teams down, by right-sizing models and removing repeated work.

Learn more

Workflow Caching

Stop paying twice for the same work with exact and semantic caching.

Learn more

Model Routing

Send each request to the most cost-effective model that still meets the quality bar.

Learn more

Looking for the definition? Read the TokenOps entry in the glossary.

Frequently asked questions

TokenOps is the practice of managing AI cost at the level of tokens, the unit AI providers actually bill. It brings FinOps discipline to input, output, cached, and reasoning tokens for every request, so teams can see, attribute, and reduce token usage and cost.

Cloud FinOps manages capacity billed by the hour or the gigabyte, which follows what you provision. TokenOps manages cost that varies with every request: how long the prompt is, how long the answer is, and how much the model reasons. That needs per-request visibility, not a daily billing export.

Providers bill by the token, and each token type is priced differently. A total spend number cannot tell you whether cost came from long prompts, verbose answers, missed cache hits, or heavy reasoning. Measuring at the token level shows which one it is, and so which fix will work.

By acting on what token data reveals: routing routine requests to smaller models, serving repeated work from cache, trimming context that rides along on every call, and capping spend with budgets and alerts. Insight surfaces the opportunities read-only. Control applies them per request through the Gateway.

TokenOps focuses on language-model usage, which is billed in tokens, including the agents, copilots, and applications built on those models across OpenAI, Claude, Gemini, Copilot, Bedrock, Vertex, and Azure AI Foundry. Spend that is not billed in tokens, such as per-seat licenses, is covered elsewhere in the platform.

Cached input tokens and reasoning tokens are tracked as their own categories, not folded into input and output. You can see cache hit behavior and how much of a request’s cost is reasoning, the two places where a combined total hides the most.

No. Insight reads usage and billing data through provider, billing, and identity APIs, not prompts. With Control, the Gateway assesses each prompt in real time for complexity, to choose the right model. Prompt content is not stored.

Know where every token goes.

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