Solutions | token meter

Your AI bill tells you what you already spent. Token Meter tells you while you're spending it.

Token Meter tracks token use live and applies limits before the spend is locked in. You can see exactly which service, app, team, user, agent, or model is using the tokens, and cap that one without touching the rest.

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How it works

Saying no to AI agents was never really an option. Then the token counts started climbing.

The business wanted them. Developers wanted them. Nobody wanted to be the one slowing things down. So you said yes, and nobody had a close enough view to see what came next.

Agents that overdo the job

An agent rereads the whole codebase to find one file. It fills the full context window on every call. It retries a flaky endpoint hundreds of times. Any of these is fine once. Repeated thousands of times at machine speed, it gets expensive fast.

Agents that drift

An agent starts doing more than it was built for, and its task doesn't explain why.

Agents pushed off course

It doesn't happen often, but it's the case security teams worry about most.

Catch AI spend before it turns into a bill.

Operant shows your team token use as it happens and gives you the controls to rein it in before it becomes a surprise on the invoice.

0.00M / 12.00M tokens

Organization budget · this month

0% used

Token use

Activity

A bill isn't a meter.

The best a provider's dashboard can do is show you a number. By the time you see it, the money is already spent.

Deeper usage reporting

Only on higher-priced plans

YOU PAY EXTRA TO SEE MORE

Usage visibility

Delayed, not live

BY THEN, THE MONEY IS SPENT

Deployment coverage

First-party only

BEDROCK, VERTEX, AND FOUNDRY AREN'T SHOWN

Runtime enforcement

None

YOU FIND OUT AFTER YOU'RE CHARGED

You need both of these. Either one alone falls short.

Spend doesn't start at the account level. It starts with one agent, in one app, for one user on one team, calling one model.

Visibility down to the source

Break usage out by service, application, team, user, agent, and model, so you can find exactly where the spend comes from instead of staring at one blended total.

■ On its own: a better dashboard. You watch the number go up and still pay the bill.

Limits at the same level of detail

Set a limit on that exact agent, user, or model, and it applies while the tokens are being used.

■ On its own: a kill switch. You save money by shutting off work the business depends on.

SERVICE

APPLICATION

TEAM

USER

AGENT

MODEL

8.42M
TOKENS

LIMIT · 26%

ServiceALL WORKLOADS

ApplicationSUPPORT-COPILOT

TeamENGINEERING

UserJ. OKONKWO

AgentCONTEXT-LOOP

ModelCLAUDE-SONNET

TOKENS IN METERED LIMIT FIRES AT THE AGENT — FLOW CONTINUES PAST IT

THE LIMIT BINDS ONE RING ONLY
the agent is throttled to 26%; every scope around it keeps flowing untouched

Five ways this plays out in practice.

In every one of these, a single account-wide number would have hidden the cause. And the only way to stop the spend would have been to shut everything down.

Coding agents across a few hundred engineers

A handful of agents were set up to reread large repos on every step, and those few ran up most of the month's token spend. The account total kept climbing, but it never showed which agents were behind it.

With a live per-agent cap, those agents would have been throttled on day one and most of the bill would never have happened. Everyone else would have kept working as usual.

A retrieval pipeline with no context limit

On some queries it maxed out the context window on every call and kept looping. Each request looked fine by itself. Added up, it spent several times what the job needed.

A live per-application limit would have capped that one pipeline early and left the rest of the data stack alone.

An eval suite pointed at a production model

It ran on every commit and worked exactly as designed. It also burned production tokens nonstop for two weeks before anyone linked the invoice to the CI job.

A per-service token limit would have flagged and throttled it within hours of spend going above normal. Two weeks of waste becomes a same-day fix.

A customer-facing agent that defaulted to the largest model

During a traffic spike it sent every request to the most expensive model, even the simple ones. There was no per-model policy, so cost per conversation went up about tenfold for days.

A live per-model cap would have saved the expensive model for the requests that really needed it.

An agent whose usage spikes for no clear reason

Usually it's a bug. Sometimes the agent has been steered somewhere it shouldn't be. This is the least common case, and the first one security leaders ask about.

With live metering, the limit on that agent has already kicked in before the cost or the risk can grow.

Three capabilities in one place.

Token Meter combines usage visibility, live budget limits, and coverage for every model you deploy. You get one view and one set of policies for all of it.

See usage as it happens

Token metrics for each agent session in near real time, split by user, team, agent, and model. You also get usage insights, recommendations, and a record of what was blocked.

Enforce budgets while agents run

Token limits that can step in mid-session. Set them per user, team, agent, or model, and add caps for the whole organization.

Cover every deployment

Works with every model, including those hosted on Bedrock, Vertex, and Foundry, which provider dashboards don't show. One policy covers all your agents, however they're deployed.