Who spends the tokens. On which models. From where.
Consumption you can actually govern - not a bill you read after the fact.
Every request the Enforcement Fabric adjudicates is written to the Ledger. Token Manager reads that record back as the three questions finance and security keep asking: who is consuming, what they reach, and where the traffic goes.
Console preview - illustrative figures.
Consumption
The few accounts that drive most of the bill.
Token spend follows a long tail - a handful of users and agents account for the majority of it. Token Manager ranks them off the Ledger, with the model mix beside it, so the conversation is about the accounts that matter, not an aggregate you can't act on.
Source & egress
Tokens spent isn't the risk. Where they went is.
The same model is a different risk depending on how it's reached. Reach it over the API and it's gated by the Enforcement Fabric; paste a prompt into a browser tab and it's shadow AI the boundary never saw. That's why every model in the mix above carries an egress tag - one number tells you how much of your spend is actually under control.
Reached over the API and adjudicated by the Fabric before it leaves.
Consumer endpoints (e.g. a browser tab) the boundary never sees - the egress to govern first.
Self-hosted or private-cloud models that never leave your network.
Console preview - illustrative figures.
Token Insights
Read by Quest, on the fly.
The numbers are only half the value. Quest reads the same consumption record and surfaces what to do about it - where spend is wasted, where egress escapes policy, and where a tighter Edict would help. Every insight traces back to a row in the Ledger you can open.
Token Optimization Intelligence
A model that learns how your organization spends - and where to trim it.
Reading the numbers back is only the start. Token Manager includes a machine-learning layer that learns your organization's own consumption patterns - which teams, agents, models, and prompts drive spend, and how that shifts week to week: a bloated system prompt here, an oversized context window there. Trained on your token data and nobody else's, it surfaces concrete, ranked ways to cut cost without changing what a request is allowed to do. Because the data is yours, so are the recommendations - and each one traces back to the Ledger rows it was learned from.
Right-size the model
It spots requests sent to a frontier model that a smaller one would clear at the same verdict, and recommends routing them down - no change to what the request is allowed to do.
Cache & de-duplicate
It detects near-duplicate prompts and cacheable calls across teams and agents, so the same answer is not paid for twice.
Trim the prompt
It flags bloated system prompts and oversized context windows that inflate every call, and suggests leaner versions that keep the same output.
Learns only from your deployment's own Ledger - your token data never trains a shared model.
Where the data comes from
A side effect of enforcement, not another agent to install.
Adjudicate
Every prompt, retrieval, and output passes through the Enforcement Fabric and is evaluated against policy.
Record
The decision - tokens, model, destination, verdict - is written to the append-only Ledger as it happens.
Read back
Token Manager queries that record. No separate collector, no sampling, no traffic that bypassed the boundary.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.