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Token Spend & Insights

Account for every AI token and dollar.

See, understand and control every dollar your company spends on AI.

Token & Spend Insights

Example · Month to date

All-in cost

$183,847

$122,554 usage + $61,293 fixed licenses

Spend breakdown

Coding agents$52,000
ChatGPT Enterprise$18,000
Lead Prospecting Agent$12,847
5 more surfaces$101,000

Usage by actor

Session split

Human · 73% Agents · 27%

Spend split · variable usage

Human · $33,847

Agents · $88,707

$40

variable cost / human

$485

variable cost / agent

Attribution · selected surfaces

Claude CodeEngineering$18,3927h / dev
Lead Prospecting AgentSales$12,8477h / SDR

Alerts

Budget nearly exhausted. reporting-pipeline-v3 switched to a cheaper model at 90% of budget.

Trusted by AI-forward enterprises

  • Vertiv
  • OK! magazine
  • Gainsight
  • The Signatry
  • Klaviyo
  • SurveyMonkey
  • Globality
  • TigerConnect
  • ConnectPay
  • The Joint Chiropractic
  • EcoVadis
  • Rev.io
  • Belcorp
  • Sundt
  • Polk County, WI
  • Source Advisors
  • Andelyn Biosciences
  • University of Hertfordshire

This is the scale

AI spend is growing fast. Get ahead of it.

Tokens processed across Google products

1 billion+

tokens per second on average, calculated from Google’s May 2026 monthly total.

“Some out there might call this tokenmaxxing, and there’s probably some truth to it.”

Google I/O, May 2026 · Sundar Pichai quote

4mo

to budget zero at Uber

Uber reportedly exhausted its 2026 budget for AI coding tools within four months. Its COO questioned how rising usage translated into useful consumer features.

Fortune, May 2026

$6M+

in unplanned annualized costs

A healthcare enterprise consumed 1 trillion tokens over six months before finance could identify the cost driver.

Deloitte, 2026

78%

of surveyed IT leaders

AI-native app spending grew 108% year over year. In a survey of 218 IT leaders, 78% reported unexpected charges tied to consumption-based or AI pricing.

Zylo 2026 SaaS Management Index

01 Integration

Consolidate spend across multiple sources.

Token usage, seat licenses, cloud model calls, API gateways. They all measure cost differently. Larridin connects to every source and maps them into a single spend model, automatically.

All sources, one spend model
No manual exports or spreadsheets

$183.8K

total spend MTD

8

surfaces in use

183

agents running

Spend sources

Example · 6 connected · syncing

Browser plugins

ChatGPT, Claude, Gemini, Perplexity

839 users

Cloud providers

Bedrock, Vertex, Azure OpenAI

llama-3.3-70b, claude-sonnet-4, gpt-4o

API gateways

LiteLLM, Portkey, Cloudflare AI Gateway

4,512 calls / hr

Subscriptions

ChatGPT Ent 240, M365 Copilot 1200, Cursor 85

1,525 seats

Desktop OTel agent

Claude Code, Cursor, Codex sessions

chat.completion spans

Custom connectors

Snowflake and warehouse feeds

48,210 rows synced

One spend model

Reconciles and maps every dollar

$183,847

total spend, month to date

Token usage, seat licenses, cloud model calls and gateway traffic each measure cost differently. Larridin normalizes them into one ledger, automatically.

Human vs Agent breakdown

Example · 10,220 sessions · MTD

Session split

73%27%

Variable usage · $122,554

$33.8K$88.7K

Human Agents

avg variable cost / human

$40

variable usage only, month to date

avg variable cost / agent

$485

72% of variable spend is agent-driven

02 Visibility

Bring complete visibility across your agents and usage-based spending.

Most tools measure human output or agent output. Not both together. Larridin gives you the human and agent split in a single view so every dollar traces back to whoever spent it, and what they produced.

Human and agent spend in one view
Every agent traced to an owner

839

users tracked in real time

183

active agents monitored

27%

of sessions are agents

72%

of variable spend is agent-driven

03 Attribution

See what separates efficient spend from the expensive one.

Larridin maps every key to an owner, team and cost center, so you can attribute every AI dollar to a specific use case or outcome.

Every token tied to a team and an outcome
Cost per unit of work, not cost per seat

Spend by surface

Selected surfaces · $39,239 MTD

AI surfaceSpendValue created
Claude CodeEngineering$18,3927h / dev
Lead Prospecting AgentSales$12,8477h / SDR
ChatGPT EnterpriseMarketing$6,200+23% output
Meeting AgentOperations$1,8004 mtgs cut / wk

Drill: selected people

Sarah Jones
Production Eng
$2,184
Ravi Kumar
Sales Ops
$5,189
Jamie Park
Marketing
$2,192

Example output for the selected workflows: hours per developer or SDR per week, output lift, meetings removed.

04 Alerts

Most AI spend problems announce themselves too late.

Larridin flags projected overages, unattributed spend, agent debt, and dormant seats before they turn into renewal surprises or runaway costs.

Example first scan

18%

of spend awaiting attribution in this example

 

47

agents awaiting an owner in this example

Projected overages flagged before quarter close
Orphaned agents with no owner surfaced
Dormant seats surfaced before renewal

Budget alert

Projected overage

Projects spend per team and flags budgets at risk before the quarter closes.

Budget 90% · reporting-pipeline-v3 auto-switched to a lower-cost model

Attribution gap

Unattributed spend

Surfaces every dollar that hasn’t been mapped to a team, agent, or use case.

18% of this month’s spend has no team or use case yet

Agent risk

Agent debt

Flags active agents with no accountable owner before they run unchecked.

47 active agents have no owner on record

Subscription drift

Dormant seats

Identifies enterprise seat licenses unused in the trailing 30 days, ready to reclaim or renegotiate at renewal.

ChatGPT Ent · 38 of 240 seats idle for 30 days · renewal in 41 days

Beyond spend

Spend is just a start. Measure complete AI ROI.

Larridin already measures how broadly your teams use AI, how skillfully they use it, whether it’s making them more productive, and how the work itself is changing. Spend is the cost side of that picture.

See it next to the rest and you stop asking what AI costs and start seeing what you get for it.

AI Adoption

How broadly your teams use AI, by team and role, and what it costs.

AI Fluency

How skillfully they use it. Find the power users and close the gaps.

AI Impact

Whether it is making them more productive: capacity added, cost per AI hour, ROI.

Workflow Intelligence

How the work itself is changing, and where automation pays off next.

CFOs are demanding answers
on what AI is buying.

What are we spending on AI? Who’s spending it? Is it working? Someone will ask. Larridin means you don’t have to scramble to find out.

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