Know what your
AI spend is buying.
XTrace ties every AI dollar to the work it does and the results it produces, so you can show what’s paying off, cut what isn’t and keep spend predictable as adoption grows.
AI spend, year to date
$1.24M
+27% Q3 vs. Q2
Budget vs. actual, FY26
99%of plan to date
Full-year forecast
$1.84M
Base forecast, 2% over budget
Spend tied to KPIs
81%
14 workflows mapped
Monthly spend and forecast
- Base$1.84M+2% vs. budget
- With optimizations$1.73M−4% vs. budget
With optimizations: spend if XTrace's suggested changes, such as a model or skill change, are applied.
Cost per outcome
Six-month changeTeams with steady AI use
17of 23
+9 teams since rollout
Stalled sessions
11%
From 24% before standards
Standards adopted
12of 15
3 rolling out
Work done with AI, before and after
Practice adoption
Teams using each standard- Plan before generating code21 / 23
- Shared project context18 / 23
- Tests on agent output15 / 23
- Right-sized model choice12 / 23
- Reuse of team knowledge10 / 23
Spend & ROI. Spend against budget, a forecast with and without XTrace’s suggested optimizations, and cost per outcome for each workflow.
The problem
AI spend is growing faster than anyone can explain it.
The bill comes before the answer.
AI is bought by many teams, inside many tools, on usage-based pricing. By the time spend is reconciled, it’s already over budget.
Usage isn’t value.
Dashboards show who used how much. They don’t show whether the work shipped, or what it was worth to the business.
Waste looks like growth.
Failed attempts, retries and repeated errors get billed like real work. On a spend report, they look like adoption.
XTrace research
Why AI budgets overrun, and how leading companies prove what they’re getting.
Industry data and our own analysis, with what each means for your budget.
Read the research
Spend & ROI
From AI bill to business case, on one screen.
Portfolio spend, forecasts and return in one place, measured against the KPIs you already report.
Portfolio spend
All AI spend by business unit, team, tool, model and workflow. No more stitching invoices together.
Cost per outcome
What each ticket closed, claim processed or release shipped costs in AI, tied to the workflow KPIs you choose.
Budget vs. actual
Spend against plan by team and workflow, with variances flagged before month end.
Spend forecast
Total spend forecast from real usage, next to what it would be with XTrace’s suggested optimizations, such as a model or skill change.
Budgets, policies & recommendations
Where your FinOps team runs AI spend day to day.
Allocation, budgets, policies, alerts and recommendations in one place. Recommendations change nothing until someone on your team approves them.
Sample XTrace screen. Four recommendations from AI gateway analysis wait for a person to approve them. Three carry a projected saving, about $15.4k a month together: route test generation to a cheaper model tier, cache repeated repository context in code review, use the light tier for ticket summaries, and set a monthly cap on nightly evaluation runs. Nothing changes until someone approves. Side panels show a spend anomaly (Support triage at 3.4 times typical hourly spend, owner notified) and an allocation rule that assigns the payments repositories to the Payments team, with 97.2% of AI spend allocated. Team-level data.
Recommendations
Suggested by AI gateway analysis of the last 30 days.
No change until someone approves.
-
Routing· 6 teams
Route test generation to the Standard tier
Quality parity 99.1% on 2,140 sampled tasks
$7.4k/mo projectedApprovedQueued for rollout Dismissed -
Caching· 9 teams
Cache repeated repository context in code review
38% of prompts repeat the same context within a day
$4.9k/mo projectedApprovedQueued for rollout Dismissed -
Model choice· 4 teams
Use the Light tier for ticket summaries
Quality parity 98.4% on 1,310 sampled tasks
$3.1k/mo projectedApprovedQueued for rollout Dismissed -
Budget· 3 teams
Set a $6k monthly cap on nightly evaluation runs
Averaged $8.2k a month last quarter, with no cap set
No estimateApprovedQueued for rollout Dismissed
Support triage · Customer Support
Tagging and allocation
Rules that assign every AI dollar to a team, product or workflow, including shared and untagged spend.
Budgets
Set by team or workflow, with owners and thresholds.
Policies
Approved models and tools for each team, enforced in the session by the XTrace plugin once you set them.
Anomaly alerts
Spikes and runaway usage detected automatically as they happen, and routed to the owner.
Optimization recommendations
Cheaper models that meet the quality bar, trimmed inputs and retries to cut, with a projected saving where one can be estimated. A person approves or dismisses each one.
XTrace plugin & AI gateway
Stop paying for the same mistake twice.
When an AI agent hits a broken tool, it rarely fails. It retries and works around the problem, and the job still gets done. The cost shows up as normal usage. Across a large company, it adds up fast.
Sessions by team · 14 days
- XTrace spots a mistakeOne team's agent keeps retrying the same failing step, and other teams start to hit it too.
- It fixes it automaticallyThe fix becomes a team rule on its own. Teams that want a look first can review it.
- Every team's plugin enforces itThe XTrace plugin stops the mistake inside each session. Then XTrace looks for the next one.
One team’s repeated mistake, flagged and fixed automatically, then stopped by the XTrace plugin in every team’s sessions. Team-level only.
Enforced by the plugin
Fixed once, inside every team’s AI tools.
XTrace automatically spots repeated errors and retry loops in live work, with what each one costs, and fixes them on its own. The XTrace plugin then blocks that mistake inside the AI tools and coding agents your teams already use. Teams that want to review a fix first can.
Recommended by the AI gateway
Caps, routing and caching, approved by people.
The AI gateway sits in the request path and analyzes real traffic. It recommends spending caps, routing to the most cost-effective model that meets the quality bar, and caching of repeated requests. Each one runs only after someone approves it in XTrace.
Continuous, not one-off
An audit cuts waste once. XTrace keeps looking.
A point-in-time audit addresses today’s issues, but new ones emerge as models and tools change. XTrace performs detection and remediation continuously and automatically, with no recurring manual effort. In some cases, a saving traces back to a specific fix in your spend and ROI reporting.
Getting started
Connect, configure, optimize.
Connect
We connect your scattered AI spend.
We link your AI providers, tools and billing sources and reconcile their spend and usage into one source of truth. No exports or spreadsheets for your team to maintain.
Configure
Set it to your KPIs.
Choose the workflows and KPIs that define value for you, and set allocation rules and budgets, so the numbers match how your business already measures itself.
Optimize
Lower cost without slowing teams down.
Approve the routing and caching recommendations you agree with, while XTrace fixes repeated mistakes on its own. No blunt per-person usage caps that stall the work.
Want to move faster? Add hands-on workshops and expert support from our team.
Workshops & expert help
Bring us one workflow. Leave with its real cost.
Pick one workflow that matters. We’ll pull together the AI spend behind it and show you where it goes, what it produces and where it leaks. The spend report is free, and more comprehensive reviews are available.
Prefer to read first?
Read the research