Turn AI spend into results you can report.
XTrace shows where AI spend goes, what it returns and how teams actually use it. Then it helps you cut waste, raise adoption and put guardrails in place, with one platform configured to your KPIs.
Spend & ROI. Spend against budget, and a forecast with and without XTrace’s suggested optimizations.
Trusted by teams at
Solutions
Three ways to get more out of AI.
For CFO & CIO teams
FinOps for AI
AI spend is growing faster than anyone can explain it.
Tie AI spend to the work it pays for and the outcomes it drives, so finance can budget, forecast and defend it.
Prove AI ROI
For AI enablement & HR leaders
AI Enablement & Transformation
The tools are rolled out. Adoption is uneven.
See which teams get value from AI and which are stuck, and spread what your best people already do. Hands-on workshops can help teams get there faster.
Raise adoption
For CTO & engineering leaders
Governance & Optimization
Every team uses AI differently, and margins show it.
Set which models and tools each team can use, act on recommendations that cut waste, and see which teams and workflows need attention.
Control AI use
The platform
One platform, configured to the KPIs you already report.
We start from your KPIs, such as cost per ticket, cycle time or margin per workflow, and connect AI usage and spend to them. Each person who touches AI sees the part built for their job.
For CFO, CIO & CTO offices
Spend, ROI & adoption
See what AI costs and what it returns, without asking anyone to pull a report. Spend, ROI and adoption progress, clear at a glance.
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
For FinOps, platform & AI enablement teams
Policies & recommendations
One place to allocate spend, set guardrails and act on gateway recommendations, which wait for a person and change nothing on their own.
Sample XTrace screen. Four recommendations from 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 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
For everyone using AI tools
The XTrace plugin
Works inside the AI tools and coding agents people already use. It suggests a better-fit model in the moment, and blocks known mistakes and policy breaks before they cost anything.
Suggests
A better-fit model, in the moment.
Blocks
Known mistakes, before they repeat.
Enforces
Policies and team rules, inside each tool.
Run the integration tests and fix whatever fails.
Running the integration suite.
pnpm test:integrationFailedConnection refused. Retrying the suite.
pnpm test:integrationHeldThis failure has been solved before.
The local test database isn’t running, so retrying won’t help. Start it first, then rerun.
make db-upLearned from your team’s earlier sessionsStarting the test database, then rerunning.
make db-up && pnpm test:integrationDoneRuns in the background
AI gateway
Sits in the request path for agents and apps, and runs the caps, routing and caching your team has approved on every call. It analyzes that traffic and recommends changes; a person decides. It has no interface of its own; you see its effects in the other three.
AI gateway
Request path. Every model call from AI tools, agents and apps passes through the gateway.
Recommends. The gateway analyzes traffic and surfaces routing, caching and budget changes. A person approves them in XTrace.
Enforces. Rules and policies go to the XTrace plugin, which applies them automatically inside each tool.
Why it keeps paying off
A one-time audit cuts waste once. XTrace keeps it cut.
A point-in-time audit identifies the inefficiencies in your AI usage today, and remediating them is a sound first step. But new failure patterns emerge as models, tools and workflows evolve, so lasting results depend on continuous assessment and correction. XTrace provides exactly that: it detects new issues in live work, resolves them automatically, and the XTrace plugin enforces each fix across every team’s AI tools, with no recurring manual optimization.
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 mistake, fixed for good. A repeated failure in one team is fixed automatically, the XTrace plugin stops it in every team, and XTrace moves on to the next one.
Workshops & expert help
Add-on
Move faster with expert help.
Our team runs hands-on workshops and expert support alongside the platform, to speed up a rollout, launch a new standard or build the internal case for AI.
Executive briefing
What leading enterprises are doing with AI, mapped to your KPIs.
Team workshops
Hands-on sessions built around your teams’ real workflows.
Expert help
Specialists on call for setup, configuration or a specific rollout.
See workshops & expert help
Research
Built on what leading enterprises are learning.
From the XTrace research team, drawing on published industry work. Short reads for when you need to make the case internally.
AI spend & ROI
Most teams can see what AI costs. Few can show what it returns.
Read the research
AI fluency & adoption
Why rolling out AI tools doesn’t raise adoption on its own, and what does.
Read the research
AI governance
How leading teams control AI use without slowing their people down.
Read the research
Security & data handling
Built to pass your security review.
Your data stays yours
What we store, where, and for how long is set out in our terms of service. Deployment is flexible, to fit your requirements.
Protected in transit and at rest
Data is encrypted with TLS in transit and encrypted at rest.
Access you control
Model and tool access is set centrally, per team, and people sign in with single sign-on.
Open source you can inspect
Review the open-source app’s code yourself. Our local session and usage tracking app is open source; the platform is not.
Privacy
Insight and coaching are team-level by default. XTrace is privacy-first and configurable to your policies.
Ask us about security
Tell us the numbers you report on.
We’ll show you where AI is helping them, where it’s costing you, and what to do next.