Sphere wins 2026 Global Recognition Award
Sphere Partners
Enterprise AI Governance + FinOps
Shadow AIUncontrolled spendModel sprawlAudit gapsNo ROI visibility

Bring AI spend, risk, and usage under control.

Sphere helps enterprises see where AI is being used, what it costs, who owns it, and where governance controls are missing without slowing down the teams already creating value.

Choose a starting pointEstimate savings
AI Governance Command Center
Live controls

Monthly AI spend

$50K
Current estimate before token, model, and workflow optimization.
GPT
76%
Claude
48%
Gemini
28%

Risk queue

Unapproved AI toolHigh
No data ownerReview
Budget thresholdWatch
Approved model routeClear
100%Spend and usage visibility target
2-3 wkFull assessment timeline
5 levelsGovernance maturity model
40-60%Optimization potential range
48-hour snapshotFast initial cost and governance review
2-3 weeksFull governance and cost assessment
Workflow-level costAttribute spend by team, product, and use case
Audit-ready controlsPolicies, approvals, monitoring, and reporting
Where risk and cost appear

AI does not stay experimental for long.

Once teams start using copilots, agents, model APIs, and AI-enabled software, the organization needs a way to see usage, control cost, approve tools, and prove that policies are being followed.

01

AI tools spread faster than policy

Teams buy and use tools before there is a shared standard for approval, data handling, or vendor review.

See the control story
02

Spend is hard to attribute

AI costs show up across model providers, copilots, APIs, infrastructure, and team budgets with no clean owner.

Estimate savings
03

Compliance proof is missing

Leadership may know AI is being used, but not whether there is an audit trail for decisions, approvals, and data access.

View framework
04

ROI is unclear

Without outcome tracking, it is difficult to separate useful AI from expensive activity that does not improve the business.

Choose next step
The governance story

From shadow AI to accountable AI.

The goal is not to block AI usage. The goal is to create enough visibility, ownership, and control that teams can scale AI safely.

Before

Personal AI accountsUnknown
Duplicate subscriptionsWaste
Unapproved vendorsRisk
No token attributionBlind
No audit trailGap
→

After

Approved AI inventoryVisible
Budget controls by teamOwned
Vendor and model governanceControlled
Cost per workflowMeasured
Executive reportingBoard-ready

Governed AI gives finance, security, compliance, and business leaders the same operating picture: what is running, what it costs, who owns it, and whether it is delivering measurable value.

What Sphere builds

A practical operating model for AI governance and AI FinOps.

Sphere connects cost intelligence, governance, ROI reporting, and observability so AI can scale as an accountable business capability.

Cost intelligence

See usage and cost by model, team, product, workflow, and business unit.

  • Token consumption tracking
  • Chargeback and allocation models
  • Cost per workflow and interaction
Clarifies where AI money is going.

Governance controls

Define how tools, models, vendors, data, and AI-assisted decisions are approved and monitored.

  • Policy and approval workflows
  • Vendor and model risk controls
  • Audit readiness and oversight
Reduces avoidable exposure.

ROI reporting

Connect AI spend to adoption, productivity, savings, revenue impact, and business outcomes.

  • Outcome-based dashboards
  • Executive reporting
  • Initiative-level ROI visibility
Separates activity from value.

AI observability

Monitor model behavior, performance, drift, cost anomalies, and agent workflows after launch.

  • Model and agent monitoring
  • Budget and anomaly alerts
  • Operational health reporting
Keeps controls alive after deployment.
Self-assess quickly

Where does your AI governance program sit today?

Use the maturity model as a simple way to frame the conversation. Most organizations do not need perfection first. They need visibility, ownership, and the next practical control.

01

Experimenting

AI usage exists, but it is not yet visible or controlled.

Risk profile

Shadow AI, personal accounts, unclear data exposure, and no central inventory.

Next control

Build an AI usage inventory and identify the highest-risk tools, vendors, and workflows.

AI cost calculator

Estimate the size of the optimization opportunity.

This is intentionally directional. The full assessment replaces estimates with actual usage data, model costs, workflow ownership, and governance findings.

$28,500
Estimated monthly optimization opportunity
Annualized opportunity$342,000
Estimated reduction range57%
Cost per user/month$1,000
Governance risk levelVery High
Get a fuller savings report

This calculator uses a directional estimate. A full assessment should be based on actual usage data, vendors, models, workflows, and governance maturity.

Choose your starting point

Start with the level of help your AI program needs now.

Make the path easy for buyers: a fast snapshot, a full assessment, or a program to implement and operate governance at scale.

Fastest

48-hour AI cost and governance snapshot

A quick review to identify obvious cost leaks, shadow AI exposure, and immediate governance gaps.

48 hoursInitial findings
Request snapshot
Most useful

AI Governance and Cost Assessment

A structured assessment of usage, spend, risk, ownership, controls, and savings opportunities.

2-3 weeksRoadmap included
Start assessment
Operationalize

AI FinOps implementation and managed governance

Deploy controls, dashboards, chargeback models, monitoring, and ongoing executive reporting.

6-12 weeksManaged program
Plan implementation
Questions before you start

AI governance and FinOps, explained plainly.

AI governance is the operating model for approving, monitoring, and controlling AI tools, models, vendors, data usage, and AI-assisted decisions. It helps teams scale AI while maintaining security, accountability, and compliance.

AI FinOps manages AI consumption, cost attribution, and business value across model APIs, agents, copilots, infrastructure, and AI-enabled applications. It focuses on visibility, optimization, and ROI.

Traditional FinOps tracks infrastructure metrics like compute, storage, and utilization. AI introduces cost units such as tokens, prompts, inferences, model routes, and agent interactions that need different controls.

The assessment produces a view of AI usage, governance maturity, risk gaps, cost attribution, savings opportunities, and a prioritized roadmap for controls, monitoring, and executive reporting.

The quick snapshot can be framed as a 48-hour review. A fuller AI governance and cost assessment typically runs 2-3 weeks depending on the systems, vendors, and teams involved.

We'd love to hear from you!

Please provide your contact details, and our team will get back to you promptly.

Know what AI is costing, where it is being used, and how to govern it.

Start with a fast snapshot, a full assessment, or a program that turns AI governance and AI FinOps into an operating discipline.

Request AI Cost and Governance SnapshotTalk to an AI Governance Expert