Use Case
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FinOps for AI
FINOPS FOR AI
Know what AI costs. Know what it’s worth.
Get visibility into AI spend across models, agents and software workflows. Attribute costs, control budgets and understand the economics of AI at scale.
Trusted by teams operating AI in production
THE AI COST CHALLENGE
AI spend is growing. Accountability isn’t keeping up.
Model requests, autonomous agents and multi-step workflows create costs across teams and systems. Without clear attribution, it’s difficult to know what’s driving spend or delivering value.
COST VISIBILITY
From individual requests to entire workflows.
Understand where AI resources are spent, whether they're powering applications or delivering software.
COST MANAGEMENT
Visibility is the start. Control is the advantage.
Attribute every request
Connect spend to the team, project, agent or customer behind each request.
Set budgets and limits
Define budgets and spending limits so usage stays within your policies.
Optimize model routing
Route requests to suitable models and reduce repeated work with caching.
Connect cost to value
Compare resource use with delivered outcomes to understand AI economics.
What is FinOps for AI?
Orq.ai helps measure ROI by connecting Software Factory costs to delivery outcomes. Track cost per completed change, engineering hours saved, backlog throughput and delivery speed to understand whether autonomous workflows deliver measurable business value.
How is AI spend tracked across models and agents?
Can I attribute costs to Software Factory workflows?
How can Orq.ai help reduce AI costs?
Can I set budgets and spending limits?
Yes. Set budgets and spending limits across teams, projects, models and agents. Use your policies to decide when to alert, reroute or block requests.