Harness Launches Tools to Track AI Spending and Results
Harness released two platforms designed to measure what organizations actually get from their AI investments. The move addresses a fundamental problem: companies are deploying AI widely but have little visibility into whether those tools improve productivity or simply add to costs.
A Harness survey found that 94 percent of engineering managers don't track cost metrics when evaluating software development tools. Finance departments receive invoices from AI vendors but struggle to connect spending to concrete business outcomes.
Tracking Development AI
AI DLC Insights monitors how developers use AI code assistants. The platform counts tokens consumed through tools like GitHub Copilot, Cursor, Claude Code, and Windsurf, then links that usage to measurable results: resolved bugs, merged pull requests, or completed features.
This lets organizations calculate the cost of AI-assisted development for specific tasks and compare it against traditional approaches. The platform also identifies wasted spending-code generated by AI that never reaches production.
Costs and output are broken down by individual developer, team, and business unit. The system flags cases where token consumption rises without corresponding increases in shipped code.
Managing Operational AI Costs
Cloud & AI Cost Management consolidates spending across multiple vendors into a single view. Organizations can track expenses from OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, and others in one place.
The platform ties costs to specific AI agents, workflows, teams, and business units. This reveals which applications deliver value and which ones run up bills without clear benefits.
Built-in budget controls let teams set spending limits and receive alerts when costs spike unexpectedly. This prevents new AI applications from quietly driving up expenses.
The Core Problem
Trevor Stuart, senior vice president at Harness, said the real issue isn't how much organizations spend on AI-it's the absence of measurable return on investment. The new products treat AI spending the same way companies manage cloud costs: by tracking results, not assumptions.
For managers overseeing AI adoption, understanding AI for Management and AI for Finance has become essential to justifying continued investment.
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