Why Real-Time AI Cost Management Has Become Urgent
AI cost management is the discipline of tracking, analyzing, and optimizing spending on AI tools and infrastructure so that every unit of cost is tied to measurable business outcomes and engineering productivity. Engineering leaders are pouring budget into AI coding agents, inference APIs, and new infrastructure, yet most still cannot explain how this investment improves delivery speed or product quality. According to Harness’s State of Engineering Excellence report, 94% of engineering leaders say the metrics that matter most are missing from their current measurement frameworks. At the same time, Gartner expects worldwide AI software spending to reach USD 2.59 trillion (approx. RM11.93 trillion) in 2026, a 47% increase over the previous year. This widening gap between AI spending visibility and financial accountability is pushing organizations to look for practical ROI tracking tools that can operate inside everyday software delivery workflows.
AI DLC Insights: Linking Token Spend to Shipped Code
Harness’s AI DLC Insights targets one of the most persistent blind spots in AI spending visibility: what happens to the tokens developers burn in coding agents. With tools such as Claude Code, Cursor, GitHub Copilot, and Windsurf now embedded in daily work, token costs accumulate with little connection to outcomes. AI DLC Insights extends Harness Software Engineering Insights with an on-machine developer agent that records every AI-generated line of code, along with token costs per model and tool. It then maps that spend through pull requests, tickets, and deployments. The result is a clear view of engineering team costs: which AI tools are adopted, how much AI-generated code ships, and where tokens are lost to abandoned branches, bloated prompts, or ill-chosen models. This gives leaders concrete AI spending visibility, instead of guesswork based on aggregate invoices or license counts.
From Coding Benchmarks to Production Outcomes
AI DLC Insights does more than tally token consumption; it connects coding behaviors to production outcomes. By tracking AI-generated code from prompt to production, the product allows teams to correlate ship rate, pull request cycle time, and DORA metrics with incident data. Engineering managers can benchmark AI-assisted teams against organization-wide baselines and explore whether AI adoption shortens review cycles or introduces more defects. Role-based access control supports governance needs, ensuring leaders can examine performance without exposing sensitive code. This data turns AI-assisted development from a black box into a measurable process: teams can see which developers and business units gain the most from AI, and which practices cause waste. For organizations under pressure to justify AI investments, this creates a direct narrative from token spend to working software, instead of vague claims about productivity boosts.
Cloud & AI Cost Management: Unit Economics for Inference
Once AI agents move into production, the cost profile shifts from developer tokens to live inference. Every customer interaction, resolved ticket, or automated workflow now triggers a model call. In most enterprises, this shows up only as a growing invoice line, with no context about which agents or use cases are driving spend. Cloud & AI Cost Management extends the existing Harness Cloud Cost Management product to cover every dollar of AI infrastructure, connecting directly to AI providers and production agents. It captures spending at the individual request level and ties it to specific agents, sessions, or workflows. This lets teams compute unit economics, such as cost per resolved ticket or per automated task, and compare those figures with operational benefits. Instead of simply knowing that AI infrastructure spend is rising, leaders can see whether that growth reflects real value.
Embedding AI ROI Tracking in Software Delivery Workflows
Both AI DLC Insights and Cloud & AI Cost Management are designed to sit inside existing software delivery workflows, not on the side as extra reporting tools. By plugging into developer environments, code repositories, ticketing systems, and production agents, they give continuous, real-time insight without adding manual reporting overhead. Trevor Stuart, SVP and GM at Harness, describes the situation bluntly: “Every enterprise we talk to is asking the same question: we’re spending more on AI than ever, so why can’t we show what it’s doing for us?” With real-time AI spending visibility available at both development and production stages, engineering leadership can fine-tune budgets, refine governance policies, and stop AI projects that no longer pay for themselves. The promise is simple: bring financial and operational clarity to AI, so teams can focus on building features that deliver measurable value.