AI DLC Insights and Cloud & AI Cost Management give engineering leaders answers to their biggest AI questions: How much are they spending on AI and where is the ROI?
Harness, an AI Software Delivery Platform™ company, today announced two new products: AI DLC Insights and Cloud & AI Cost Management. Together, these will give engineering organizations real-time visibility into how much money is being spent on AI and whether that investment is producing measurable results.
According to Gartner, global AI software spending is expected to reach $2.59 trillion in 2026, a 47% increase over last year’s spending. However, according to Harness’ 2026 State of Engineering Excellence report, 94% of engineering leaders say their most important metrics are missing from their current measurement frameworks. As spending continues to rise, the link between spending and business outcomes remains largely unmeasured.
“Every company we talk to is asking the same question: We’re spending more than ever on AI, so why can’t we show what it’s doing for us? The first phase of AI adoption was getting teams to use and understand the tools. The next phase is proving that the tools have a positive impact,” said Trevor Stuart, SVP and GM at Harness. “At Harness, we created these products because that is exactly where our customers are today, and demonstrating ROI will be the defining challenge for enterprise AI in 2026.”
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AI DLC Insights: Connect developer token spend to shipped software
Today, developers are writing nearly every line of new code with AI assistance. The tools may vary, such as Claude Code, Cursor, GitHub Copilot, Windsurf, etc., but the patterns are universal. The problem is that token spending is not tied to results. That is, what portion of AI-generated code actually ships, how much money is wasted on abandoned code and bloated prompts, and whether AI-assisted work is actually moving quickly through review and into production.
AI DLC Insights answers these questions. This product extends Harness Software Engineering Insights with a new on-machine developer agent that runs directly in the developer’s environment. The agent captures every line of code generated by the AI, records token costs for each model and tool, and maps spend throughout the delivery chain to pull requests, tickets, and generated deployments.
The result is a complete picture of developer AI ROI. That means you know what tools your team is actually using, where tokens of unshipped code are placed, and whether AI-assisted work is producing faster and better software. The main features are:
- Visibility into integrated AI coding deployments: Get a single view of hiring across all major coding agencies.
- Attribution by developer: Token spend, sessions, and shipped code tracked to developers, teams, and business units.
- Detect wasteful spending: Abandoned code, bloated prompts, expensive model choices, and missed cache hits surface.
- Impact from coding to production: Track AI-generated code from prompt to production using DORA metrics associated with shipping rates, PR cycle times, and incident data.
- Benchmarking and governance: Compare your team’s performance to an organization-wide baseline with role-based access control.
Cloud and AI cost management: dollar-for-dollar unit economics of AI infrastructure
Once the AI agent is shipped to production, a different cost formula takes over. Every customer interaction, solved ticket, and automated workflow triggers inference. Most organizations only see their spending at the invoice level. That is, you can see which items are increasing, but you have no idea whether the increase is worth it.
Cloud and AI cost management can help you make that decision. This product extends Harness Cloud Cost Management to cover all spend for your AI infrastructure, connecting directly to AI providers and production agents to capture spend at the individual request level and associate it with the agent, session, or workflow that triggered it.
The main features are:
- Unified AI cost visibility: A single view of your spending across all AI and managed service providers, from OpenAI and Anthropic to AWS Bedrock and GCP Vertex AI.
- Attribution of full expenditure: The cost goes back to the agents, models, teams, and business units that drive it.
- Anomaly detection: Unusual spikes in AI spending are flagged for proactive action.
- Budget and governance: Set controls at the agent, team, or line-of-business level and extend existing FinOps controls to AI spend.
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