Nearly all engineering leaders surveyed – 94% – for the State of Engineering Excellence report say they are not finding cost metrics in their current measurement frameworks, making it difficult to ascertain if each dollar of their AI spend is producing a real outcome.
According to Harness, there are two issues driving spending for AI: developers are using it to generate almost all their new code, and agents in the infrastructure consume massive amounts of tokens on ticket resolution, customer interaction and automated workflows, without any real idea of how much value is being gained from that spend.
“There’s a lot of chatter right now around rising AI costs. But the real challenge isn’t the spend itself—it’s that teams can’t pin down the impact of the spend,” Trevor Stuart, senior VP at Harness, told SD Times in a statement.
So Harness is releasing two new products that help organizations get a handle of how much is being spent on AI, and whether or not companies are seeing value from that. The first is AL DLC Insights, and the second is Cloud & AI Cost Management.
AI DLC Insights
According to the company, AI DLC Insights lets organizations see where spending on tokens results in shipped work, and where it doesn’t. An agent that runs in the developer’s IDE captures each line of code, records the token cost and maps it back to the pull request or ticket or shipped work. WIth that, companies can see, for example, that it costs X amount in AI credits to fix a bug, and they can determine if that is more cost-effective than having humans do that work.
“Companies are paying to write code that never reaches end users. That’s the problem AI DLC Insights solves,” Stuart explained. “It’s an agent deployed on developer machines that captures token spend at the source, surfaces efficiency gaps, and delivers informed recommendations. You get a clear view of ROI, efficiency, and the true cost per feature or per bug resolved.”
The following capabilities are included with this release of AI DLC Insights, Harness announced:
- Unified AI coding adoption visibility — One place to track adoption, sessions, and AI-generated code across every coding agent — Claude Code, Cursor, GitHub Copilot, Windsurf. Which tools your developers actually use, not just which seats you bought.
- Per-developer attribution — Token spend, sessions, and shipped code traced to the developer, agent, repository, team, and business unit behind them, turning bulk AI invoices into per-developer ROI you can act on.
- Wasted spend detection — Tokens burned on abandoned code, bloated prompts, expensive model choices, and missed cache hits surfaced automatically. The first time a team doubles its token bill without shipping more code, you know before the next renewal.
- Coding-to-production impact — Track AI-generated code from prompt to production using ship rate, PR cycle time, and DORA metrics, correlated with incident and vulnerability data. Know whether coding agents are actually making your team faster.
- Benchmarking and governance — Adoption, efficiency, and impact metrics compared across teams against an org baseline, with role-based access control and Harness-native engineering governance included.
Cloud & AI Cost Management
The second feature, Cloud & AI Cost Management, picks up where development cost visibility ends. In its announcement of the new products, Harness wrote: “A $28,000 monthly spend on a customer support agent is a completely different number depending on how many tickets it resolved. If it cost $0.60 per resolved ticket and the human alternative costs more, it is one of the best investments in your stack. If the math runs the other way, you are paying more for automation than the process it replaced. Most organizations cannot tell the difference today.”
In its announcement, the following capabilities are included with this release of Cloud & AI Cost Management:
- Unified AI cost visibility — One place to see spend across every AI provider and managed service, from OpenAI and Anthropic to AWS Bedrock and GCP Vertex AI. One source of truth for every dollar of AI spend, no matter where it originates.
- Full spend attribution — Cost traced down to the agent, session, workflow, team, and business unit driving it, turning invoice totals into agent ROI you can act on.
- Anomaly detection — Spend spikes flagged before they hit the invoice, using the same detection engine already watching your cloud costs. The first time a release doubles your token bill, you know before finance does.
- Budget and governance — Budgets set at the agent, team, or business unit level, with approved-model policies and Cost Categories that extend the same FinOps controls you already trust for cloud spend to AI.
“A lot of what we build at Harness starts with our own internal pain,” Stuart said. “With both of these products, we were hearing the same thing from customers and watching the industry grapple with rising AI costs—but we also needed to solve it for ourselves.”
