Rippling has this week introduced the AI Spend Console, a tool designed to help companies track and contain their AI spending. The HR software provider built the product after discovering how quickly its own AI costs were spiralling out of control.
The console maps how much individual employees, teams, and roles are spending on AI, and whether that spending translates into genuine productivity. One notable feature identifies, in the company’s words, “which engineers have high AI spend whose peers frequently ask them to redo work in code reviews.”
How the tool came about
The product emerged after Rippling committed heavily to AI at the start of the year, only to find employees were burning through cash at an alarming rate. Chief Product Officer Matt MacInnis recalled an executive meeting in March, when CFO Adam Swiecicki presented figures that stunned the leadership team.
At the time, Rippling was on track to spend 40% of its R&D headcount budget on AI tokens, meaning it was spending as much on tokens as it paid in compensation for 40% of that unit’s employees. Spending was growing by 80% month-over-month, and if the trend had continued, the following year it would have spent almost as much on AI tokens (90%) as it spent on its highly paid R&D staff.
“We were incredulous,” MacInnis said. Management launched an urgent project to understand the spending and what value it delivered. A subsequent analysis found that roughly 10 to 15% of employees were driving about 60% of total AI spend, with one engineer spending around £37,000 a month.
Reining in spending
Rather than halting AI usage, Rippling sought to control it. The company began by negotiating maximum spending caps with each of the tools it used, including Cursor, OpenAI, and Anthropic. It quickly identified an obvious problem: employees defaulted to the most recent, and most expensive, frontier models for all tasks.
“The truth is that the inference providers, like Anthropic and OpenAI, have absolutely no incentives to help you control your spend,” MacInnis said. “They have every incentive for it to be a runaway expense, and that’s exactly what they do. They don’t provide you with great usage insight, and they don’t collaborate with one another.”
Eight months into the year, enterprises have learned they need multiple models from multiple AI labs at various price points, including a frontier open-weight option, potentially of Chinese origin. Rippling founder and CEO Parker Conrad noted last month that internal benchmarks found SpaceX’s Grok to be the all-around leader, but that GLM 5.2 was 85% cheaper with nearly identical performance to the frontier models.
SpaceX now owns Cursor, which offers access to Grok and dozens of other models. Z.ai’s GLM 5.2 has become a favourite Chinese model for coding tasks among tech companies, and Databricks has also championed it.
