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Rippling launches AI Spend Console after burning millions on AI tokens

Rippling unveiled AI Spend Console to help companies track and contain AI spending, after its own AI token costs ballooned to 40% of R&D headcount budget. The tool maps employee usage and routes prompts to cost-effective models.

AI News Feed · 2026-08-14 21:58

The product was born after Rippling itself adopted an aggressive AI spending strategy at the start of the year. In March, Chief Financial Officer Adam Swiecicki presented figures showing that the company was on track to burn 40% of its R&D headcount budget on AI tokens, equal to millions of dollars. Spending was growing 80% month over month, and if that trend continued, the next year it would spend almost as much on AI tokens as on its R&D employees. Chief Product Officer Matt MacInnis told TechCrunch, "We were incredulous."

Rippling management launched an urgent project to understand the spending and what it was getting in return. Its analysis found that roughly 10–15% of employees drove about 60% of total AI spend, and one engineer was spending $50,000 a month. The company did not want to stop AI usage but to rein it in. It negotiated maximum spending caps with each AI tool provider, including Cursor, OpenAI and Anthropic, and discovered that employees defaulted to the most expensive frontier models for all tasks. MacInnis said that inference providers such as Anthropic and OpenAI have no incentive to help companies control spending and instead have every incentive for it to be a runaway expense. He added that they do not provide usage insights nor collaborate with one another.

Rippling also built an AI gateway that routes prompts to the most cost-effective model for each task. Founder and CEO Parker Conrad noted that in internal benchmarking, SpaceX's Grok was the overall leader but that "GLM 5.2 is 85% cheaper but [had] nearly identical performance" to frontier models. Z.ai's GLM 5.2 has become a popular Chinese model for coding tasks among tech companies.

AI Spend Console produces dashboards that score attributes such as prompts per day combined with work output and spend. With the tool in place, Rippling said it dropped its token spending from 40% of its headcount budget to about 15%. MacInnis said that internal usage hit a peak of 605 billion tokens in April, and again reached 600 billion tokens in July, yet the cost of July's token spend was 37% of April's. He attributed this to routing to more effective models and joked, "We're not letting the sales team do grammar updates using Fable."

Rippling also appointed "AI captains," employees who use AI effectively and help the rest of the company. MacInnis noted that such efforts beyond engineering are a work in progress. The company is, for example, working on automating some mailing data and data-reconciliation tasks for customer onboarding teams, and the dashboard will measure productivity in terms of onboarding volume.