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After Rippling blew millions on AI in months, it built an employee ROI tool
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Rippling released AI Spend Console, a dashboard and gateway that monitors individual and team AI token usage and routes prompts to cheaper models to curb runaway costs.
Engineering teams that rely heavily on generative AI can now see how much each developer or group is spending and whether that spend translates into measurable output. The tool also forces a shift from defaulting to the most expensive models toward a cost-aware routing strategy, which can dramatically reduce token expenses without throttling usage.
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The console links AI spend to productivity metrics such as code changes, exposing high-spending engineers whose output may not justify the cost.
It includes an AI gateway that selects the most cost-effective model for each task, requiring integration with Rippling’s routing layer for full spend-control features.
Adoption reduces token spend from a large share of R&D budgets to a modest fraction, but the spend-governance functions are limited to users of Rippling’s gateway.
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Rippling moved from an uncontrolled AI token consumption phase to a managed environment by launching a product that visualizes spend at the employee, team, and role levels. The dashboards compare spending against concrete output like lines of code or pull-request activity, allowing managers to identify outliers who consume large token volumes without proportional productivity. This shift replaces the previous practice of letting engineers default to the newest, most expensive models. The core of the solution is an AI gateway that routes requests to a mix of models across providers, favoring cheaper alternatives when performance is comparable. Companies that already operate a different gateway can still use the console’s reporting, but they must adopt Rippling’s routing component to benefit from automated spend caps and model selection. Integrating the gateway may involve engineering effort to replace existing API calls and configure routing policies. Cost control is achieved by setting per-tool caps and by encouraging the use of lower-priced models such as the Chinese-origin GLM 5.2, which the internal analysis found to be substantially cheaper with similar performance. The resul
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