Rippling and Runlayer Drop Dueling AI Lawsuits
Runlayer and Rippling have dropped their mutual lawsuits without a settlement, clearing the way for Rippling to launch a rival AI gateway and highlighting the risks of enterprise testing.
On Wednesday night, early-stage startup Runlayer and HR tech giant Rippling dismissed their respective lawsuits against each other. The resolution involved no financial settlement, exchange of funds, or coverage of legal fees. Immediately following the dismissal, Rippling launched its Model Context Protocol (MCP) gateway, the very product that sparked the legal battle.
The conflict began after Rippling spent more than a year testing Runlayer's secure AI gateway. Runlayer, which launched out of stealth in November 2025 and has raised $42 million from investors like Felicis and Khosla Ventures' Keith Rabois, is led by Andrew Berman. Berman is a third-time founder who previously started baby-monitor company Nanit and AI video tool Vowel, which sold to Zapier in 2024. According to Runlayer's initial lawsuit, Rippling chose not to purchase the software but instead built its own version, which a Rippling employee allegedly described in a text message as a clone. Runlayer sued for contract violations, prompting Rippling to countersue over patent infringement. Both companies dropped their claims after three weeks of discovery.
An MCP gateway secures enterprise AI agent requests by managing how they retrieve data from internal software systems, applying role-based access controls and logging usage. With its new release, Rippling—historically known for payroll and benefits administration—is entering the AI gateway space to compete with Stripe, Ramp, and Databricks. It also enters the AI security market against Docker, Amazon Bedrock, and Runlayer itself. Meanwhile, Runlayer is focusing on a broader suite of services, including agent creation and detecting unauthorized shadow AI agents.
For software founders and developers, this dispute serves as a stark warning about the dangers of extended enterprise pilot programs. In an era where AI software development has become highly accelerated, long-term technical evaluations can give prospective clients enough time and insight to build competing tools internally. Practitioners must carefully structure evaluation agreements to protect their intellectual property and limit the duration of product trials.
This is our own summary of reporting by TechCrunch AI
