The US Court of Appeals for the Third Circuit has allowed a putative class action against Cendyn’s Rainmaker hotel revenue-management software to proceed past the motion-to-dismiss stage. The court’s ruling was based on plaintiffs’ specific allegations that competing casino-hotels supplied current, non-public pricing and occupancy data to a common vendor, received pricing recommendations allegedly generated using that data, and followed those recommendations at a high rate. The decision does not hold that Cendyn’s algorithmic pricing tools are unlawful, nor does it resolve how the software worked or whether any defendant violated the antitrust laws. Cornish-Adebiyi v. Caesars Entertainment, Inc., Case No. 24-3006 (3d Cir. July 29, 2026).

The Third Circuit’s opinion offers several practical takeaways:

• Using a common pricing, revenue-management, or analytics tool is not automatically unlawful. The risk increases where plaintiffs can plausibly allege that a common vendor functioned as a shared pricing agent, leveraged current non-public data from competing users, and generated recommendations that users allegedly followed rather than independently evaluated.
• Users’ preservation of final pricing authority may not defeat a Section 1 claim if plaintiffs plausibly allege high adherence to algorithmic recommendations and practical constraints that discourage deviations.
• Staggered adoption may not defeat parallel-conduct allegations if plaintiffs also allege continuous use and synchronous movements in price and output during the class period.
• Courts may allow algorithmic-pricing complaints to proceed past the pleadings stage even where plaintiffs cannot yet plead the proprietary software’s precise mechanics.
• Users of these pricing tools should map the tools they are relying on, understand data flows, preserve independent pricing judgment, document governance controls, and consult antitrust counsel before adoption, renewal, or material configuration changes. 

What the plaintiffs alleged

Plaintiffs alleged a hub-and-spoke conspiracy, with Atlantic City casino-hotels as the spokes and Cendyn’s Rainmaker software as the hub. According to the complaint, defendants supplied current, non-public per-room pricing and occupancy data that Rainmaker allegedly analyzed alongside competing casino-hotels’ data to generate rates multiple times per day. Those recommendations were allegedly integrated into hotel property-management systems and accepted approximately 90% of the time.

Why the Third Circuit reversed dismissal

In September 2024, the US District Court for the District of New Jersey dismissed the complaint, finding plaintiffs had not adequately pleaded the horizontal “rim” necessary for a hub-and-spoke conspiracy.¹ The Third Circuit disagreed, holding that plaintiffs plausibly alleged an agreement to fix room rates through Cendyn’s algorithm and to avoid competing on price. Four aspects of the court’s analysis stand out:

• Parallel conduct. The Court found parallel conduct based on plaintiffs’ allegations of “continuous deployment” of the software during the class period and “subsequent, sudden synchronous movement of prices and output” after adoption..

  Defendants emphasized that they adopted the tool over a 14-year period. The court rejected that argument, reasoning that an adaptive pricing tool could create the “opportune time and capability for collusion” any time after adoption.

• Plus factors. Plaintiffs alleged a common motive to conspire following financial hardship, conduct against economic self-interest in the casino-hotel context, exchanges of non-public information through the software, overlapping industry contacts, and a sudden shift from historical independent pricing practices.  

  The court was influenced by allegations that defendants failed to undercut one another even as occupancy fell. It credited allegations that a former pricing-tool executive warned against a “race to the bottom” and reasoned that maintaining higher prices in those conditions required confidence that rivals would not cut rates.

• Data exchange theory. The court held that plaintiffs plausibly alleged a de facto exchange of commercially sensitive information through Rainmaker. While information exchanges are not per se illegal, they can support an inference of price fixing depending on market structure and the nature of the information exchanged.
• Adherence and hurdles to deviation. The court emphasized allegations that defendants followed recommendations 90% of the time, and that deviations required special override permissions. In the court’s view, prices can be fixed even if conspirators do not always adhere to the recommendations.

What the decision does not hold

The opinion is a pleading-stage ruling. The court did not determine how Cendyn’s software works or whether defendants violated the law. It also declined to hold that all uses of a common algorithmic tool create exposure to antitrust liability, noting that “software programs used separately and independently to help businesses compete against one another” would not state an antitrust claim.

The decision turned on a combination of specific allegations: centralized use of current non-public competitor data, a shared pricing agent, alleged confidence that competitors would not undercut recommended rates, and market outcomes the court viewed as consistent with coordinated price elevation rather than independent competition.

The decision is likely to become an important pleading-stage reference point in antitrust cases brought based on algorithmic-pricing tools, particularly where plaintiffs allege common vendor use, current non-public competitor data, high adherence to recommendations, and constrained user discretion. At the same time, the opinion underscores that the degree of antitrust risk depends on the specific tool design, data flows, contractual controls, market context, and evidence of the exercise of independent pricing judgment.

• Map pricing tools and vendors. Identify all pricing, revenue-management, yield-management and forecasting tools used by a given business, including any product that generates recommended prices, discounts, capacity decisions, or output-related guidance. 
• Understand data flows. Confirm whether the tool receives current or recent non-public data, whether it uses data from competitors or other users, and whether outputs are based on pooled, commingled, benchmarked or otherwise shared competitively sensitive information.
• Preserve independence. Ensure the business retains meaningful independent pricing judgment, avoids default or automatic implementation without review, and keeps override processes practical, available, and well documented. 
• Review vendor contracts and controls. Consider contractual restrictions on using the company’s non-public data to generate recommendations for competitors, audit rights, data segregation commitments, model-governance obligations and clear limits on vendor facilitation of competitor coordination. 
• Train commercial teams. Pricing, revenue-management, sales and analytics personnel should understand that using AI or third-party software does not eliminate antitrust risk. Training should focus on preserving independent pricing decisions, avoiding competitor communications about pricing tools or adherence, and escalating questions about non-public competitor data or vendor-facilitated benchmarking.
• Document legitimate business rationales. Before adopting a tool, record the procompetitive reasons for its use, such as improved forecast accuracy, inventory management, transaction-cost reduction or responsiveness to demand, and pair that record with antitrust advice from sophisticated counsel and data-governance review

1 Cornish-Adebiyi v. Caesars Entertainment, Inc., 2024 WL 4356188 (D.N.J. Sept. 30, 2024).

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