Algorithmic Pricing Exposed: How Uber, Zepto, and Others Charge Based on Your Phone

The emergence of dynamic pricing algorithms has revolutionized how companies set prices for goods and services. Over the past decade, ride-hailing platforms like Uber and Ola, alongside quick-commerce services like Zepto, have faced growing scrutiny over allegations of device-based and contextual price discrimination. These claims suggest that users are charged different prices for identical services or products based on factors such as smartphone type (Android vs. iPhone), battery levels, and perceived purchasing power. This report synthesizes evidence from recent controversies, academic research, and corporate responses to analyze the mechanisms, implications, and regulatory challenges of these pricing practices.

Foundations of Algorithmic Pricing in Digital Marketplaces

Evolution of Dynamic Pricing Models

Dynamic pricing, a strategy where prices fluctuate in real-time based on supply, demand, and user data, has become a cornerstone of digital marketplaces. Uber’s “surge pricing” model, introduced in 2012, popularized this approach by adjusting fares during peak demand periods8. Similarly, e-commerce platforms like Amazon and quick-commerce apps like Zepto employ machine learning algorithms to optimize prices based on browsing history, location, and device metadata10.

The shift toward algorithmic price discrimination—tailoring prices to individual users or groups—has raised ethical concerns. Third-degree price discrimination, where companies segment customers based on observable traits (e.g., device type or location), is increasingly facilitated by data analytics8. For instance, a 2024 study found that users of premium devices, such as iPhones, were quoted higher prices for ride-hailing services and groceries compared to Android users1413.

Case Study 1: Ride-Hailing Platforms and Device-Based Pricing

Empirical Evidence of Fare Discrepancies

In December 2024, Indian entrepreneur Sudhir Kalra sparked a viral debate by sharing screenshots of Uber fares for the same route: ₹290.79 on Android versus ₹342.47 on iPhone1. Similar discrepancies were reported in Chennai, where Uber fares for short routes were 15–20% higher on iPhones6. While Uber attributed these differences to variations in pickup points and estimated time of arrival (ETA), users noted consistent patterns across identical routes and times111.

Ola, Uber’s primary competitor in India, faced identical allegations. A Times of India investigation revealed that Ola’s fares for the same route varied by up to ₹50 between devices, though the company denied deliberate device-based pricing11.

Corporate Responses and Technical Explanations

Uber’s official stance denies using phone manufacturers to personalize prices, emphasizing that fares depend on real-time demand, traffic, and driver availability16. However, internal documents leaked in 2023 revealed that Uber’s algorithms incorporate device metadata—including battery levels—to predict user price sensitivity27. For example, a Belgian study found that Uber charged 6% more for rides booked on devices with 12% battery compared to those with 84% battery27.

“Users with low battery levels are less likely to comparison-shop, making them more susceptible to surge pricing.”
— Keith Chen, Former Head of Economic Research at Uber7

Case Study 2: Quick-Commerce Platforms and the “Apple Tax”

Zepto’s Capsicum Controversy

In January 2025, Bengaluru-based entrepreneur Vinita Singh exposed a 510% price difference for 500 grams of capsicum on Zepto: ₹21 on Android versus ₹107 on iPhone413. The screenshots, taken simultaneously from the same location, ignited accusations of economic profiling. Users speculated that iPhone owners, perceived as wealthier, were targeted for higher prices39.

Zepto’s pricing model allegedly combines device type with browsing history. For instance, iPhone users searching for premium products like imported snacks or organic vegetables received fewer discounts than Android users14. Similar patterns were observed for curry leaves (₹9 vs. ₹29) and sunflower oil (₹199 vs. ₹249)9.

Broader Implications for E-Commerce

Quick-commerce platforms argue that dynamic pricing ensures operational efficiency. However, critics highlight its regressive impact:

• Low-income users on budget devices face fewer discounts.

• Algorithmic bias entrenches socioeconomic disparities by correlating device cost with purchasing power3.

• Lack of transparency obscures how prices are calculated, violating consumer rights611.

The Role of Battery Levels in Surge Pricing

Behavioral Economics and User Vulnerability

A 2023 experiment by Dernière Heure demonstrated that Uber charged €17.56 for a ride booked on a phone with 12% battery versus €16.60 on a device with 84% battery2. This aligns with Uber’s 2016 findings that low-battery users are 2.3x more likely to accept surge pricing due to urgency7.

Technical Feasibility and Denials

While Uber denies accessing battery data, Android’s open-source API allows apps to request battery status. iOS restricts this access, but indirect metrics (e.g., screen brightness adjustments) might infer low battery12. Rishabh Singh’s 2025 experiment showed that Uber fares increased by 8–12% as battery levels dropped below 20%, regardless of device type12.

Regulatory and Ethical Challenges

Legal Frameworks and Enforcement Gaps

India’s Consumer Protection Act (2019) prohibits unfair trade practices but lacks provisions for algorithmic transparency3. In January 2025, the Central Consumer Protection Authority (CCPA) issued notices to Uber and Ola, demanding explanations for device-based pricing11. However, proving intent remains difficult, as companies attribute discrepancies to “dynamic market conditions”611.

Global Precedents

The European Union’s Digital Markets Act (2023) mandates transparency in algorithmic pricing, while California’ Consumer Privacy Act grants users the right to opt out of data-driven pricing5. Neither regulation fully addresses device-based discrimination.

Recommendations for Fair Pricing Practices

1. Algorithmic Audits: Independent third parties should audit pricing models for bias310.

2. Transparency Requirements: Companies must disclose factors influencing prices (e.g., device type, battery levels)38.

3. Consumer Education: Public awareness campaigns can teach users to comparison-shop across devices3.

4. Regulatory Reforms: Update laws to classify device-based pricing as discriminatory611.

Conclusion: Toward Equitable Digital Marketplaces

The debate over device-based pricing underscores a broader tension between profit maximization and consumer fairness. While algorithms enhance efficiency, their opacity risks exacerbating inequality. Regulatory bodies must balance innovation with accountability, ensuring that digital platforms serve all users equitably—regardless of their smartphone or battery level.

As Uber, Zepto, and similar companies refine their models, the onus lies on policymakers to bridge the gap between algorithmic potential and ethical practice. Only through transparency and inclusivity can the digital economy avoid deepening societal divides.

The Hidden Algorithm Behind Your Phone’s Price Tag

Digital marketplaces like Uber and Zepto have mastered the art of dynamic pricing, but recent revelations show they’re not just adjusting prices based on demand. Your smartphone type (Android or iPhone), battery level, and even your location can now influence how much you pay for rides, groceries, and more.

Ride-Hailing Apps: iPhones Pay More

In December 2024, Indian entrepreneur Sudhir Kalra exposed Uber’s device-based pricing by sharing two screenshots for the same route: ₹290 on Android vs ₹342 on iPhone. Similar trends emerged in Chennai, where iPhone users consistently paid 15–20% more for short rides.

Uber claims prices depend on “real-time demand,” but leaked documents reveal their algorithms analyze device metadata. A 2023 Belgian study found users with 12% phone battery paid 6% more for Uber rides than those with 84% battery.

Zepto’s “Apple Tax” Controversy

Bengaluru-based entrepreneur Vinita Singh uncovered a 510% price difference for capsicum on Zepto: ₹21 (Android) vs ₹107 (iPhone). The app allegedly targets iPhone users with inflated prices for staples like curry leaves (₹9 vs ₹29) and sunflower oil (₹199 vs ₹249). Analysts argue this exploits the perception that iPhone owners have higher purchasing power.

Why Low Battery Costs You More

Uber’s former Head of Economic Research, Keith Chen, admitted in 2016 that users with low battery levels are 2.3x more likely to accept surge pricing. A 2025 experiment by developer Rishabh Singh confirmed Uber fares rise by 8–12% when phone batteries dip below 20%.

How to Fight Back Against Algorithmic Pricing

1. Compare Devices: Check prices on Android and iPhone before booking.
2. Charge Your Phone: Avoid booking rides or groceries with <20% battery.
3. Use Incognito Mode: Prevent apps from tracking your browsing habits.

The Future of Fair Pricing

India’s Central Consumer Protection Authority (CCPA) issued notices to Uber and Zepto in 2025, but regulatory gaps persist. The EU’s Digital Markets Act (2023) offers a blueprint by requiring transparency in algorithmic pricing. Until reforms arrive, staying informed is your best defense.

Pro Tip: Book rides during off-peak hours and clear app cookies weekly to reset price profiles.