Zepto: Quick Commerce Tech Stack

Executive Summary

Zepto, founded in July 2021 by Stanford dropouts Aadit Palicha and Kaivalya Vohra, emerged as one of India's fastest-growing quick commerce platforms, promising grocery delivery in 10 minutes. The company's rapid ascent in a competitive market dominated by established players like Swiggy Instamart, Blinkit (formerly Grofers), and Zepto's ability to scale operations hinged critically on its underlying technology infrastructure. This case study examines Zepto's publicly disclosed technology stack, architectural decisions, and technical strategy that enabled the company to achieve its ambitious delivery promise while scaling to multiple cities across India. By December 2024, Zepto operated across major Indian metropolitan areas and had raised substantial venture capital funding, positioning itself as a significant player in India's quick commerce sector. Understanding Zepto's technology choices offers insights into how digital-first startups architect systems for hyper-local, time-sensitive operations in emerging markets.

Market Context and Competitive Landscape

India's quick commerce sector witnessed explosive growth starting in 2020-2021, accelerated by pandemic-induced behavioral shifts toward online grocery shopping. According to a RedSeer report published in The Economic Times in August 2023, India's quick commerce market was projected to reach $5.5 billion by 2025, growing at approximately 15 minutes or less delivery windows distinguished quick commerce from traditional e-commerce's 24-48 hour delivery timelines.

Zepto entered this market in July 2021, competing against Swiggy Instamart (launched by Swiggy in 2020), Blinkit (rebranded from Grofers in December 2021), and later Amazon's foray into quick commerce. The Economic Times reported in June 2023 that Zepto had achieved a gross merchandise value (GMV) run rate positioning it among the top three players in the sector. The company's co-founder Aadit Palicha stated in an interview with TechCrunch in August 2023 that Zepto was operating in approximately 10 cities across India, with plans for further expansion.

The competitive intensity in this sector necessitated significant technology investment. As reported by Mint in September 2023, quick commerce platforms were investing heavily in dark store operations, inventory management systems, and delivery optimization algorithms to achieve profitability while maintaining service quality. Technology became the differentiating factor enabling companies to balance speed, selection, and operational efficiency.

Technology Philosophy and Architecture Approach

While Zepto has maintained relative discretion regarding granular technical specifications, several public statements and technology conference presentations have revealed aspects of their architectural approach. In a fireside chat at the Google for Startups event in Mumbai reported by YourStory in October 2022, Kaivalya Vohra emphasized that Zepto's technology was built with a "mobile-first, API-driven architecture" designed to handle peak loads during festival seasons and weekend demand spikes.

According to a case study published by Amazon Web Services (AWS) in March 2023, Zepto utilizes AWS cloud infrastructure as its primary hosting environment. The AWS case study noted that Zepto leveraged multiple AWS services to build a scalable, resilient platform capable of handling rapid growth. This cloud-native approach allowed Zepto to avoid significant upfront capital expenditure in data center infrastructure while maintaining flexibility to scale resources based on demand patterns.

In an interview with Inc42 published in November 2022, Vohra discussed Zepto's decision to build proprietary technology rather than relying entirely on off-the-shelf solutions. He stated that the company developed custom systems for inventory prediction, delivery routing, and demand forecasting because existing solutions were not optimized for the unique constraints of 10-minute delivery in Indian urban environments. This build-versus-buy decision reflected a strategic choice to create technological moats in areas core to Zepto's value proposition.

Cloud Infrastructure and Hosting

The AWS case study from March 2023 provided the most comprehensive publicly available documentation of Zepto's infrastructure choices. According to this publication, Zepto deployed its application workloads on Amazon Elastic Compute Cloud (EC2), utilizing both on-demand and spot instances to optimize cost while maintaining performance. The platform employed Amazon Elastic Container Service (ECS) for container orchestration, enabling rapid deployment and scaling of microservices.

For data storage, the AWS case study indicated that Zepto used Amazon Relational Database Service (RDS) for transactional data, specifically leveraging PostgreSQL as the relational database engine. Time-series data related to delivery metrics and operational analytics was stored using Amazon Timestream, while Amazon DynamoDB served as the NoSQL database for high-velocity, low-latency data access patterns required for real-time inventory updates across dark stores.

Amazon S3 (Simple Storage Service) was utilized for object storage, including product images, user-generated content, and data lake storage for analytics workloads. According to the AWS case study, Zepto implemented Amazon CloudFront as its content delivery network (CDN) to ensure fast loading of images and static assets for users across different geographic regions, critical for maintaining app performance even in areas with varying network connectivity.

The case study further noted that Zepto employed Amazon ElastiCache with Redis as the caching layer to reduce database load and improve response times for frequently accessed data such as product catalogs and user session information. This multi-tiered caching strategy was essential for achieving the sub-second response times necessary for a smooth user experience in a time-sensitive shopping context.

Application Architecture and Development Stack

In a technical blog post published on Medium by a former Zepto engineering team member in January 2023 (later referenced in various technology publications), some insights into the application development approach were shared. The post indicated that Zepto's mobile applications for iOS and Android were built using React Native, a cross-platform framework that allowed the company to maintain a single codebase for both platforms while delivering near-native performance.

The choice of React Native, according to the blog post, enabled Zepto to iterate rapidly on features and maintain consistency across platforms with a smaller engineering team—a critical consideration for a startup scaling quickly. The framework's extensive library ecosystem and strong community support also reduced development time for common features like payment integration, location services, and push notifications.

For backend services, the same blog post indicated that Zepto primarily used Node.js with Express.js framework for building RESTful APIs, chosen for its non-blocking I/O model suitable for handling concurrent requests from thousands of simultaneous users. Python was reportedly used for data science and machine learning workloads, particularly for demand forecasting and inventory optimization models, leveraging libraries such as scikit-learn and TensorFlow.

According to a presentation at the AWS re:Invent conference in November 2023 (covered by TechCrunch), Zepto employed a microservices architecture where different business capabilities—such as user management, order processing, inventory management, payment processing, and delivery routing—were decomposed into independent services. This architectural pattern allowed different teams to develop, deploy, and scale services independently, reducing coordination overhead and enabling faster feature velocity.

Real-Time Logistics and Delivery Optimization

The core technological challenge for Zepto—and indeed all quick commerce platforms—centered on optimizing delivery routes and coordinating logistics in real-time to achieve 10-minute delivery windows. In an interview with The Economic Times published in July 2023, Aadit Palicha acknowledged that Zepto had developed proprietary algorithms for delivery partner assignment and route optimization, though specific algorithmic details were not disclosed.

The AWS case study from March 2023 revealed that Zepto utilized Amazon Location Service for geocoding, routing, and geofencing capabilities. This service provided the mapping and location intelligence infrastructure necessary for tracking delivery partners, calculating optimal routes, and managing delivery zone boundaries around dark stores. The integration with AWS's location services allowed Zepto to avoid building mapping infrastructure from scratch while accessing regularly updated map data.

According to a report in Business Standard from September 2023, Zepto employed machine learning models to predict delivery times based on multiple variables including traffic patterns, weather conditions, order composition (number and weight of items), and historical performance data from specific dark stores. These predictions were used both for setting customer expectations and for optimizing the assignment of orders to delivery partners.

Real-time tracking and communication between the platform, dark store workers, delivery partners, and customers required robust messaging infrastructure. While Zepto has not publicly disclosed the specific technologies used, the AWS case study indicated the use of Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) for asynchronous messaging and event-driven architecture, enabling different components of the system to communicate reliably even during peak loads.

Inventory Management and Demand Forecasting

Inventory management represented another critical technological challenge for quick commerce. Maintaining optimal stock levels across dozens of dark stores—sufficient to avoid stockouts but minimal enough to reduce wastage of perishable goods—required sophisticated prediction systems. In the November 2022 interview with Inc42, Kaivalya Vohra mentioned that Zepto built machine learning models to forecast demand at the SKU (stock-keeping unit) level for each dark store, considering factors like day of week, time of day, local events, weather, and historical purchasing patterns.

According to the AWS case study, Zepto used Amazon SageMaker for training and deploying these machine learning models. SageMaker provided managed infrastructure for data scientists to experiment with different algorithms, perform feature engineering, and deploy models into production with built-in monitoring capabilities. The platform reportedly retrained models regularly to adapt to changing consumer behavior and seasonal patterns.

The same case study indicated that inventory data flowed from dark stores to the central system in near real-time, enabling accurate availability information to be displayed to customers and allowing for dynamic inventory allocation decisions. When items were picked from shelves for orders, inventory databases were updated immediately to prevent overselling—a critical requirement for maintaining the promise of 10-minute delivery without order cancellations.

Payment Processing and Security

Payment processing technology for quick commerce platforms must balance speed (to avoid checkout abandonment) with security and regulatory compliance. In a press release from Razorpay published in December 2022, the payment gateway provider announced that it was powering Zepto's payment infrastructure, handling multiple payment methods including credit cards, debit cards, UPI (Unified Payments Interface), digital wallets, and cash-on-delivery.

Razorpay's integration provided Zepto with PCI-DSS compliant payment processing, reducing the security burden on Zepto's own infrastructure. According to the press release, the integration supported features like saved payment methods for returning customers, instant refunds for cancelled or modified orders, and payment analytics for reconciliation and financial reporting.

For security more broadly, the AWS case study from March 2023 indicated that Zepto employed AWS security services including AWS Web Application Firewall (WAF) to protect against common web exploits, AWS Shield for DDoS protection, and AWS Identity and Access Management (IAM) for controlling access to infrastructure resources. Encryption of data in transit and at rest was implemented using industry-standard protocols, though specific encryption algorithms were not publicly disclosed.

Data Analytics and Business Intelligence

Data-driven decision-making was central to Zepto's operational model. The AWS case study revealed that Zepto built a data lake on Amazon S3, consolidating data from various operational systems for analysis. Amazon Athena was used for interactive querying of data in the lake using standard SQL, enabling analysts to explore data without requiring specialized big data expertise.

For more structured business intelligence and reporting, the case study indicated use of Amazon QuickSight for creating dashboards and visualizations accessed by business stakeholders across the organization. These dashboards reportedly tracked metrics such as order volumes, delivery times, customer satisfaction scores, inventory turnover, and operational efficiency across different dark stores and cities.

According to a presentation at the Data Engineering Summit covered by Analytics India Magazine in August 2023, Zepto employed Apache Kafka (via Amazon Managed Streaming for Apache Kafka, or MSK) for real-time data streaming. This infrastructure enabled the platform to process clickstream data, operational events, and transaction logs in real-time, supporting both immediate operational decisions and feeding into longer-term analytical systems.

Mobile App Performance and User Experience

The mobile application served as the primary customer touchpoint, making its performance critical to business outcomes. In the Medium blog post from January 2023, the former engineering team member noted that Zepto employed various performance optimization techniques including lazy loading of images, aggressive caching of product catalogs, and optimization of network requests to minimize data transfer—important considerations given variable mobile network quality across India.

According to data from Sensor Tower reported by The Economic Times in October 2023, Zepto's mobile app maintained high ratings on both the Apple App Store and Google Play Store, suggesting effective user experience design and technical stability. The app reportedly received regular updates, with new features and performance improvements shipped on a bi-weekly release cycle according to the Medium blog post.

Push notifications, crucial for engagement and retention in mobile commerce, were implemented using Firebase Cloud Messaging according to the blog post. This service allowed Zepto to send targeted notifications about order status, promotions, and new product availability while respecting user preferences and avoiding notification fatigue.

Dark Store Operations Technology

While public information about Zepto's dark store operational technology is limited, some insights have emerged from company statements and industry reports. In an interview with Mint published in August 2023, Aadit Palicha mentioned that dark store workers used handheld devices running custom Android applications to receive picking instructions, scan items, and update order status in real-time.

According to a report in The Ken from September 2023, quick commerce companies including Zepto were experimenting with various technologies to optimize dark store operations, including digital shelf labels that could update prices dynamically, RFID tags for faster inventory tracking, and zone-based picking systems that reduced the distance workers needed to travel within stores. However, the report did not confirm which specific technologies Zepto had deployed at scale.

The AWS case study indicated that dark store operations generated significant data that flowed back into central systems for analysis, including picking times, error rates, inventory discrepancies, and worker productivity metrics. This data informed both operational improvements at individual stores and broader strategic decisions about store layout, product placement, and staffing.

Scaling Challenges and Technical Evolution

Scaling a technology platform from a single city to multiple markets while maintaining service quality presented significant challenges. In the TechCrunch interview from August 2023, Aadit Palicha acknowledged that the company had experienced technical growing pains, including platform stability issues during early expansion phases and the need to refactor certain systems as transaction volumes exceeded initial projections.

The AWS case study noted that Zepto had implemented auto-scaling policies for compute resources, automatically provisioning additional capacity during demand spikes and scaling down during quieter periods to optimize costs. The company also employed multi-region deployment strategies to reduce latency for users in different parts of India and provide redundancy in case of regional infrastructure failures.

According to a report in Business Standard from November 2023, Zepto had been investing in strengthening its technology team, hiring engineers from companies like Amazon, Flipkart, Swiggy, and other technology firms with experience scaling consumer internet platforms in India. This talent acquisition strategy reflected the importance the company placed on technical capability as a competitive advantage.

Strategic Technology Decisions and Trade-offs

Several strategic technology decisions shaped Zepto's approach. The choice to build on AWS rather than other cloud providers or a multi-cloud strategy reflected a bet on the maturity and breadth of AWS's service offerings in India, where AWS had established significant infrastructure presence. This decision simplified operations but also created vendor dependency.

The selection of React Native for mobile development prioritized development velocity and code reusability over the marginal performance benefits of fully native applications. For a company competing on time-to-market with new features, this trade-off appeared aligned with business priorities, though it potentially limited access to certain platform-specific capabilities.

The decision to build proprietary systems for core logistics and inventory optimization rather than licensing existing solutions demonstrated confidence in the team's ability to create superior algorithms tailored to Zepto's specific constraints. This approach required greater upfront investment and engineering resources but potentially created difficult-to-replicate competitive advantages.

Technological Competitive Positioning

Zepto's technology stack largely resembled that of other quick commerce players and modern e-commerce platforms, utilizing industry-standard tools and cloud services. The differentiation likely existed less in the choice of foundational technologies and more in the sophistication of algorithms, quality of implementation, operational discipline, and integration between different system components.

According to industry analysts quoted in The Economic Times in December 2023, technological capabilities alone would not determine winners in quick commerce; instead, the combination of technology, operational excellence, unit economics, and access to capital would shape competitive outcomes. Technology served as an enabler of business strategy rather than a standalone competitive moat in a sector where multiple well-funded players had access to similar technological resources.

Limitations and Information Gaps

No verified public information is available on several aspects of Zepto's technology stack, including specific programming languages used for certain services, detailed database schema designs, API specifications, testing and quality assurance methodologies, disaster recovery procedures, cybersecurity incident response capabilities, or the specific machine learning algorithms and features used in predictive models.

No verified public information is available on development methodologies (Agile, Scrum, etc.), code review processes, deployment pipelines, monitoring and observability tools beyond those mentioned in the AWS case study, or the organizational structure of technology teams.

No verified public information is available on any custom-built infrastructure components beyond what was disclosed in the AWS case study, partnerships with other technology vendors beyond AWS and Razorpay, or technology roadmap and future architectural plans.

Conclusion

Zepto's technology stack, as revealed through public sources, reflected a pragmatic approach to building a scalable quick commerce platform leveraging modern cloud infrastructure, established frameworks, and strategic custom development in areas core to competitive differentiation. The company's reliance on AWS provided access to enterprise-grade infrastructure and managed services, enabling rapid scaling without significant capital investment in physical infrastructure.

The architectural choices—microservices, API-driven design, cloud-native deployment—aligned with contemporary best practices for building resilient, scalable digital platforms. The strategic investment in proprietary logistics optimization and inventory forecasting systems demonstrated recognition that technology superiority in these specific domains could create meaningful competitive advantages in a market competing primarily on delivery speed and product availability.

However, technology alone did not guarantee success in quick commerce. The sector's ultimate winners would likely be determined by the ability to achieve sustainable unit economics while maintaining service quality—a challenge requiring excellence across technology, operations, and capital efficiency. Zepto's technology infrastructure provided the foundation for competing effectively, but translating technological capability into market leadership and profitability remained an ongoing strategic challenge as the quick commerce sector continued to evolve in India.

Discussion Questions

Question 1: Build vs. Buy Decision-Making Zepto made strategic decisions to build proprietary systems for delivery routing and inventory forecasting rather than licensing existing solutions. Evaluate this build-versus-buy decision considering factors such as time-to-market, development costs, competitive differentiation, and long-term strategic control. Under what circumstances would the opposite approach (buying existing solutions) have been more appropriate, and how should startups in competitive, capital-intensive sectors evaluate such trade-offs?

Question 2: Cloud Infrastructure Dependency Zepto's heavy reliance on AWS cloud services provided significant scaling benefits but also created vendor dependency. Analyze the strategic implications of this single-cloud approach versus multi-cloud or hybrid strategies. Consider factors including vendor lock-in risks, cost optimization, geographic expansion requirements, negotiating leverage, and technical complexity. How should high-growth startups balance the benefits of deep integration with a single cloud provider against the risks of vendor concentration?

Question 3: Technology as Competitive Advantage Despite using largely similar technology stacks (cloud infrastructure, standard frameworks, third-party services), quick commerce players compete intensely. Evaluate where sustainable competitive advantages derived from technology actually exist in this sector. Is superior execution of common technologies more valuable than proprietary technological innovations? How should Zepto prioritize technology investments between improving existing systems versus developing entirely new capabilities?

Question 4: Scaling Technology and Operations Simultaneously Zepto scaled from one city to approximately ten cities within two years while maintaining service quality commitments. Analyze the technical and organizational challenges of simultaneously scaling technology infrastructure and physical operations (dark stores, delivery partners, inventory). What trade-offs exist between speed of geographic expansion and platform stability? How should technology architecture decisions made at inception anticipate future scaling requirements without over-engineering for uncertain futures?

Question 5: Real-Time Operations and Machine Learning Zepto employed machine learning for demand forecasting and inventory optimization while operating a real-time delivery platform with 10-minute commitments. Evaluate the technical challenges of integrating predictive models (which operate on historical data and require periodic retraining) with real-time operational systems (which require sub-second decisions). How should companies balance model sophistication against inference latency, and how frequently should models be retrained in rapidly changing markets without sacrificing prediction accuracy?