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MongoDB | Blog

10 Years of MongoDB Atlas: Built for what’s Next Build Trust in Agentic AI: From POC to Production Production-Ready Agents Need A Production-Ready Data Platform Agentic Supplier Management with MongoDB Atlas, Voyage AI, and Multi-Modal Search Fighting Tool Sprawl: The Case for AI Tool Registries AI Is Changing What Customers Need From a Database. MongoDB 8.3 Is Built for It New Research Reveals Overcoming Legacy Tech Issues Key to AI Success MongoDB Predictive Auto-Scaling: An Experiment Introducing MongoDB Agent Skills and Plugins for Coding Agents Enhance Your In-IDE Data Browsing Experience With MongoDB Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration Towards Model-based Verification of a Key-Value Storage Engine Inside MongoDB Dublin: The Heart of Our International Growth Innovating with MongoDB | Customer Successes, February 2026 Building a Movie Recommendation Engine with Hugging Face and Voyage AI Edge AI Made Easy: MongoDB and ObjectBox Data Synchronization MongoDB.local San Francisco 2026: Ship Production AI, Faster Vision RAG: Enabling Search on Any Documents That’s a Wrap! MongoDB’s 2025 in Review & 2026 Predictions Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries MongoDB Announces Leadership Transition The Cost of Not Knowing MongoDB, Part 3: appV6R0 to appV6R4 The 10 Skills I Was Missing as a MongoDB User Innovating with MongoDB | Customer Successes, October 2025 Smarter AI Search, Powered by MongoDB Atlas and Pureinsights Charting a New Course for SaaS Security: Why MongoDB Helped Build the SSCF Top Considerations When Choosing a Hybrid Search Solution Endian Communication Systems and Information Exchange in Bytes MongoDB SQL Interface: Now Available for Enterprise Advanced From Niche NoSQL to Enterprise Powerhouse: The Story of MongoDB's Evolution Carrying Complexity, Delivering Agility MongoDB is a Glassdoor Best-Led Company of 2025 Build AI Agents Worth Keeping: The Canvas Framework Simplify AI-Driven Data Connectivity With MongoDB and MCP Toolbox MongoDB Community Edition to Atlas: A Migration Masterclass With BharatPE Modernizing Core Insurance Systems: Breaking the Batch Bottleneck MongoDB.local NYC 2025:定义 AI 时代的理想数据库 MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era MongoDB.local NYC 2025: Definiendo la base de datos ideal para la era de la IA MongoDB.local NYC 2025 : définir la base de données idéale à l'ère de l'IA MongoDB.local NYC 2025: Definindo o Banco de Dados Ideal para a Era da IA MongoDB.local NYC 2025: AI 시대를 위한 이상적인 데이터베이스 정의 MongoDB.local NYC 2025: Definition der idealen Datenbank für das KI-Zeitalter MongoDB.local NYC 2025: Definire il database ideale per l'era dell'AI Hommage à l’excellence : MongoDB Global Partner Awards 2025 Wir feiern Spitzenleistungen: MongoDB Global Partner Awards 2025 Celebrating Excellence: MongoDB Global Partner Awards 2025 庆祝卓越:MongoDB 全球合作伙伴奖 2025 Celebrando la Excelencia: Premios Globales de Emparejar de MongoDB 2025 Começando a destacar a excelência: MongoDB GlobalPartner Services 2025 Celebrare l'eccellenza: MongoDB Global Partner Awards 2025 우수성을 기념하기: 2025년 MongoDB 글로벌 파트너 어워드 The Future of AI Software Development is Agentic MongoDB Queryable Encryption Expands Search Power Supercharge Self-Managed Apps With Search and Vector Search Capabilities Potencie las aplicaciones autogestionadas con capacidades de búsqueda y búsqueda vectorial
Cars24 Improves Search For 300 Million Users With MongoDB Atlas
Nick Bell · 2025-10-13 · via MongoDB | Blog

The Indian multinational online car marketplace Cars24 serves 300 million users globally. The company offers services that span sales, insurance, maintenance, financing, and more, reshaping the entire car ownership journey.

Speaking at MongoDB .local Bengaluru in July 2025, Pradeep Sharma, Head of Technology at Cars24, shared how MongoDB has been a key driver of Car24’s digital transformation journey. Specifically, he highlighted two recent use cases that show how MongoDB Atlas has helped Cars24 scale, improve its search capabilities, and reduce its architectural complexity.

Matching the growing scale with simplified and expanded search

Cars24 has operations in multiple countries, and a diverse customer base. Over the years, the company has used customer data, behavior analytics, and operational workflows to build, evolving from being a platform for buying and selling cars, to an end-to-end ecosystem, supported by a hub of interconnected systems.

At the start of its journey, Cars24 relied on legacy databases for managing and searching data, such as Postgres. Their relational database set-up would store information, synchronize the data to a separate “bolt-on” search engine (such as Elasticsearch), manually indexing it, and then querying the index.

While initially effective for a small application ecosystem, these processes became bottlenecked as the organization’s services grew. Multiple engineering teams piped data into a single search index, which often resulted in synchronization challenges and overwhelming administrative overhead.

Cars24 faced three core limitations with this setup:

  • Lower developer productivity: Exponential effort was spent maintaining pipelines and synchronizing procedures. Developers had little bandwidth for building business features or innovation.

  • Architectural complexity: Ensuring data sync consistency required multiple pipelines and race logic. This led to inefficiencies in real-time dashboard updates for agents.

  • Operational overhead: Maintaining separate systems for database and search—alongside provisioning, patching, scaling, and monitoring—strained resources.

Seeking an integrated approach, Cars24 embraced MongoDB Atlas, hosted on Google Cloud. MongoDB Atlas would serve as a single, consistent, modern database and embedded search solution, powered by Apache Lucene.

MongoDB Atlas Search also enabled Cars24 to run queries directly in the database. This eliminated the need to synchronise data between systems while delivering real-time results.

This unified approach allowed the company’s developers to transition from managing complex synchronization mechanisms to building applications. Furthermore, the reduced administrative overhead enabled Cars24 to consolidate the team’s efforts, and to streamline query execution across the ecosystem.

Thanks to MongoDB Atlas and MongoDB Atlas Search, Cars24 was able to:

  • Avoid "synchronization tax”: Switching to MongoDB Atlas eliminated the need for data synchronization and the additional tooling this mandated. Real-time searches can be performed from a single interface and workflow.

  • Deliver new search features faster: By using a single, unified API across database and search operations, new features can be delivered rapidly.

  • Work with a fully managed platform: With MongoDB Atlas, Cars24’s engineers can focus more on application development and building products, rather than thinking about managing indexes, syncing, and more.

Following this successful migration, Cars24 decided to also use MongoDB Atlas to replace one of its legacy databases, ArangoDB. The switch to MongoDB Atlas eliminated major roadblocks for other critical search capabilities.

From ArangoDB to MongoDB: Streamlined operations and 50% cost savings

As Cars24 scaled new services globally, it encountered limitations with its geospatial search solution, which was based on ArangoDB. This included performance bottlenecks, weak transactions as it was difficult to guarantee consistent data operations, and a limited ecosystem which meant that scaling developer onboarding and troubleshooting became increasingly onerous.
Moving to MongoDB Atlas enabled Cars24 to transition its geospatial services, consolidating its data storage and search capabilities under a single, versatile platform.

“We now have a highly available architecture, and an amazing team at MongoDB that has our back,” said Sharma.

MongoDB offered a proven architecture for high availability, scalability, and real-world production readiness:

  • Enhanced scalability: MongoDB’s ability to scale massive workloads supports Cars24’s growing global presence.

  • Reliable transactions: MongoDB provides robust multi-document ACID transactions across shards, meeting mission-critical needs.

  • Streamlined operations: MongoDB offers a single platform that is not limited to a database only. By consolidating its geospatial search workload under MongoDB, Cars24 has reduced maintenance and operational overhead.

Not only did Cars24 cut costs in half by moving to MongoDB, but the widespread market adoption of MongoDB Atlas also means that Cars24 can continue to rapidly onboard developers familiar with MongoDB, a recruiting priority for Cars24’s growing development team.

“To give you an idea, one of our business units had a developer team of less than 10 about a year ago. Now they are a triple-digit team,” said Sharma. “If we are going to keep introducing new developers, for a product coming up or scaling up, it becomes very important to focus on the community skills and support provided by our technology partner.”

“Now that we have moved from ArangoDB to MongoDB Atlas, our developers are the happiest,” he added.

Cars24 is now looking to consolidate even more of its application and data workflows under MongoDB Atlas. With the growing number of developers joining Cars24’s engineering teams, plans are to utilize MongoDB Atlas further to enhance productivity, scalability, and data-driven insights.