• BERANDA > Interview-2025 > MongoDB > A while back, Temenos Banking Cloud scaled to record high transactions with MongoDB Atlas and Microsoft Azure. Can you shed some light on it?

A while back, Temenos Banking Cloud scaled to record high transactions with MongoDB Atlas and Microsoft Azure. Can you shed some light on it?

That benchmark was a landmark moment, powerfully demonstrating the enterprise-grade scalability and efficiency that modern cloud-native banking demands, particularly in the realm of Banking-as-a-Service (BaaS) and embedded finance.

1. Unmatched BaaS Scalability: The key result was the platform processing a staggering 150,000 transactions per second, simulating an environment supporting 50 million retail customers and 50 million embedded finance customers on a single cloud instance. This proves the combined stack is robust and elastic enough to handle the massive, volatile transaction volumes required for BaaS.
2. The Document Model Advantage for Core Banking: The success is rooted in the fundamental architectural fit of the document model (native JSON) for banking data. Unlike traditional relational systems, which capture related entities (like a day’s worth of account transactions) across numerous rows that require slow, complex joins at runtime, the document model allows that related data to be stored together in one object. For instance, a month’s cash flow can be read in a single query by accessing one document, rather than having to query and aggregate multiple individual rows. This architectural alignment is key: beside Temenos Transact (core banking), their front office digital banking solution, Temenos Digital’s microservices layer also utilizes a JSON-based model, leveraging MongoDB like an Operational Data Layer (ODL) to provide enriched, high-performance APIs for core banking data access.
3. Operational Efficiency and Sustainability: The results showed monumental efficiency gains. MongoDB Atlas delivered the needed database performance with one third of the resources compared to the prior year’s result, while delivering 50% more throughput and being two times faster in throughput per transaction (measured in milliseconds). This improved efficiency directly translates to lower operational costs and a measurable reduction in the ESG footprint.