Relational databases are great for structured, predictable schemas — but our data needs are dynamic and constantly evolving. Each Teruza instance can support:

• Hundreds of thousands of users
• Years of trading history
• Custom CRM fields
• Dynamic compliance logs
• Time-based financial triggers and audits

Storing and querying all of that using only traditional SQL systems quickly led to unnecessary complexity, performance bottlenecks, and painful migrations whenever business requirements changed. As datasets grew, so did the load — and the limits.

We also needed a way to query massive, multi-tenant datasets in real time, with full auditability and low latency — without relying on deeply nested joins or overly complex relational structures that don’t scale cleanly.

And just as importantly, we needed a solution that wouldn’t lock us into expensive cloud ecosystems. Platforms like AWS or Google Cloud offer powerful hosted databases, but the cost at scale was simply too high — especially for clients who didn’t need or want hyperscaler pricing. By using open-source MongoDB, we’ve been able to cut database hosting costs by up to 90% for some clients, without compromising on performance or flexibility.