Legaltech: Google Cloud to AWS Migration with 30% Cost Reduction

A Brazilian legaltech that helps lawyers and legal teams win more clients, capture fees more efficiently, and gain daily productivity through technology. The platform supports legal research in the era of Generative AI, combining case law with jurimetrics to reduce manual work and enable more data-driven decisions in legal practice.

Positioned at the intersection of data, artificial intelligence, and law within Brazil's legal innovation ecosystem, the company offers a technology foundation that supports everything from advanced legal analyses to day-to-day operational workflows for law firms and legal departments.

The platform operated on Google Cloud and, as the product scaled, clear limitations emerged related to cost, scalability, and operational consistency.

The first point was cost: high and difficult to optimize without a structured rightsizing effort. The second was platform elasticity, which needed to scale up and down based on real load signals rather than just static provisioning. The third challenge was in build and deploy automation maturity across an environment with a very large number of services. When each team or repository evolves its own pipeline standards over time, inconsistency accumulates: different deployment methods, different quality gates, and different operational behaviors. At this stage of scale, this translates to slower deliveries, more complex governance, and greater operational overhead.

The migration itself also had a significant dimension. The environment included approximately 150 buckets totaling roughly 20 TB of data, a large Elasticsearch cluster, multiple databases (including MongoDB, PostgreSQL, and MySQL), Kafka, network components, and approximately 200 microservices and repositories. The challenge was not just moving infrastructure, but arriving at AWS with an operational model that would make growth cheaper and day-to-day execution more predictable.

Elevata led a migration and modernization program focused on moving the platform to AWS while simultaneously improving delivery mechanisms and infrastructure cost efficiency.

A fundamental step was defining a single build and deploy standard that could be applied across the approximately 200 repositories. In practice, this meant modernizing how applications were built and deployed, then replicating that standard consistently, avoiding isolated pipelines and per-service customizations. The impact goes beyond a technical decision: consistent standards directly affect delivery speed and release confidence, as teams begin operating within governed, predictable workflows rather than bespoke processes.

With delivery mechanisms standardized, Elevata performed a detailed database sizing and infrastructure utilization analysis to identify cost and performance opportunities. Database sizes and compute requirements were reviewed, with adjustments to compute and storage to reduce overprovisioning. Where appropriate, Graviton instances and Spot Instances were adopted for stateless workloads — a strategy that reduces compute costs while maintaining reliability, provided workloads are designed to tolerate interruptions.

To improve scalability and avoid paying for peak capacity 24/7, Elevata implemented autoscaling based on real workload behavior, going beyond basic thresholds. Scaling decisions began considering custom metrics such as processing queue depth in addition to CPU and memory. This allowed the environment to scale more intelligently, adjusting better to actual traffic patterns.

Finally, governance and consistency were built directly into the deploy process through pipeline controls. This included mechanisms such as merge blocking and automated checks to validate security and code consistency before changes were deployed. For the client, this means compliance discipline that scales with the company: the same set of rules applies regardless of which team owns a service.