Cloud Optimization & FinOps
The cloud should work for your business – not the other way around. Our Cloud Optimization services help organizations cut unnecessary costs, improve performance, and increase predictability without sacrificing flexibility or scalability.
We combine FinOps practices, deep engineering expertise, and hands-on experience with AWS, Azure, and Google Cloud to make your cloud investments transparent, controlled, and efficient.
We operate at the intersection of engineering and finance – from detailed billing analysis and architectural reviews to autoscaling, storage, and observability optimization.
Bring full transparency to your cloud spend and establish FinOps practices for better financial control.
Right-size compute and services so you don’t pay for capacity you never use.
Tune autoscaling so you spend less during off-peak hours while staying resilient under load.
Optimize storage and data lifecycles so you don’t overpay for cold or archival data.
Improve database and application performance while lowering latency and query costs.
Build the right level of observability and governance so cloud usage stays visible and controlled.
We help SaaS, e-commerce, media, and enterprise organizations reduce cloud costs, improve performance, and make infrastructure more predictable.
A fast-growing SaaS provider faced unpredictable cloud bills and periodic performance bottlenecks under peak load.
We conducted a FinOps audit, right-sized instances, tuned autoscaling, introduced non-prod scheduling policies, and delivered spend dashboards.
An e-commerce platform overpaid for EC2 and RDS capacity during seasonal peaks while running underutilized most of the year.
We redesigned instance usage, added reserved instances for baseline load, tuned autoscaling groups, and optimized RDS configuration.
A media company processed large volumes of streaming data in GCP and faced rapidly growing BigQuery and Dataflow costs.
We optimized data schemas, enabled partitioning and clustering in BigQuery, tuned Dataflow jobs, and established policies for intermediate data retention.
A large enterprise with multiple engineering teams had fragmented logging, monitoring, and access policies, making cost and reliability hard to control.
We unified the observability stack, rolled out centralized resource tagging, introduced baseline access policies, and established regular architectural reviews.
An early-stage tech startup was growing quickly without a structured approach to environments and cost control, risking runaway cloud bills.
We designed a baseline architecture with clear environment separation, tagging, budgets, and simple usage rules understandable to the whole team.