Cloud optimization, as it has been practiced, has not been to the benefit of the cloud's customers.
Serra Labs was founded to change that.
Why cloud optimization needed rethinking
When a customer's workload runs on the wrong configuration — overpaying, underperforming, or both — the cloud provider still gets paid. There is no incentive on the provider's side to surface that misalignment. The tools built to help were mostly designed to minimize cost, applied uniformly, without regard for what the workload actually needed to deliver. For a lot of workloads, that was good enough. For a lot more, it wasn't.
The arrival of AI infrastructure made the gap impossible to ignore. GPU workloads don't behave like CPU workloads. The same cost-minimization logic that works well for a database produces the wrong answer for an AI training run — where spending less doesn't save money, it just slows results down. And across all workloads, the lifecycle stage matters: a workload in prototyping has different requirements from the same workload in production, and treating them the same wastes budget in one direction and leaves performance on the table in the other.
Serra Labs has developed a platform that enables cloud workloads to be optimized with the right goal — determined by workload behavior and its lifecycle stage. Doing so requires solving a problem of exponential complexity: matching the optimal configuration across GPU cores, VRAM, CPU, memory, network, and storage, across potentially millions of combinations, in reasonable time. We solve it using patent-pending methods built for exactly this purpose.
Location
Silicon Valley
Founded by a team with deep background in cloud optimization, monitoring, migration, and service delivery across enterprise and cloud-native environments.
Integrations
Works with Amazon Web Services, Microsoft Azure, and NVIDIA GPU infrastructure.
The Hard Problem
Finding the optimal cloud configuration is a problem of exponential complexity — NP-hard, with potentially millions of combinations per workload. Our patent-pending methods solve it in reasonable time, at scale — finding the configuration that satisfies the optimization goal sought by the customer.
Our Values
What guides our approach
💡 Trusted Innovation
We believe in using cutting-edge tools and technology, with rigor, so that customers can reliably acrue the benefits.
⚖️ Balance & Control
We believe in helping customers make balanced decisions with regard to workload optimization so that they can exercise control over their cost and performance.
🪴 Sustainability
We believe in helping our customers be optimal whether it be to maximize savings, maximize value, or maximize performance. We believe that optimality helps enhance sustainability.
