Processing performance
Measure images processed per second and end-to-end job duration against the cloud baseline.
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Drone technology / Industry use case
Taking computer vision beyond the cloud.
A path to local inference for a drone team with growing vision workloads.
01 / The business challenge
A better foundation for the work ahead.
As a drone business processes more imagery, compute demand can become harder to forecast. Uploads, network availability, and changing cloud bills can constrain how quickly its team evaluates new vision models.
Why consider private AI?
A dedicated inference environment could put eligible image-processing workloads closer to the team and its data. Local infrastructure gives the organization more control over capacity, model versions, and when jobs run.
02 / The Corsantic approach
Understand first. Build with purpose.
- Profile representative imagery, model accuracy, throughput, and response-time requirements.
- Benchmark suitable vision models on candidate GPU configurations before committing to hardware.
- Design a staged migration with an external-cloud fallback for workloads that remain better suited to it.
Deployment approach
A deployment could pair a right-sized GPU system with a local model-serving layer, job queues, monitored storage, and access controls. This is ground-based processing infrastructure; it does not imply onboard drone inference or flight-control capabilities.
03 / What success would look like
Outcomes to validate together.
Greater independence
Run eligible processing jobs without depending on an external inference API.
Room to scale
Add capacity when measured utilization and demand justify it.
Validation before expansion
A pilot would need to demonstrate model accuracy, sustained throughput, recovery behaviour, and total operating cost.
Build your business case
Explore our 24-month cost comparison to evaluate ownership costs, retained cloud usage, and projected savings against your own workloads.
The next chapter starts with your needs