Overview
Some workloads cannot go to a public cloud. The data is regulated, residency rules keep it in the country, responses have to be local, or running models at volume costs less on owned capacity than on rented. This service is for those cases.
We plan capacity from the workload rather than from a catalogue: which models, of what size, serving how many requests, with what failover. Then we design and build the compute, GPU, networking and storage to match, whether it sits on premise, in a colocation facility or across a hybrid cloud.
Where the data cannot leave the building, the engineering has to be better, not simpler. There is no elastic capacity to hide a mistake behind, so monitoring, failover and capacity planning are designed in from the start, and we can operate the environment after handover.
Capabilities
Capacity and GPU planning
Compute sized from the models, volumes and response times the workload needs, with room for growth and a clear cost model.
Colocation and hybrid cloud
Each workload placed where it fits best across owned facilities, colocation and public cloud, with residency rules respected.
Networking
Network design for inference and training traffic, segmentation of sensitive systems and secure links between sites.
Storage
Storage for models, training data and operational records, tiered by performance and protected by backup and recovery plans.
Monitoring and operations
Health, capacity and utilisation monitored continuously, with alerting, runbooks and on-call support.
Resilience and recovery
Failover, backup and disaster recovery designed and tested, so a failed component does not become a stopped operation.
Technical detail
Industries
Questions
- When does owned capacity make sense instead of public cloud?
- When data cannot leave a jurisdiction or a building, when responses have to be local, or when steady, high-volume inference costs more to rent than to run. The plan compares the options on your workload.
- Can you work with our existing facilities?
- Yes. We start from the facilities, hardware and cloud accounts already in place and extend them where the workload requires.
- Do you operate the environment after it is built?
- We can. Managed AI Operations covers monitoring, maintenance and on-call support, or we hand over to your team with documentation and runbooks.
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