Solution
Our data pipeline is the bottleneck, not the compute
Expensive accelerators wait on storage and data movement instead of doing work.
It is easy to assume a slow AI workload needs faster or more numerous accelerators. Frequently it does not — the accelerators are idle, waiting for data to arrive from storage, from another region, or from another cloud.
This is the layer teams look at last and the one that most often explains a disappointing benchmark. It also drives cost in a way that is hard to see, because data transfer charges appear on a different part of the invoice from the compute they are starving.
What is usually going on
Storage throughput sized for a smaller job
Training and batch inference read data far faster than typical application workloads. Storage that was adequate before becomes the limit.
Data and compute in different places
When the dataset lives in one cloud or region and the accelerators are in another, every run pays for the distance in both time and transfer charges.
Checkpoint writes stalling the job
Large checkpoints written to slow or distant storage pause everything. Frequency, location and format all matter, and are usually set once and never revisited.
Preprocessing on the critical path
Transformation done inline that could have been done once and cached, leaving expensive hardware waiting on CPU work.
Egress charges nobody forecast
Moving data out of a cloud is billed. At AI data volumes this becomes material quickly and rarely appears in the original business case.
What we look at, in order
- 01 Establish where time actually goes in a representative run — compute, input wait, checkpoint, transfer
- 02 Map where data lives relative to where it is processed, and what that distance costs
- 03 Review storage throughput against what the workload demands
- 04 Assess caching, staging and preprocessing strategy
- 05 Compare private interconnect against what you currently pay in transfer charges
What you receive
- Bottleneck analysis showing where the time and money actually go
- Storage and data-locality recommendation
- Checkpoint and caching strategy
- Interconnect options costed against current transfer spend
Delivered as a written assessment. More on how we work.
Products relevant to this
Not a shortlist for you specifically — that depends on constraints this page cannot know. These are the options an engineering team addressing this problem will encounter first.
Megaport
L2 · Network as a service
A software-defined network service for creating private connections between data centres and cloud providers on demand.
- Fit
- Hybrid estates moving enough data for transfer charges to matter.
- Catch
- Only useful where they are already present in your facility.
Equinix Fabric
L2 · Interconnection
On-demand interconnection between Equinix facilities, cloud providers and other participants in them.
- Fit
- Organisations with equipment already in an Equinix facility.
- Catch
- Compelling if you are already there; a weak reason to move.
AWS, Google Cloud and Azure
L3 · Hyperscale cloud
The three large general-purpose clouds, each offering accelerated compute alongside everything else you already run.
- Fit
- Estates where the data, identity and compliance posture already live.
- Catch
- Generally the highest hourly rate, and quota is often the real limit.
Other problems
Tell us what you are trying to solve.
Describe your current setup and the problem in your own words. We reply within 2 working days with an honest assessment of whether we can help.