Solutions
Start with the problem, not the product
These are the six situations we are asked about most often. Each page explains what usually causes it, what we look at first, and which products are worth understanding.
Our inference costs rise faster than our usage
L4 · L3The model is in production and working. The monthly bill is growing faster than the business it supports.
We are weighing model APIs against running our own
L4 · L3Provider invoices have grown past the point where self-hosting on dedicated accelerators is worth costing properly.
It works in the pilot but not in production
L4 · L3 · L2The prototype proved the idea. Turning it into something dependable has stalled.
We cannot see where our AI spend actually goes
L4 · L3The invoice arrives as one number. Nobody can attribute it to a team, a feature or a customer.
We are paying for accelerators that sit idle
L4Reserved or owned capacity goes unused while work queues elsewhere — and preempted jobs lose their state.
We are locked into one model provider
L4A price change, a deprecation or an outage upstream would be a serious problem for us.
Our data pipeline is the bottleneck, not the compute
L3 · L2Expensive accelerators wait on storage and data movement instead of doing work.
We need to run AI on infrastructure we control
L1 · L2 · L3 · L4Regulation, contractual commitments or data sensitivity mean it cannot all sit in a public cloud.
We are about to commit, and it is hard to undo
L3 · L4A one to three year reservation or a hardware purchase is on the table, and the decision outlives the workload that justified it.
Not sure which of these it is
Describe the situation in your own words and we will tell you which layer we think it sits in.
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.