Enterprises are buying training grade infrastructure to run chatbots. The vendor conversation starts with how many racks. Ours starts with what you actually need to run, because the most expensive mistake in AI infrastructure is buying the tier above your workload, and it is a mistake the seller has no incentive to stop.
Workload honesty
What enterprises actually run on premises: inference on open weight models, retrieval over internal data, call and document processing, and established machine learning. What almost nobody runs: foundation model training.
Sizing follows the workload, not the vendor's quarter. Say it plainly at the start of every engagement and most of the overspend never happens.
What we do
We start from what you actually need to run: the models, the users, the data, the concurrency. The sizing comes out of that, not out of a vendor reference deal from a customer nothing like you.
Validated designs for the workloads enterprises actually run, published with honest scope. See C4C AI Pods for the tiers and what each is genuinely for.
On prem against cloud against API, priced honestly over the life of the workload, including the staffing and utilisation caveats the comparison decks leave out.
When you are ready to buy, we configure to order through our Dell, HPE and distribution relationships, and negotiate with vendor side knowledge of how the pricing is built.
Our validated designs
Our reference architectures for the AI workloads enterprises actually run, published as C4C Validated Designs: each tier states what it is for and, just as plainly, what it is not for.
Serving open weight models, internal chatbots, document Q&A and retrieval. The tier most enterprises actually need.
Inference plus periodic fine tuning capacity for adapting open models on your own data. Explicitly not a training cluster.
The regulated tier. Sovereign inference with the security and data residency wrap for financial services, healthcare and critical infrastructure.
The founding team built validated reference architectures the first time the industry did this, in the converged infrastructure era. The discipline is the same: published scope, honest fit, no surprises at deployment.
Questions
Start by characterising the workload, not by picking hardware. Profile which models you will serve, how many concurrent users, what data the system retrieves over and what latency is acceptable, then let those numbers set the accelerator class, memory and storage. This is not a contrarian method: even the large infrastructure vendors run proving grounds whose whole purpose is to profile the workload before committing, which tells you sizing from a budget or a bundle overshoots. Performance per watt belongs in the same calculation, because sustained power draw is a real running cost that a headline hardware price hides.
Overwhelmingly inference and retrieval: serving open weight models, internal chatbots and assistants, document processing and question answering over internal data, plus established machine learning workloads. Almost nobody trains foundation models, and infrastructure sized for training is the wrong buy for an inference estate.
Almost certainly not, and the distinction matters commercially. Most enterprise value comes from serving existing models over your own data with retrieval, which needs a fraction of the infrastructure that training does. Where adaptation is genuinely needed, periodic fine tuning of an open model covers most cases, and that is a capacity question, not a cluster question.
It depends on volume, data constraints and utilisation, and the honest answer changes as usage grows. Per token APIs win at low and unpredictable volume. Owned infrastructure starts winning when volume is high and steady, or when data cannot leave your estate. We model the crossover with your numbers, and if the numbers say stay on the API, that is what we tell you.
The vendor sizing conversation starts with how many racks, because capacity is what the vendor sells. Ours starts with what you need to run, because advice is what we sell. We spent years on the vendor side building sizing quotes, so we know exactly where the padding goes, and we are not paid by the rack.
One sizing conversation, no obligation to buy anything from us afterwards, and a straight answer on what your workload genuinely needs. Not sure the numbers stack up yet? Run the AI Inference Cost Calculator first.
Book a sizing conversation