Requirement and fit review
What you are actually trying to do, and whether it needs a model at all. A retrieval problem dressed up as an AI problem is common, and cheaper to solve honestly.
Partner-powered · Operated by CypherMax
Ship AI features without building an ML platform first.
Managed access to leading models and GPU capacity through our cloud partners, wired into your application with access control, data handling and cost limits already in place.
Fit
This is for you if
- You have an AI feature on the roadmap and no infrastructure plan behind it.
- Your prototype works on a developer's personal API key and now needs to go to production.
- You need GPU capacity for a specific workload and do not want to own hardware for it.
- Your customers or regulator will ask where their data goes when it reaches a model, and you need a defensible answer.
- Someone ran an experiment last month and the bill was a surprise.
Scope
What is included
Model and platform selection
A recommendation across the models available through our partners, weighed on capability, regional availability, data-residency terms, and cost per unit of real usage rather than per benchmark.
Provisioning
Model endpoint or GPU capacity provisioned under a proper account structure, with quotas and limits set from day one instead of after the first incident.
Integration
Connecting the endpoint to your application: authentication, key management, retry and timeout handling, and a fallback path for when the model endpoint is unavailable.
Access control and secrets handling
Keys held in a managed secret store, scoped per environment, rotatable, and never in a repository. This sounds basic and it is the most common failure we see.
Data handling review
What data leaves your environment, where it is processed, which region it is processed in, and what the platform's terms say about retention and training use. Written down, so you can hand it to a customer who asks.
Cost guardrails
Budgets, quotas and anomaly alerting on AI spend specifically. Token and GPU costs scale with usage in a way that surprises teams used to fixed infrastructure — see FinOps.
Monitoring
Latency, error rate, throughput and spend, reported alongside the rest of your infrastructure rather than in a separate tool nobody opens.
Models
Models we can put in front of you
You should not have to pick a model before you understand the trade-offs. Here is what we can provision, and which platform it runs on.
Which model suits you is a question about your use case, your budget and where your data is allowed to go — not about which name you have heard most often. We will work through it with you before anything is provisioned.
Platforms
Platforms and GPU capacity
We provision managed AI services and GPU capacity from the cloud platforms we collaborate with: Amazon Web Services, Google Cloud, Alibaba Cloud, Tencent Cloud, Huawei Cloud and BytePlus.
Boundaries
What this is not
The AI market rewards vagueness. We would rather be specific.
- We do not build these models. Qwen, Claude, Gemini, Seedance and every other model on the wall belong to the companies that made them. What we own is the infrastructure, integration, controls and cost around them.
- We are not a data science consultancy. We do not build your model strategy, label your data, or write your evaluation methodology.
- We do not promise a specific model's capability. Model behaviour, pricing and availability are set by the platform that operates it, and they change without reference to us.
- This is not a way to avoid a data-protection decision. We will document what happens to your data, but the decision about whether that is acceptable is yours to make.
FAQ
AIaaS FAQ
Often yes, and it depends on the platform, the specific model, and the region it is served from. Regional availability for AI services changes frequently, so this is a question we answer against current documentation for your chosen platform rather than from memory.
That is governed by the terms of whichever platform serves the model, and the answer can differ between two platforms serving the same model. We will read those terms with you and document the answer before you build on it, because it is usually the question your customers will ask first.
Yes, and it is often the right architecture — a cheaper model for routine work, a stronger one for the cases that need it. Because the models on our wall run across different partner platforms, this does mean more than one account and more than one set of terms, which we will set up and manage.
No. Managed endpoints suit intermittent use better than reserved GPU capacity does, and part of the selection work is matching the commercial model to how often you will actually use it.
Yes, and it is included rather than sold separately. Quotas, budgets and anomaly alerting are set up during provisioning.
We build a fallback path during integration — degrade the feature gracefully, queue the request, or route to an alternate endpoint, depending on what your application can tolerate. Deciding this before launch is much easier than deciding it at 3am.
Related