Cloud Consulting
Cloud Consulting & Cloud Consulting Services for Production AI
Through expert cloud consulting, Athena AI’s cloud consulting services help move AI and edge-vision systems from prototype to production with reliable, scalable, and cost-efficient infrastructure. We build the MLOps foundation, pipelines, monitoring, and deployment architecture that keep inference running where it works best — in the cloud, at the edge, or across both.
Trusted by teams building production vision systems
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This Isn’t A Cloud-Migration Practice
Plenty of firms will lift-and-shift your servers to the cloud. That’s not what this is. We work at the layer where AI actually succeeds or fails in production — the pipelines that move a model from a notebook to a live system, the infrastructure decisions that keep it fast and affordable, and the increasingly common question of which workloads belong in the cloud and which belong on a device at the scene.
Three things define the work, and the third is where we differ from every general cloud consultancy and single-cloud partner:
1. Get Models To Production — And Keep Them There
The MLOps layer: versioning, automated retraining, deployment, monitoring and drift detection. The engineering that turns a one-off model into a system that stays accurate and shippable. This is the gap most projects die in.
2. Control The Bill
GPU and cloud cost is where AI budgets quietly haemorrhage. Right-sizing, spot and committed-use pricing, autoscaling, scheduling, model optimisation, and removing per-inference cloud costs where the workload should run elsewhere.
3. Decide Where Each Workload Runs — Cloud, Edge, Or Hybrid
The decision most cloud consultancies skip. Training is usually a cloud job; real-time inference often belongs on a device. The common answer is hybrid: train in the cloud, infer at the edge, with only small events flowing back. Because we build the edge side too, we give you a decision grounded in real numbers — not a cloud-by-default reflex.
What You Actually Get
Outcome | What it means |
|---|---|
Models that reach production | A repeatable pipeline from training to deployment, with monitoring and retraining built in — not a model that lives in a notebook. |
