A large enterprise does not run one model — it runs dozens, across teams that each stood up their own stack. We engineer multi-model serving with a routing layer, shared autoscaling capacity, and cost-per-token observability per team, so the whole fleet runs on one SLA-backed platform instead of a cluster per project.
In a multi-function enterprise, inference does not arrive as one workload. Different teams adopt AI at different times, each picking its own model and standing up its own serving stack, until there are dozens of models behind dozens of endpoints. Most of those clusters sit underutilized — a GPU reserved for one team's use case is idle while another team is queueing — and nobody can say what any of it costs or whether it is holding its latency.
The fix is consolidation without taking choice away. We build a multi-model platform: a routing layer that sends each request to the right model, shared capacity that several teams draw from behind one SLA-backed endpoint, autoscaling across the pooled fleet, and cost-per-token observability tagged to whoever generated the request. Teams keep their models; the enterprise gets one platform it can run, account for, and right-size.
A serving platform engineered to consolidate the fleet — routed, pooled, and accounted for across the whole enterprise.
Value concentrates wherever AI has spread across teams faster than the platform underneath it has consolidated:
A dedicated cluster per use case leaves most GPUs idle most of the time. We consolidate onto a shared, multi-model serving platform with a routing layer that sends each request to the right model, and shared capacity that several teams draw from behind a single SLA-backed endpoint. Continuous batching keeps utilization high across the mixed workload, so the fleet runs on far less hardware than a model-per-cluster sprawl would need.
Every request is tagged to a team, model, and use case, so cost per token rolls up to whoever generated it — turning a single shared bill into per-team, per-model accountability and chargeback. The same telemetry covers latency and capacity across the platform, so a central team can see who is driving spend, where headroom is running out, and which workloads to right-size, all from one view rather than a patchwork of per-project dashboards.
Bring the models and teams running their own stacks today and the cost you cannot currently attribute. In thirty minutes we will show how a routed, shared platform consolidates them — and how we will give each team cost-per-token visibility. Response inside 24 hours.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: