Agentic AI
Run multi-step agents across models, tools, and enterprise context under one policy and service layer.
ExploreProduct / How servescale.ai helps
Operate private inference on your infrastructure.
servescale.ai gives IT one place to publish models, apply policy, and operate inference across cloud, colo, and on-prem systems. Applications keep a standard interface while the platform manages the runtimes and capacity behind it.
Enterprise demand
Applicationsstandard APIs
Agentsmulti-model workflows
Teamsgoverned access
servescale.ai
private inference platform
analyze, serve, scale, optimize
Controlled supply
Models + datayour differentiated assets
Compute + statecloud · colo · on-prem
Policy + SLOsyour operating rules
one governed model service across your heterogenous distributed infrastructure
Use cases
What servescale.ai can be used for.
Start with the operating need. Keep the same governed model service underneath.
Enterprise Model as a Service
Publish approved models behind stable APIs with shared capacity, tenant controls, and predictable service levels.
ExploreSovereign AI
Operate inference within regional, organizational, and infrastructure boundaries you control.
ExploreData sovereignty
Keep proprietary models, enterprise context, cache state, and operational data inside the required perimeter.
ExploreWhat changes
servescale.ai enables IT to provide inference as a service to the development and business teams.
The service consumed is separated from the models, runtimes, and hardware used to deliver it. The implementation, deployment, and configuration can change without forcing applications to adapt. Consumption Integration
Publish a governed model service
Expose approved models through OpenAI-compatible APIs and MCP. Define who can use each model, the service level it must meet, and the capacity it may consume.
- Model catalog and lifecycle
- Stable interfaces for applications and agents
- Tenant policy, quotas, and SLOs
Keep models and context inside your boundary
Run proprietary models, enterprise context, cache state, and policy in environments your organization controls. Identity, audit, and change management remain part of the existing operating model.
- Local models, data, context, and cache
- Existing identity and authorization
- Auditable deployment and policy changes
Use the fleet as a shared pool
Place workloads across supported GPUs, CPUs, NPUs, and runtimes according to model fit, available state, service requirements, power, and cost.
- Heterogeneous accelerators and runtimes
- Reuse of warm weights and KV cache
- Placement against measured service objectives
Inside the platform
A single control path from API to hardware.
Policy and telemetry follow a request from the API to the hardware. Operators do not have to reconcile separate systems for routing, cache, runtime selection, and resource placement.
Service plane
Publish approved models, authenticate callers, apply tenant policy, and report service health through interfaces application teams already use.
Model preparation
Profile a model and its workload, then select or create an execution form suited to the available hardware and required quality.
Execution control
Select the runtime, place the request, route traffic, manage cache and context state, and isolate tenants across the serving fleet.
Operations and economics
Measure latency, throughput, utilization, failures, power, and cost. Use those observations to update placement and configuration decisions.
Virtualization / Fleet economics
Pack more inference into the fleet you already own.
Inference demand comes in bursts. Hardware does not. servescale.ai virtualizes execution, memory, and model state so workloads can move with demand: packed onto GPUs, spilled to CPU when the model allows it, or parked when traffic goes quiet.
Demand
Inference arrives in bursts. Volume is not constant.
real-time · steady · batch
servescale.ai
inference virtualized
share · pack · spill · park · move
Capacity
Heterogeneous fleet works as one pool.
latest GPUs · older GPUs · CPUs · other acceleratorsPack the fleet
Combine models with complementary demand instead of reserving an accelerator for every model.
Follow demand
Move execution and state as traffic, latency targets, memory pressure, power, and cost change.
Protect the investment
Keep current and older-generation GPUs productive. Save premium hardware for workloads that need it.
Raise return per watt
Spill eligible work to CPU, park cold models, and avoid powering idle capacity just in case.
The operating shift
Replace separate model stacks with a shared operating layer.
Move fromMove to
Separate stacks for every modelOne governed model service
Hardware islands and idle capacityA shared heterogeneous resource fabric
Infrastructure-first schedulingModel-, state-, and economics-aware decisions
Fragmented cache and runtime stateA coordinated context and cache fabric
Usage that becomes a surprisePolicy-controlled performance, power, and cost
Fits your operating model
Operate inference without replacing the rest of your platform.
Use the same service and policy model across cloud, colo, and on-prem infrastructure.
Kubernetes, CI/CD, observability, identity, and governance stay in place. servescale.ai adds the model, runtime, state, placement, and economics decisions those systems do not make.