Product / Where servescale.ai deploys

Deploy inference inside your perimeter.

servescale.ai is designed to run in environments your organization controls, with your models, data, state, identity, and policy inside the approved boundary. Deploy it on supported infrastructure you own, rent, reserve, or operate through your cloud account.

Request demo How it is consumed

servescale.ai

one operating model one governed service policy · optimization · visibility

Your deployment environments

SpecializedNeocloudaccelerator capacity in your tenant

CloudYour VPCyour account, network, and policy

HostedColo + hostingowned or dedicated provider capacity

PrivateDatacenter + cloudenterprise-controlled infrastructure

DistributedEdgeinference close to demand and data

Your models. Your data. Your infrastructure boundary.

Deployment environments

One platform across the environments your organization operates.

Put inference where data, users, machines, capacity, and policy require it. Use one environment or combine several without changing the service applications consume.

Neocloud

Run on validated accelerator fleets supplied by a specialist provider. Keep the model-service layer, deployment policy, identity, and operational evidence under your control while using high-density capacity without building the facility yourself.

Specialized capacity · customer-controlled service

Public cloud - inside your VPC

Deploy in your organization’s cloud account and private network on supported instances. Continue using the procurement, identity, observability, security, and governance systems already attached to that environment.

Traditional cloud · your account and network

Colocation or hosting facility

Operate customer-owned equipment in a colo, or use dedicated capacity from a hosting provider. Control the software stack, data path, model policy, and service boundary without needing to own the building.

Your gear or dedicated capacity · external facility

Private cloud, datacenter

Run inside enterprise-owned facilities, private clouds, secure networks, or dedicated infrastructure. Keep models, proprietary context, runtime state, keys, cache, and telemetry inside the operational boundary you define.

Maximum local authority · existing infrastructure

Edge and remote locations

Place inference near users, machines, regulated data, or disconnected operations. Reduce round trips, preserve local data handling, and make deliberate use of constrained power and compute where latency and efficiency matter most.

Low latency · local data · power-aware operation

Hybrid by design

Infrastructure can change. The governed inference service stays consistent.

Capacity does not have to come from one supplier or live in one place. servescale.ai applies a common service and policy model across validated deployment targets, so IT can place workloads where data, performance, resilience, power, and economics are best served.

Service

Stable interfaces

Applications and agents use governed service endpoints instead of binding directly to a specific accelerator, runtime, or location.

Policy

Your operating rules

Identity, model access, data handling, service objectives, change control, and audit remain aligned with your operating model.

Placement

Environment-aware decisions

Work can be placed against validated model and runtime support, available state, topology, latency, power, capacity, and economics.

Boundary

Data where it belongs

Models, proprietary context, cache, and telemetry can remain in the approved environment instead of crossing into a public model API by default.

Request demo Consumption model Integration model