Analyze
Understand workload, model, and available infrastructure.
Product / How servescale.ai works
Decide how every inference request runs.
A control plane inside servescale.ai evaluates the model, request, policy, live state, and available capacity. It selects an execution path, places the work, and records the result for the next decision.
Optimize
Transform the workload/model to match the infrastructure.
Economiccontrol plane
Observe
Watch performance, learn, and adapt continuously.
Deploy +
scale
Use and optimize resources where available.
Optimized for your models on your infrastructure:
power, cost, performance
Inputs to a decision
Placement depends on the model, the request, and what is already running.
A GPU allocation alone is not enough information. servescale.ai evaluates execution requirements, reusable state, service policy, and current resource conditions before it commits capacity.
Model requirements
Architecture, memory footprint, context profile, precision, and measured execution behavior.
- Model form
- Token profile
- Quality boundary
Execution choices
Supported runtime, kernel, batching, parallelism, quantization, and concurrency settings.
- Kernel path
- Batch policy
- Parallelism
Reusable state
Loaded weights, KV cache, context, locality, and other work that should not be repeated.
- Warm weights
- Cache locality
- Context tier
Available capacity
Accelerators, CPU, memory, network, topology, pressure, availability, and the power envelope.
- Topology
- Memory pressure
- Power envelope
Service objective
Latency, throughput, priority, isolation, capacity, power, and cost requirements for the request.
- Service tier
- Cost ceiling
- Watts per token
Request path
What happens between the API call and the first token.
Each request passes through admission, execution planning, placement, and feedback. The path can change as policy, state, and capacity change.
Admit
Authenticate the caller and apply tenant policy, quota, priority, and service-level rules.
Inspect
Identify the model, request shape, context size, existing state, and quality requirements.
Prepare
Select the model artifact, runtime, kernel, batch policy, parallelism, and cache strategy.
Place
Choose capacity using topology, memory, locality, warm state, availability, power, and cost.
Run
Route and execute the request while enforcing isolation and observing the selected path.
Update
Record latency, throughput, utilization, failures, power, and cost for subsequent decisions.
Architecture
The control plane between service interfaces and Kubernetes.
Applications call a stable service. Kubernetes continues to manage infrastructure. servescale.ai handles the inference-specific work between them: model preparation, admission, routing, placement, runtime selection, cache, context, and recovery.
Requests + policy ↓Telemetry + outcomes ↑
Standard APIs + MCPApplications · agents · catalog · authorization · service objectives
Model intelligenceAnalyzer · optimizer · model forms · policy · workload objectives
Model-aware schedulerAdmission · placement · routing · priority · scaling · rebalancing
Execution fabricRuntimes · cache plane · context tiers · virtualization · isolation · recovery
Your infrastructureKubernetes · compute · storage · network · power · xPUs
While the system is running
Decisions change as demand, state, and capacity change.
Profile the workload
Measure the model and request mix. Validate runtime, kernel, model form, batching, and cache settings against the service objective.
Place the work
Choose an execution target using warm state, topology, memory pressure, accelerator fit, priority, power, and cost.
Protect each tenant
Virtualize models, state, and resources so capacity can be shared and rebalanced without crossing tenant or policy boundaries.
Update the next decision
Use measured demand, failures, latency, throughput, utilization, and power to revise configuration and placement.
Kubernetes
Manages infrastructure
Provides the durable infrastructure substrate and lifecycle primitives.
- Pods, nodes, deployments
- Networking and storage
- Coarse resource allocation
servescale.ai
Manages inference decisions
Adds the model-, state-, and economics-aware decisions inference requires.
- Models, requests, and execution phase
- Runtime, cache, and context state
- Performance, power, and cost objectives
ResultAn adaptive private inference service on the infrastructure you already operate.
Result
A private inference service that improves while it runs.
Models, runtimes, state, and heterogeneous infrastructure are managed through the same set of service and policy objectives.