How it works / The operational layer

From GPU cluster to production endpoint.

A unified workflow for the inference lifecycle. Connect infrastructure, define your workloads, and operate with visibility.

The inference lifecycle

Connect.
Configure.
Operate.

Define how an inference workload should behave, then use performance and cost signals to iterate.

AI applications REQUESTS

InferGrid Gateway

InferGridCONTROL PLANE

DeployScheduleScaleRouteObserve

Inference runtimes CONTAINERS

GPUGPUGPUGPU

CONCEPTUAL ARCHITECTURE / CLOUD · KUBERNETES · PRIVATE

01
Connect infrastructure
Start with Kubernetes, cloud GPUs, or private infrastructure. Define the environment in which your inference workloads will run.
02
Choose your runtime
Select a model-serving container and configure its runtime requirements. Keep your serving stack aligned with your models.
03
Configure GPU resources
Define GPU requirements, memory, replicas, and placement constraints around the workload.
04
Deploy an endpoint
Create a consistent deployment workflow with model versions, health policies, and runtime configuration.
05
Set scaling & routing policies
Align capacity with queue depth, concurrency, latency, and utilization. Define how inference traffic is routed.
06
Send production traffic
Connect your AI applications to model endpoints and operate the inference lifecycle from one place.
07
Observe performance
Track latency, throughput, endpoint health, GPU memory, and utilization in context.
08
Analyze cost & iterate
Understand endpoint and infrastructure costs. Use those signals to refine runtime and scaling configuration.

Developer experience

Give AI developers simple deployment workflows while platform teams retain visibility and control.

ILLUSTRATIVE CLI / CONCEPTUAL EXAMPLE

infergrid deploy \
  --name llama-3-8b-prod \
  --runtime vllm \
  --gpu h100 \
  --replicas 2

Example syntax from the company context; confirm current interfaces with the team.

The next step

Standardize how your team deploys and operates AI inference.