The control plane for production GPU inference.
Deploy, scale, observe, and optimize AI inference workloads across GPU infrastructure from one unified platform.
Book a meeting01 / The operational layer
Models are only
the beginning.
Running inference in production means operating an entire stack. Scheduling. Scaling. Routing. Telemetry. Cost.
InferGrid brings these workflows into one GPU-aware control plane, so your team can focus on the models and applications that matter.
Uncover our approachGPU INFERENCE / OVERVIEW
Inference overview
Example interface and sample values from the company context. Not live telemetry or benchmark results.
ARCHITECTURE & COMPATIBILITY TARGETS
Kubernetes
NVIDIA
Docker
PyTorch
OpenTelemetryTechnologies referenced in the product architecture. Confirm supported configurations with the team; no partnership is implied.
02 / Inference first
One platform.
The whole lifecycle.
Infrastructure controls designed for the realities of GPU inference, from deployment to workload economics.
Deploy
Configure runtimes, GPU resources, replicas, health policies, and deployment settings through a consistent workflow.
Explore deployScale
Adjust GPU capacity using workload signals such as request volume, concurrency, latency, and utilization.
Explore scaleObserve
Monitor inference latency, throughput, queue time, endpoint health, GPU utilization, GPU memory, and workload efficiency.
Explore observeOptimize
Break down GPU spend by model, endpoint, cluster, or environment and identify infrastructure that is underutilized.
Explore optimizeRoute
Direct inference traffic based on health, capacity, policy, and performance.
Explore routeControl
Operate models, endpoints, clusters, and infrastructure from one control plane.
Explore control03 / Built around your stack
Build on GPUs.
Operate with InferGrid.
Standardize how teams run inference across Kubernetes, cloud GPUs, and private infrastructure.
One operational layer between your AI applications and accelerated compute. Keep visibility and control over how models are served.
Explore the architectureCONCEPTUAL ARCHITECTURE / CLOUD · KUBERNETES · PRIVATE
04 / Beyond one kind of model
Every workload.
A common foundation.
Production inference infrastructure for the models behind modern AI applications.
LLMs & AI agents
Model endpoints for generative AI, agents, and copilots.
02Vision & multimodal
GPU-accelerated detection, classification, and multimodal workloads.
03Speech & voice
Inference operations for speech-to-text, text-to-speech, and voice agents.
04Embeddings & reranking
Dedicated inference infrastructure for RAG and search systems.
05 / Infrastructure, considered
Define how your workload
should behave.
Keep the operations in view.
Give AI developers consistent deployment workflows while platform teams retain policy, visibility, and operational control.
See the workflow06 / Inference field notes
Know the layer
behind the model.
All resourcesQuestions / Answers
A clearer view
of InferenceOps.
What is InferGrid?
InferGrid is a GPU InferenceOps platform for deploying and operating production AI inference workloads.
Does InferGrid train models?
Training is not the primary focus. InferGrid is centered on production inference and model serving.
Which workloads is InferGrid designed for?
LLMs, AI agents, computer vision, speech, embeddings, rerankers, multimodal models, and other GPU-accelerated inference workloads.
Does InferGrid replace Kubernetes?
Not necessarily. InferGrid can provide higher-level inference abstractions while Kubernetes remains part of the underlying orchestration layer.
The next step
Your models are ready.
Your infrastructure should be too.
Standardize how your team deploys and operates AI inference.
Book a meeting