The product / InferenceOps
Everything your inference stack needs to run in production.
Move from container to production endpoint with infrastructure controls designed specifically for GPU inference.
Book a meetingGPU INFERENCE / OVERVIEW
Inference overview
Example interface and sample values from the company context. Not live telemetry or benchmark results.
Deploy
From container to production endpoint.
Configure runtimes, GPU resources, replicas, health policies, and deployment settings through a consistent workflow.
- Container-based workloads
- Runtime & GPU configuration
- Model versions & health checks
Scale
Scale with inference demand.
Adjust GPU capacity using workload signals such as request volume, concurrency, latency, and utilization.
- Queue depth & concurrency
- GPU-aware placement
- Replica limits & cooldowns
Observe
See what your models and GPUs are doing.
Monitor inference latency, throughput, queue time, endpoint health, GPU utilization, GPU memory, and workload efficiency.
- Latency & token throughput
- GPU utilization & memory
- Endpoint health & queue time
Optimize
Understand the cost behind every workload.
Break down GPU spend by model, endpoint, cluster, or environment and identify infrastructure that is underutilized.
- Endpoint-level cost visibility
- Idle capacity & utilization
- Runtime configuration
Route
Give every request a clear direction.
Direct inference traffic based on health, capacity, policy, and performance.
- Endpoint health
- Model versions & regions
- Capacity & traffic policies
Control
One operational layer. Every workload.
Operate models, endpoints, clusters, and infrastructure from one control plane.
- Multi-environment operations
- Infrastructure visibility
- Platform team control
Enterprise Search
The inference layer behind retrieval.
Deploy dedicated embedding and reranking models used in RAG and search systems. InferGrid focuses on the GPU operations behind these workloads: deployment, scaling, observability, and cost visibility.
- Embedding model endpoints
- Reranking workloads
- GPU capacity & workload telemetry
The next step
Your models are ready.
Your infrastructure should be too.
Standardize how your team deploys and operates AI inference.
Book a meeting