PRODUCTION AI PLATFORMS
AI workloads that hold under load.
Scalable, secure and cost-efficient infrastructure for AI (inference platforms, GPU scheduling, and ML pipelines) on a production-grade Kubernetes and AWS foundation built to run, not just to demo.
ClientRequest
APIIngress
RouterDispatch
Auto Scale Up
GPU Pool
GPU
GPU
GPU
GPU
GPU
GPU
GPU
GPU
Scale to Zero
Inference
Response
Response
Queue Depth
312
Active GPUs
16/64
Utilization
78%
Cost Efficiency
$3.21/hr
The Problem
AI infra is easy to prototype and hard to productionize. GPUs sit idle and expensive, inference falls over under real traffic, pipelines are fragile, and underneath it all, cloud estates that grew account-by-account are fragile and hard to govern.
What We Do
- Inference platforms built for scale and latency
- GPU scheduling that maximizes utilization and minimizes idle cost
- Reproducible, reliable ML pipelines
- Production-grade EKS with autoscaling and zero-downtime rollouts
- Well-architected AWS foundations, landing zones and least-privilege baselines
How It Works
- 1ArchitectDesign the platform around the workload
- 2SchedulePlace GPU work to cut idle spend
- 3ServeRun inference that holds under real traffic
- 4OptimizeTune cost and latency continuously
Outcomes
- Scales on demand, scales to zero idle
- Cost-efficient GPU use
- Production-stable inference and zero-downtime rollouts
Go deeper on the foundation this platform runs on: AWS cloud foundations
Platforms are one half of the job. how our forward-deployed engineers take AI from pilot to production
Put your AI workloads on solid ground.
Talk to an engineer