GPU costs spiral without visibility
There's no per-team or per-model cost attribution. Idle GPUs burn budget while teams wait in queue for capacity that's already allocated but unused.
AI infrastructure without operational discipline is expensive and fragile
Most teams treat AI workloads differently from the rest of their stack. The result: runaway costs, blind spots, and production incidents that nobody saw coming.
AI workloads run without SLOs
Models deploy on ad-hoc infrastructure with no alerting, no capacity planning, and no runbook. When inference breaks, users notice before your team does.
Inference is a black box
Token costs, latency percentiles, throughput, and model drift go unmonitored. You can't optimize what you can't measure.
Agent runtimes lack guardrails
AI agents run without audit trails, versioned prompts, or safe rollout mechanisms. One bad deployment affects every user, with no way to trace what happened.
Teams reinvent solved problems
Engineering time goes to building bespoke infra for AI workloads (scheduling, serving, rollbacks) instead of applying patterns that already work for traditional services.
Production-grade AI infrastructure, from GPU to endpoint
We apply proven operational practices to the unique challenges of GPU scheduling, model lifecycle, and AI cost management.
GPU Orchestration & Scheduling
Multi-tenant GPU scheduling with bin-packing, preemption, and cost-aware placement. Your teams share GPU capacity efficiently, with per-namespace quotas and spot instance fallback.
LLM Inference Infrastructure
Production model serving with autoscaling, latency optimization, and A/B traffic splitting. Deploy new model versions with canary rollouts, not all-or-nothing switches.
Agent Runtime Management
Versioned prompt configurations, safe rollout mechanisms, and full reasoning traces for every agent action. Roll back a bad prompt version as easily as rolling back a container image.
AI Workload Observability
Token costs, inference latency, throughput, and model drift, all in your existing observability stack. Per-model and per-team dashboards with alerting on cost and performance thresholds.
Frequently asked questions
No. We integrate with your existing cluster infrastructure. GPU scheduling, model serving, and observability layers are added alongside your current workloads, not as a replacement.