The Inference Operating System
for Production AI
Take control of production inference. One layer to orchestrate, optimize, and govern AI workloads — across any model, any GPU, any cloud.
How it works
From plan to production.
In four steps.
Choose your deployment, tune the knobs, deploy in one command, and watch your token factory run — all without touching infrastructure.
Choose your deployment
Select your active models and pick your primary SLO constraint. NR-NEXUS automatically schedules resources and schedules compute layers to match your targets.
Project capacity and cost
Tune your infrastructure levers. Slide the node count and toggle optimizations to see latency, throughput, and annual spend trade off in real time.
One command to production
Watch NR-NEXUS compile your deployment plan, configure engines, set up KV-aware routing, and initiate production traffic routing in real time.
Live production dashboard
Monitor token-level metrics. SLO compliance, cost, and latency update in real time as production traffic hits your newly optimized engines.
Why NR-NEXUS
One unified operating layer
Replace the fragmented tangle of serving engines, custom operators, and hand-rolled observability with a single production inference plane.
Intelligent routing
Every request finds its optimal path — engine selection, KV-aware routing, and disaggregation, out of the box.
Learn moreK8s-native orchestration
Deploy inference as Kubernetes-native workloads. No custom operators, no vendor lock-in.
AI-aware scaling
Scale to zero or to peak demand based on real-time workload signals — not static thresholds.
Observability
Token-level metrics, SLO dashboards, and per-tenant cost tracking built in from day one.
Lifecycle management
Canary rollouts, model versioning, and traffic shifting across model revisions without downtime.
Security
Tenant isolation, mTLS, audit logs, and RBAC — governed inference for regulated workloads.
Results
Proven in production
Real customer outcomes from NR-NEXUS deployments. Figures confirmed in press release — flag for stakeholder review before publish.
Models
Any open model. Any hardware.
Serve the models your teams want and swap without re-architecting.
Solutions
One platform. Two paths to production.
For Enterprise
Take control of your AI economics. Govern inference at scale with full cost visibility, SLO enforcement, and multi-model serving — without a dedicated infrastructure team.
Explore EnterpriseFor NeoClouds
Turn your GPU infrastructure into managed token factories. Monetize idle capacity, differentiate beyond raw compute, and deliver managed inference at hyperscaler margins.
Explore NeoCloudsSee it on your own workload.
One model. One week. Measure the cost and performance impact on your own infrastructure.