NewPre-deployment SLO estimation is now in preview —see it in action

For Enterprise AI Teams

Take control of
your AI economics

Improve performance at scale, cut your cost per token, and get full token-level visibility — without turning your AI team into an infrastructure team.

nexus · governor

NR-NEXUS Governor — control dashboard

Replace with product screenshot

cost / 1M tokens−73%
Trusted by
QualcommAMDFidelityCirrascaleMicrosoft

Why NR-NEXUS

Built to put you in control.

The economics, performance, and governance of production inference — in one operating layer.

01

Cost you can explain

Token-level cost, utilization, and SLO compliance by model, team, and tenant — reporting your CFO can read.

02

Predictable performance

SLO-backed latency and throughput, enforced before and during production. No guesswork at scale.

03

No vendor lock-in

One control plane across GPUs, XPUs, clouds, and on-prem. Swap hardware and engines without a rebuild.

04

Production governance

Tenant isolation, quotas, audit logs, and lifecycle workflows — governed inference from day one.

One layer

Nine systems, unified.

Engines, routing, caching, autoscaling, observability, tenancy, lifecycle — NR-NEXUS runs them as one governed layer, not nine tools you maintain.

vLLMSGLangTensorRT-LLMKV-cacheRoutingAutoscalingObservabilityTenant quotasDevOps on-call
NR-NEXUSone operating layer

Performance vs vLLM

Quantified. Not claimed.

Throughput
4.2×
vLLM1.0×
NR-NEXUS4.2×
Time to first token
vLLM1.0×
NR-NEXUS7.0×
Serving capacity / yr
167M
Baseline44M
NR-NEXUS167M

Pre-deployment estimation

Know before you deploy.

Set your model, workload, and SLO targets. NR-NEXUS projects whether you'll clear every target — throughput, latency, TTFT, GPU allocation — before a single token goes live.

Run a benchmark analysis
nexus · estimatorestimating
DeepSeek V310k users3 SLO targets
Time to first tokenclears
target ≤ 250 ms
Throughputclears
target ≥ 4,000 tok/s
p99 latencyclears
target ≤ 900 ms
projected allocation17 GPUs
meets all SLOs

Architecture

One layer. Three jobs.

NR-NEXUS sits between your models and your hardware — governing, orchestrating, and executing every token.

nexus · governor

Governor — control dashboard

Screenshot: NR-NEXUS Governor UI

Proof

From 64 GPUs to 17.

A production GenAI team ran one workload across 64 GPUs on a hand-built stack. The same workload now runs on 17 — with better throughput and lower latency.

Governor in action — demo coming soon

DIY stack64
NR-NEXUS17
73% fewer GPUs for the same workload
~$1.4Mannual savings
3.8×serving capacity
We stopped managing inference and started shipping product. The cost reporting alone paid for itself.
HoHead of AI PlatformProduction GenAI team

Models

Open models. Open choice.

Deploy the models your teams want — and swap without re-architecting.

Deployment

Three ways to run NR-NEXUS.

One spectrum from zero-ops to full ownership. Start where you are; move when you're ready.

01

Serverless API

Pay per token, zero infrastructure to manage. Start in minutes — ideal for discovery, prototyping, and early production.

controlPer-token
02

Dedicated EndpointPopular

Reserved capacity with full SLO controls, observability, and tenant governance. Best for production workloads that need guaranteed performance.

controlReserved
03

Annual License

A full token factory on your own infrastructure. Complete control over capacity, policy, and economics. Best for inference at scale.

controlAnnual

Ready to take control?

One model. One workload. One week. See the results on your own infrastructure.