DEC 12, 2024
How We Achieved Sub-4ms P99 Latency Across 47 Global PoPs
A deep dive into our neural mesh routing algorithm, CUDA kernel optimizations, and the surprising role of TCP BBR in cutting tail latency by 61%.
READ ARTICLE →NEXUS is the unified AI development platform that transforms how engineering teams build, deploy, and scale machine learning infrastructure. Ship faster. Think bigger. Scale infinitely.
NEXUS emerged from a simple frustration: building production AI systems was unnecessarily hard. We spent three years embedded inside hyperscale ML teams at Fortune 500 companies, understanding every bottleneck, every failure point, every late-night incident.
Today, NEXUS powers 50,000+ engineering teams across 120 countries. We've processed over 2.4 trillion inference requests and helped teams reduce deployment cycles from weeks to hours. Our neural mesh architecture ensures your models stay fast, reliable, and observable at any scale.
We believe AI infrastructure should be invisible — so your team can focus on what matters: building exceptional products that transform industries.
Adaptive compute allocation
92% EFFICIENCYP99: 4.2ms inference
99.9% SLA47 edge locations worldwide
87MS AVG GLOBALSub-5ms latency inference across 47 global PoPs. Automatic batching, quantization, and hardware-aware optimization. Supports PyTorch, JAX, ONNX, and TensorRT out of the box.
COREVersion-controlled model storage with lineage tracking, A/B comparison, and automated performance regression detection. One-click rollback and shadow deployment support.
MLOpsReal-time drift detection, feature importance tracking, and prediction quality monitoring. Automated alerting with root-cause analysis powered by anomaly detection models.
MONITORINGDeclarative ML pipelines with DAG visualization, incremental retraining triggers, and data freshness guarantees. Integrates with Airflow, Prefect, and Kubeflow natively.
WORKFLOWSOC 2 Type II certified. Zero-trust networking, field-level encryption, RBAC with attribute policies, audit logging, and GDPR/CCPA compliance tooling built-in.
SECURITYKubernetes-native autoscaling with predictive load forecasting. Spot instance orchestration reduces GPU costs by up to 73%. Supports multi-cloud and on-premise hybrid deployments.
INFRANEXUS handles the entire deployment lifecycle — from model serialization and hardware-specific optimization to global distribution and health monitoring. No Kubernetes expertise required.
Our intelligent deployment engine automatically detects your model architecture, applies the optimal inference backend (TensorRT, ONNX Runtime, or custom CUDA kernels), and routes traffic based on latency and load.
"NEXUS reduced our model deployment time from 3 weeks to 4 hours. The observability suite caught a data drift issue that would have cost us millions in bad predictions. It's fundamentally changed how we operate."
"We evaluated every MLOps platform on the market. NEXUS was the only one that could handle our 50,000 requests/second peak load without a single dropped prediction. The auto-scaling fabric is genuinely magical."
"As a regulated financial institution, security was non-negotiable. NEXUS's zero-trust architecture and audit logging gave our compliance team exactly what they needed. We were SOC 2 certified 40% faster."
A deep dive into our neural mesh routing algorithm, CUDA kernel optimizations, and the surprising role of TCP BBR in cutting tail latency by 61%.
READ ARTICLE →Traditional monitoring misses 73% of production ML failures. We built a multivariate drift detector that catches the ones that matter — before your users notice.
READ ARTICLE →We analyzed 200 teams who built their own ML platforms. The average team spends 40% of their AI engineering time on infrastructure. Here's how to get that time back.
READ ARTICLE →Join 50,000+ engineering teams already building on NEXUS. Start free, scale to billions of inferences.