The platform underneath your production AI
Private LLM hosting, GPU clusters, and retrieval pipelines. We run the stack so your team can spend its time shipping models instead of babysitting them.
The AI Stack, Fully Managed
From silicon to inference endpoint—every layer engineered for performance, security, and cost control.
GPU Clusters
Dedicated NVIDIA H100/A100 compute fabrics with high-speed interconnects, sized for training and high-throughput inference.
Private LLM Hosting
Deploy open-weight and fine-tuned models inside your VPC. Full data sovereignty, zero vendor lock-in.
RAG & Vector Stores
Production-ready retrieval pipelines with managed vector databases, embeddings, and ingestion workflows.
Low-Latency Inference
Edge-aware routing, autoscaling, and KV-cache optimizations to keep token latency predictable under load.
Secure by Design
Tenant isolation, prompt/PII filtering, audit logs, and SOC 2-aligned controls from day one.
FinOps for AI
GPU utilization dashboards, per-team chargeback, and model right-sizing to control AI spend.
Ready to run your own AI?
Let our engineers architect a secure, scalable AI platform tuned to your workloads.
Schedule a ConsultationFrequently Asked Questions
What AI infrastructure does VegaNext provide?
VegaNext runs dedicated GPU clusters (NVIDIA H100/A100), private LLM hosting inside your VPC, and production-ready RAG and vector-store pipelines, so your team can focus on models instead of the platform underneath them.
Can I keep full control of my data and models?
Yes. Private LLM hosting deploys open-weight and fine-tuned models inside your own VPC, giving you full data sovereignty with no vendor lock-in.
What inference latency should I expect?
VegaNext's platform targets under 50ms inference latency through edge-aware routing, autoscaling, and KV-cache optimizations, backed by 99.95% platform uptime.
How does VegaNext keep AI infrastructure secure and cost-controlled?
Security comes from tenant isolation, prompt/PII filtering, audit logs, and SOC 2-aligned controls. Cost comes from GPU utilization dashboards, per-team chargeback, and model right-sizing — what we call FinOps for AI.