Ship AI models to
production, fast.
We design, deploy, and manage cloud-native AI infrastructure on AWS, GCP, and Azure — Kubernetes-orchestrated, GPU-optimised, and built for 99.99% uptime from day one.
Cloud & MLOps Stack
Cloud Providers
AWS, GCP, Azure —
or all three.
We're certified on all major cloud platforms and take a provider-agnostic approach to avoid lock-in.
AWS
Most popular- SageMaker
- EKS
- Lambda
- Bedrock
GCP
Best for AI/ML- Vertex AI
- GKE
- Cloud Run
- TPU v4
Azure
Enterprise pick- Azure ML
- AKS
- Functions
- OpenAI API
Multi
We recommend- Avoid lock-in
- Cost arbitrage
- DR
- Geo
What We Deploy & Manage
Full-stack AI cloud
engineering
Model Containerisation
Docker + ONNX-optimised containers for every model type — LLMs, CV models, embedding engines. Reproducible builds, GPU-aware scheduling, and auto-scaling replicas.
- Multi-stage Docker builds
- ONNX / TensorRT export
- GPU node pool management
Inference Serving at Scale
Triton Inference Server, vLLM, and Ray Serve for high-throughput, low-latency AI serving. Continuous batching, KV cache optimisation, and auto-scaling by queue depth.
- vLLM continuous batching
- Triton dynamic batching
- Request queue auto-scaling
MLOps & CI/CD Pipelines
Automated model retraining, evaluation, promotion, and rollback pipelines. GitHub Actions + ArgoCD + MLflow — every model change goes through a rigorous deployment gate.
- A/B model canary deploys
- Automated eval gating
- One-click rollback
Cost Optimisation
Spot / preemptible GPU instances, right-sizing recommendations, and idle-resource cleanup — we routinely cut cloud AI costs by 40–70% without sacrificing performance.
- Spot GPU auto-provisioning
- Right-size dashboards
- Reserved instance planning
Security & Compliance
VPC isolation, private endpoints, IAM least-privilege, secrets via Vault, and automated compliance scans. SOC 2, HIPAA, and GDPR-compatible architectures by default.
- Private VPC endpoints
- IAM + OIDC federation
- Automated SAST/DAST
Observability & Alerting
Prometheus + Grafana stacks for model latency, throughput, drift, and error rate. PagerDuty integration, SLO dashboards, and anomaly-based auto-scaling triggers.
- Model drift detection
- SLO / error budget tracking
- Multi-channel alerting
Managed Service
Always-On SLA
Managed Infrastructure
We own the infra.
You own the model.
When you ship with DGCrux, you get a dedicated cloud engineering team managing your infrastructure 24/7 — so your ML team focuses entirely on model quality, not on-call rotations.
Your AI model deserves
production-grade infra
Tell us your model, your traffic expectations, and your cloud preference. We'll architect and deploy it with full observability, auto-scaling, and a 99.99% SLA.