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Enterprise Cloud AI
Architecture, Infrastructure
& Migration

Design, provision, and scale dedicated vector databases, GPU inference clusters, and containerized AI microservices inside your private AWS, GCP, or Azure cloud account.

Schedule an Architecture Consultation › Review Cloud Frameworks ›

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100% Private Cloud Ownership: Infrastructure deployed directly inside your corporate cloud tenant.

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Optimized GPU/Compute Costs: Implement auto-scaling and spot instances to minimize cloud infrastructure bills.

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Resilient Microservice Design: Containerized Docker and Kubernetes deployments engineered for high availability.

Problem & Solution

AI Infrastructure Requires Purpose-Built Cloud Engineering

Running production-grade AI systems on generic web hosting leads to high latency, memory bottlenecks, and uncontrolled cloud compute expenses.

The Problem

Vector databases and GPU inference clusters have unique networking, memory, and scaling requirements that differ from standard web applications.

The Solution

We architect and manage dedicated, auto-scaling cloud environments on AWS, Google Cloud, or Azure optimized specifically for AI workloads.

Core Capabilities

Private Vector Database Deployment

Configure high-availability vector database clusters (Qdrant, Pinecone, pgvector) with automated snapshots and multi-region replication.

GPU Cluster Setup & Auto-Scaling

Provision dedicated GPU instances (NVIDIA H100/A10G) configured to scale compute nodes dynamically based on real-time traffic demand.

Containerized Deployment Pipelines

Package AI models and agent frameworks into standardized Docker containers managed by Kubernetes or serverless runners.

Cloud Cost Optimization & Caching

Implement semantic caching layers (GPTCache, Redis) to serve repeated queries instantly without paying for duplicate model inference.

150%

Reduction in Manual Workload

60%

Faster Decision-Making

Improved Workflow Efficiency

40%

Task Automation Rate

Get a Free Demo, Audit & Leads ›

Technical Specifications

No Component Cloud Architecture Standard

01 Supported Cloud Provider Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure

02 Containerization Docker, Kubernetes (EKS / GKE / AKS), Docker Compose

03 Interface Servers vLLM, Triton Inference Server, Ollama, TGI

04 Caching Layers Redis, Momento, Semantic Vector Cache

Implementation Process

A 4-week path from corpus ingestion to production API deployment.

WEEK 1 01

Architecture

Review current cloud architecture, compute requirements, and security configurations.

WEEKS 2 02

Access Roles

Provision private VPCs, IAM access roles, and database storage clusters.

WEEK 3 03

Deployments

Deploy containerized microservices and configure automated CI/CD deployment pipelines.

WEEK 4 04

Evaluation

Stress-test auto-scaling triggers, configure semantic caching, and hand over infrastructure access.

01 WEEK 1

Architecture

Review current cloud architecture, compute requirements, and security configurations.

02 WEEKS 2

Access Roles

Provision private VPCs, IAM access roles, and database storage clusters.

03 WEEK 3

Deployments

Deploy containerized microservices and configure automated CI/CD deployment pipelines.

04 WEEK 4

Evaluation

Stress-test auto-scaling triggers, configure semantic caching, and hand over infrastructure access.

Your Success Story Starts Here
Let’s Begin!

Frequently Asked Questions

Got questions? We've answered the most common ones about working with RixDigi — from services to timelines to support.

Yes. We build directly inside your AWS, GCP, or Azure organization using Infrastructure as Code (Terraform) so that your team maintains full ownership and billing control.