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Infrastructure That
Powers AI Agencies

From client demos to production AI systems, IBEE gives agencies the GPU power, storage, networking, and security they need to deliver chatbots, agents, and RAG platforms faster.

Start building on IBEEExplore GPU infrastructure
AI agency infrastructure on IBEE

AI agencies don't fail on models. They fail on infrastructure.

01

AI agencies are expected to move from idea to demo quickly.

Clients often want a working AI chatbot, automation system, RAG app, or AI agent within days or weeks. But setting up servers, storage, APIs, deployment environments, firewalls, and backups for every project can slow the agency down before the actual AI work begins.

How IBEE handles it

Use IBEE Cloud VMs, GPU VMs, Object Storage, Load Balancers, and Managed Kubernetes to launch project environments quickly. Agencies can build prototypes, demos, and production apps on a consistent infrastructure foundation instead of rebuilding cloud setup for every client.

Cloud VMsGPU VMsObject StorageLoad BalancerManaged Kubernetes
Client projects need to launch fast illustration
02

One client needs a chatbot. Another needs automation. Another needs a private AI platform.

AI agencies rarely build the same thing twice. Some clients need customer-support chatbots, some need document search, some need AI agents, some need internal dashboards, and others need full AI SaaS platforms. Each workload can need different compute, storage, networking, and deployment choices.

How IBEE handles it

Run lightweight projects on Cloud VMs, heavier AI workloads on GPU VMs, containerized systems on Managed Kubernetes, and file-heavy workflows on S3-compatible Object Storage. IBEE gives agencies the flexibility to match infrastructure to each client's use case.

Cloud VMsGPU VMsManaged KubernetesObject StorageBlock Storage
Every client has a different AI workload illustration
03

Client AI systems need a clean place for documents, datasets, outputs, and backups.

Many AI agency projects involve client documents, PDFs, knowledge bases, embeddings, uploaded files, model outputs, generated media, logs, and backups. If these assets are stored across local machines, temporary disks, or scattered buckets, the project becomes difficult to manage and hand over.

How IBEE handles it

Use IBEE Object Storage to store client documents, datasets, model artifacts, generated outputs, files, exports, and backups. Since it is S3-compatible, agencies can use standard SDKs, CLIs, and tools across multiple client projects.

Object StorageS3-compatible APIsBackups & SnapshotsBlock StorageCloud VMs
AI data and RAG assets become messy illustration
04

A client demo is not the same as a production-ready AI system.

AI agencies can often build a working demo quickly. But clients also need stable hosting, secure access, deployment workflows, backups, API routing, environment secrets, and recovery planning. Without production infrastructure, the agency may struggle when the client asks to go live.

How IBEE handles it

Deploy client apps on Cloud VMs or Managed Kubernetes, place APIs behind Load Balancers, protect access with Firewalls, manage credentials with Secret Manager, and use Backups & Snapshots for recovery planning.

Cloud VMsManaged KubernetesLoad BalancerFirewallSecret ManagerBackups & Snapshots
Demos work, but production handoff is hard illustration
05

AI agencies often handle sensitive client data.

Client AI projects may involve business documents, internal knowledge bases, customer data, support tickets, sales records, training material, private files, or enterprise workflows. Clients want to know how data is stored, who can access it, how secrets are managed, and whether infrastructure is isolated.

How IBEE handles it

Use Firewalls to control network access, Secret Manager to store application secrets, API Tokens for controlled access, private networking for service isolation, and Backups & Snapshots for recovery planning.

FirewallSecret ManagerAPI TokensPrivate networkingBackups & Snapshots
Client data security becomes a blocker illustration
06

Unclear cloud costs can reduce profitability on fixed-price client projects.

Many AI agencies work on fixed project fees, retainers, or monthly support contracts. If GPU usage, storage, inference, background jobs, or hosting costs are not controlled, infrastructure can eat into project margins.

How IBEE handles it

Start with the infrastructure each client actually needs: Cloud VMs for apps, Object Storage for files, GPU VMs for AI workloads, Load Balancers for traffic, and Kubernetes for scaling. IBEE helps agencies avoid overbuilding early while keeping a clear path to production growth.

Cloud VMsGPU VMsObject StorageLoad BalancerManaged KubernetesAPI Tokens
Agency margins depend on infrastructure control illustration

40Gbps

server network fabric

Built for fast movement between compute, storage, and internal AI services.

S3compatible

client data storage

Store documents, datasets, generated outputs, backups, and project assets with standard APIs.

GPU+ VM

AI project compute

Run client AI workloads on GPU VMs and application backends on Cloud VMs.

Oneplatform

client delivery stack

Compute, storage, Kubernetes, load balancing, firewall, secrets, and backups together.

Before / After

From manual client setup to a reusable cloud foundation.

See how agencies move once they stop rebuilding cloud infra for every client and start delivering on a consistent foundation.

Before IBEE

Building client AI infrastructure manually

  • Every client project needs a fresh cloud setup before development can move fast
  • Client documents, embeddings, generated files, and backups are stored across scattered tools
  • AI demos are easy to build but harder to turn into secure production systems
  • Client workloads have different compute, storage, and deployment needs
  • Engineers spend time managing servers, secrets, firewalls, storage, and deployments
With IBEE

Delivering client AI projects on a reusable cloud foundation

  • Launch client environments on IBEE Cloud VMs, GPU VMs, or Managed Kubernetes
  • Store project files, documents, outputs, datasets, and backups in S3-compatible Object Storage
  • Deploy production APIs behind Load Balancers with Firewalls and Secret Manager
  • Match infrastructure to each client project without rebuilding the cloud layer every time
  • Use IBEE cloud primitives to deliver faster while keeping client workloads easier to manage
The Platform

One platform for the AI agency delivery lifecycle.

Build, deploy, secure, and support client AI projects using IBEE compute, storage, networking, Kubernetes, and security services.

01 / Compute

GPU VMs and Cloud VMs

Run AI workloads, backend APIs, chatbots, RAG apps, automation workers, dashboards, and client applications on flexible compute.

Use forAI · Apps
WorkloadsAgents · RAG · APIs
Scale pathVMs → Kubernetes
02 / Storage

S3-compatible Object Storage

Store client documents, datasets, model outputs, generated files, embeddings assets, backups, and project files using standard S3-compatible APIs.

Use forDocs · Outputs
APIS3-compatible
WorkflowsSDKs · CLI · Tools
03 / Kubernetes

Managed Kubernetes

Run containerized AI apps, agents, workers, queues, APIs, and multi-service client platforms.

04 / Delivery

Load Balancer

Place client-facing AI apps, APIs, dashboards, and automation services behind reliable traffic distribution.

05 / Security

Security Controls

Use Firewalls, Secret Manager, API Tokens, backups, snapshots, and private networking to protect client workloads.

How It Works

Three steps from client idea to production AI system.

STEP 01

Build

Create client AI apps, chatbots, agents, RAG systems, and automation workflows on IBEE Cloud VMs, GPU VMs, or Managed Kubernetes.

STEP 02

Secure

Protect client data and services with Firewalls, Secret Manager, API Tokens, private networking, backups, and snapshots.

STEP 03

Deliver

Deploy production APIs, dashboards, AI workflows, and client platforms with scalable compute, object storage, and load balancing.

client-deploy.shcopy

# launch a client AI application server

$ ibee compute create \

  --name "client-ai-agent-prod-01" \

  --image "ubuntu-22.04" \

  --region "ind-hyd-1"

› client AI app server ready

# store client knowledge base files

$ aws s3 cp ./client-docs \

  s3://client-ai-prod/knowledge-base/ \

  --recursive \

  --endpoint-url https://{project_id}.blob.ibeestorage.com

› client documents stored for AI workflow

Use Cases

Built for AI agencies delivering real client solutions.

AI Chatbots

support · sales · internal helpdesk

AI Agents

tasks · workflows · automation

RAG Applications

documents · search · knowledge bases

AI Automation

operations · CRM · reporting

Computer Vision Apps

inspection · OCR · media analysis

Custom AI Platforms

dashboards · APIs · client portals

Get Started

Deliver client AI projects faster without rebuilding infrastructure every time.

Start with IBEE for AI compute, application hosting, object storage, Kubernetes, load balancing, firewalls, secrets, backups, and production-ready client infrastructure.

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