Infrastructure That
Powers AI Startups

From first experiment to millions of users, IBEE gives AI teams the GPU power, storage, networking, and security they need to build, scale, and ship world-class AI products.

Start building on IBEE Explore GPU

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

AI teams burn money before they find product-market fit.

GPU usage gets expensive long before revenue catches up. A few idle instances or unoptimized training runs can spike the bill overnight.

How IBEE handles it

Start with GPU VMs for experimentation and inference. As workloads grow, move heavier workloads to larger GPU capacity or bare metal GPU infrastructure. IBEE helps teams avoid overbuilding early while still giving them room to scale.

GPU VMsCloud VMsBare Metal GPU planned40 Gbps server network

GPU cost becomes painful before revenue illustration

The demo runs fine, but real users expose infrastructure gaps.

Demos work for a handful of internal users. Real customers need uptime, secure APIs, load balancing, backups, and clean deploys, and engineers end up building infra instead of shipping AI.

How IBEE handles it

Use IBEE Cloud VMs, Managed Kubernetes, Load Balancer as a Service, Firewalls, Backups, Snapshots, API Tokens, and Secret Manager to move from demo to production without building every infrastructure layer from scratch.

Cloud VMsManaged KubernetesLoad BalancerFirewallBackups & SnapshotsSecret Manager

Prototype works, production breaks illustration

Every user request becomes a compute cost.

After launch, daily inference, not training, drives the bill. As traffic grows, latency creeps up, queues build, and the product starts to feel unstable.

How IBEE handles it

Deploy inference workloads on GPU VMs or CPU VMs, place APIs behind IBEE Load Balancers, and use Managed Kubernetes when the workload needs container orchestration and scaling. IBEE's 40 Gbps infrastructure helps with fast internal data movement between services, storage, and compute.

GPU VMsCloud VMsManaged KubernetesLoad Balancer40 Gbps network

Inference scaling is harder than training illustration

Datasets, model files, and outputs keep growing every day.

Datasets, checkpoints, embeddings, user uploads, and generated media pile up fast. Without one storage layer, data ends up scattered across disks, buckets, and temporary servers.

How IBEE handles it

Use IBEE Object Storage to store datasets, model artifacts, generated outputs, backups, and customer files. Because it is S3-compatible, teams can use standard SDKs, tools, and workflows without rewriting their application for a proprietary storage API.

Object StorageS3-compatible APIsBackups & SnapshotsBlock Storage

AI data grows faster than expected illustration

The moment customers trust you with data, security becomes a sales blocker.

The first enterprise deal brings real questions: where data lives, how secrets are stored, who can hit your APIs, how services are isolated, and what the backup story looks like.

How IBEE handles it

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

Secret ManagerAPI TokensFirewallPrivate networkingBackups & Snapshots

Security questions arrive suddenly illustration

AI startups need flexibility, not a closed infrastructure path.

Teams want open-source models, standard containers, S3 tools, and Kubernetes workflows. Proprietary platforms make leaving, or even multi-cloud, slow and expensive later.

How IBEE handles it

Build on standard VMs, GPU VMs, S3-compatible Object Storage, Managed Kubernetes, standard networking, and API-driven infrastructure. This gives AI teams flexibility while still getting managed cloud convenience.

Cloud VMsGPU VMsS3-compatible Object StorageManaged KubernetesAPI Tokens

Vendor lock-in slows future growth illustration

40Gbps

server network fabric

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

S3compatible

object storage APIs

Use standard SDKs, CLIs, and tools without rewriting your storage layer.

GPUready

AI compute

Run AI workloads on GPU VMs and scale toward larger GPU infrastructure.

Oneplatform

production stack

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

From infrastructure overhead
to product velocity.

See how teams move once they stop managing GPUs and start shipping.

Before

Building everything yourself

GPU bills increase before revenue becomes predictable
Datasets, model files, and outputs are scattered across tools
Inference slows down when real users start using the product
Engineers spend time managing servers, firewalls, SSL, and deployments
Security and backup questions become blockers during customer onboarding

After

Focused on the product

Start with GPU VMs and scale infrastructure as the product grows
Store datasets, model artifacts, and generated outputs in S3-compatible Object Storage
Run APIs behind load balancers and scale workloads with Managed Kubernetes
Use IBEE cloud primitives instead of building every infrastructure layer yourself
Add firewall, secret management, API tokens, backups, and snapshots from the same platform