VPC deployment

A virtual private cloud is a logically isolated network inside a public cloud account. Deploying AI into your VPC means the model server, vector database, application services and observability stack are all provisioned in your account, on subnets you define, with security groups and egress rules you own. The vendor building the system works inside your environment rather than hosting it for you.

This differs from a vendor-hosted SaaS, where your data is sent to their infrastructure, and from a true on-premise deployment, where you own the physical hardware. VPC deployment is the middle path most mid-size companies choose: cloud elasticity and managed services, but within an account boundary that your security team already governs.

A VPC deployment is not automatically zero-egress. Services inside a VPC can still call external APIs unless egress is restricted. The VPC provides the container; the egress policy, IAM roles and private endpoints decide what actually stays inside.

• CISO: the AI stack inherits the account's existing IAM, logging, key management and network controls instead of needing a separate review.
• CTO / Head of Engineering: the team can use managed GPUs, databases and queues from the cloud provider while keeping data in-account.
• Compliance officer: region selection within the VPC directly answers data-residency requirements.
• CFO: infrastructure appears on the existing cloud bill with reserved-instance and committed-use discounts already negotiated.

VPC deployment exists because most companies neither want a vendor holding their data nor want to run physical servers. Their cloud account already has the security posture, audit logging and procurement approval; putting the AI system there reuses all of it. The trade-off is that the vendor needs scoped access to your account, which requires proper IAM roles and a handover plan, and the system's cost and uptime become your operations team's responsibility. It is the natural companion to the stance that clients own their code, models and infrastructure: if it runs in your account, ownership is a fact rather than a clause.

• A fintech deploying a KYC extraction pipeline in its own AWS Mumbai region VPC to satisfy RBI localisation expectations.
• A B2B SaaS company running per-tenant AI workloads in its GCP project with tenant isolation enforced at the network and IAM layer.
• A hospital group hosting a voice-agent backend in Azure with private endpoints to its patient-administration system.
• A retailer running a recommendation model next to its order database in the same VPC to avoid cross-network data copies.
• A logistics operator provisioning GPU nodes in its cloud account for document parsing, scaled down outside business hours.

Technique / practiceA deployment practice covering network design, IAM scoping, private endpoints and handover. Eazyware builds in the client's cloud account by default under Private Agentic AI, so ownership and residency are settled by where the system runs.

Eazyware service that covers it: Agentic AI Solutions (self-hosted). Starting prices are on the pricing page.