AI Agent Automation Platforms
How do we deploy StackAI in a VPC—what are the network requirements, data residency options, and isolation model?
8 min read
For most IT and enterprise architecture teams, the decision to deploy StackAI in a VPC comes down to three things: how the platform sits in your network, where data is stored and processed, and how strongly tenants are isolated. This FAQ walks through the VPC deployment model for StackAI’s Enterprise AI Transformation Platform, including typical network requirements, data residency options, and how isolation works in practice.
Quick Answer: StackAI can be deployed into your VPC with tightly controlled ingress/egress, regional data residency, and a clear isolation model that separates your workloads and data from other tenants, while still enabling secure access to LLM providers and 100+ enterprise integrations under your governance policies.
Frequently Asked Questions
What does a VPC deployment of StackAI look like in practice?
Short Answer: In a VPC deployment, StackAI runs inside your controlled cloud network, using your security policies, private subnets, and routing to handle agentic workflows, data extraction, RAG, and document generation.
Expanded Explanation:
When you deploy StackAI in a VPC, the platform’s core components—workflow engine for agentic workflows, data extraction/OCR, knowledge retrieval (RAG), document generation, and interface/API services—run in your own cloud account or a logically isolated environment you control. You determine how traffic flows in and out: through load balancers, WAFs, private links, and service gateways that align with your existing standards.
This model is designed for regulated operations where “prove it” is non‑negotiable: you need clear audit logs, control over which external LLMs and SaaS tools are reachable, and a guarantee that your data is not used to train AI models. StackAI’s VPC deployment gives you that control layer while still letting agents read, write, and execute tasks across your existing systems via 100+ enterprise integrations.
Key Takeaways:
- StackAI runs inside your VPC, governed by your network and security controls.
- You retain control over ingress/egress, reachable LLMs, and third‑party integrations.
What are the network requirements to deploy StackAI in a VPC?
Short Answer: You’ll need standard VPC networking (subnets, routing, security groups), inbound access for users and/or internal services, and controlled outbound access to LLM providers and any external SaaS integrations you choose to enable.
Expanded Explanation:
From a network architecture perspective, StackAI behaves like a modern enterprise application stack deployed inside your VPC. Core services typically sit in private subnets, fronted by load balancers or API gateways in public or internal subnets. You manage identity at the edge (SSO/SAML/OIDC), inspection (WAF, IDS/IPS), and routing to internal systems (databases, ticketing, ERP, content repositories).
Outbound connectivity is required for LLM providers (e.g., OpenAI, Anthropic) and any external tools you intentionally connect (email gateways, document storage, CRM, etc.). Many teams route this traffic through NAT gateways, egress firewalls, or proxy appliances to preserve central control and logging. Because StackAI is built for enterprise deployment, it fits cleanly into environments where egress is tightly pinned down and every external destination must be justified.
Steps:
-
Design network placement:
Allocate subnets (typically private) for StackAI services, decide on load balancer/API gateway placement, and define routing to internal systems that agents must access (databases, ticketing systems, file repositories). -
Configure ingress and identity:
Expose StackAI via internal or external endpoints (depending on your use cases), integrate with your SSO provider, and apply edge protections (TLS termination, WAF, IP allowlisting if needed). -
Control egress and integrations:
Define outbound rules for approved LLM endpoints and external tools, route traffic through your preferred egress controls, and enable only the integrations your governance policies permit.
How does StackAI’s VPC deployment compare to multi‑tenant SaaS for data residency and isolation?
Short Answer: Multi‑tenant SaaS gives you certified security and regional hosting, while VPC deployment adds deeper environmental control, stricter network isolation, and the ability to align fully with your own data residency and segmentation policies.
Expanded Explanation:
In a multi‑tenant SaaS model, you rely on StackAI’s cloud infrastructure, which is certified for HIPAA, GDPR, SOC 2 Type II, and ISO 27001, with strong logical tenant separation, audit logs, and the guarantee that customer data is not used to train AI models. You typically choose a hosting region to align with data residency needs, and StackAI manages scaling, patching, and baseline security controls.
With a VPC deployment, you gain an additional layer of isolation and control. Your StackAI environment is deployed into your network boundary, under your cloud account or a dedicated VPC isolated from other customers. You decide which regions to use (to meet residency requirements), how to segment environments (prod vs. non‑prod, line‑of‑business boundaries), and how to integrate with your internal systems. This is particularly valuable for highly regulated workloads like claim processing, healthcare operations, and financial due diligence, where network boundaries and data locality are audited.
Comparison Snapshot:
- Multi‑tenant SaaS:
StackAI‑managed environment with regional hosting, logical tenant isolation, and certified security (HIPAA, GDPR, SOC 2 Type II, ISO 27001). - VPC Deployment:
Your‑managed infrastructure in your VPC, with network‑level isolation, custom routing/egress, and tight alignment to your internal residency and segmentation policies. - Best for:
VPC deployment is best for organizations that need maximum control over network topology and data residency, or must prove isolation at both logical and network layers to regulators and internal security teams.
How is tenant and data isolation handled in a VPC deployment?
Short Answer: StackAI uses strong logical isolation and governance controls by default; in a VPC deployment, that is combined with your own VPC‑level segmentation (accounts, VPCs, subnets, and IAM) to separate environments and workloads.
Expanded Explanation:
StackAI’s enterprise‑grade platform includes guardrails, PII protections, role‑based access control, and audit logs that trace which agent ran, on which data, and what it produced. In a VPC deployment, you extend this with your own isolation strategies: dedicated accounts or VPCs per environment, network ACLs and security groups to segment services, and IAM policies to control who can administer the platform and which systems agents can touch.
Practically, most teams create separate environments (e.g., dev, test, prod) with distinct network segments and access policies. Publishing controls and an agentic lifecycle (similar to software delivery) ensure that changes are peer‑reviewed before hitting production. On the data side, you can align storage (for RAG indices, run logs, and generated documents) with your residency rules and encryption standards, while retaining full visibility through your existing telemetry and SIEM tools.
What You Need:
- Clear environment strategy (e.g., separate accounts/VPCs or subnets for dev, test, prod) with corresponding IAM and access controls.
- Alignment between StackAI’s built‑in RBAC, audit logs, and your broader governance stack (SSO, logging/monitoring, and change‑management practices).
What are StackAI’s data residency options and how do they apply in a VPC model?
Short Answer: StackAI supports regional hosting and strict data‑handling controls; in a VPC deployment, you choose the cloud region(s), storage services, and routing policies that enforce your data residency obligations.
Expanded Explanation:
From a compliance standpoint, StackAI is certified for HIPAA, GDPR, SOC 2 Type II, and ISO 27001, and does not use customer data to train AI models. In practice, data residency is enforced through where data is stored, where it is processed, and which external services it can be sent to.
In a VPC deployment, you pick the underlying region(s) that host StackAI’s services and your data stores. RAG indices, extracted structured data from PDFs/scans/forms, audit logs, and generated documents stay within that region unless your own network configuration permits cross‑region traffic. You can further constrain which LLM regions/endpoints are used and apply opt‑out controls for any third‑party integrations that you don’t want receiving data. This enables IT and compliance teams to map StackAI’s footprint cleanly into existing data residency and sovereignty frameworks.
Why It Matters:
- You can align StackAI’s deployment with regulatory requirements for healthcare, finance, and other regulated sectors by anchoring it in approved regions and storage services.
- Your team retains control over where data lives, how long it’s retained, and which external providers can process it, backed by StackAI’s certifications and explicit non‑training commitment.
Quick Recap
Deploying StackAI in a VPC gives IT and enterprise architecture teams a way to bring agentic workflows—spanning data extraction, RAG, and document generation—into production while staying inside their own network and compliance boundaries. You handle the VPC networking (subnets, routing, ingress/egress), choose regions and storage that satisfy data residency rules, and apply your isolation model (accounts, VPCs, RBAC) on top of StackAI’s enterprise‑grade security, audit logs, and governance controls. The result is a platform where AI agents can read, write, and execute tasks across your systems, with the same level of control you expect from any critical enterprise application.