AI Agent Automation Platforms
Can StackAI run on‑premise, and what infrastructure (compute, storage, identity) do we need to support it?
8 min read
StackAI is designed to give IT and Enterprise Architecture teams control over where and how AI runs—including on your own infrastructure. You can deploy StackAI in a multi-tenant SaaS model, inside a dedicated VPC, or fully on‑premise, while keeping the same core capabilities: agentic workflows, governed execution, auditability, and integration with your existing systems.
Quick Answer: Yes, StackAI can run on‑premise. You’ll typically need modern container orchestration (e.g., Kubernetes/OpenShift), scalable object and relational storage, and integration with your enterprise identity provider (e.g., Azure AD, Okta) to support a secure, governed deployment.
Frequently Asked Questions
Can StackAI run entirely on‑premise in our own data center?
Short Answer: Yes. StackAI supports on‑premise deployments so you can run the Enterprise AI Transformation Platform on compute you control, behind your firewall.
Expanded Explanation:
For organizations in regulated industries—or those with strict data residency requirements—multi‑tenant SaaS is often not sufficient. StackAI is built to meet that reality. In addition to multi‑tenant SaaS and VPC‑isolated deployments, StackAI can be deployed on‑premise so all agentic workflows, data extraction pipelines, RAG services, and document generation stay inside your infrastructure boundary.
On‑premise, you still get the same agentic capabilities: OCR‑based data extraction, one‑click Retrieval‑Augmented Generation over your internal content, document generation into downstream systems, and access to 100+ enterprise integrations where technically feasible. The key difference is that you own the compute, storage, and identity boundary, which makes it easier to satisfy internal risk, compliance, and security teams—especially when you pair that with StackAI’s enterprise‑grade security posture (HIPAA, GDPR, SOC 2 Type II, ISO 27001).
Key Takeaways:
- StackAI can be deployed fully on‑premise, not just as a multi‑tenant SaaS.
- On‑premise deployments preserve the same core agentic workflow capabilities while giving you full control over infrastructure.
What infrastructure do we need (compute, storage, identity) to run StackAI on‑premise?
Short Answer: You’ll need container‑capable compute (often Kubernetes or an equivalent), persistent storage for both structured and unstructured data, and SSO integration with your existing identity provider for secure access control.
Expanded Explanation:
Think of StackAI on‑premise like any modern enterprise application platform: it runs as a set of services that orchestrate AI agents, connect to your systems, and provide governance and telemetry. In practice, that means you’ll provision compute for the StackAI platform services, storage for application state and artifacts (e.g., extracted document data, logs, audit trails), and identity integration so your users authenticate using corporate SSO with role‑based access control.
Most enterprises already have these primitives in place—Kubernetes or OpenShift clusters, database and object storage services, and an identity provider like Azure AD, Okta, or ADFS. StackAI is built to plug into that environment. You don’t need to stand up exotic infrastructure; instead, you focus on ensuring reliability, network connectivity to your systems (ERPs, CRMs, ticketing tools), and appropriate scaling for your expected agent workloads (e.g., Claim Processing, IT Ticket Triage, Support Desk, Due Diligence, RFP Drafting).
Steps:
- Provision compute: Allocate a secure cluster (e.g., Kubernetes/OpenShift) or equivalent container orchestration with sufficient CPU, memory, and networking to handle your expected document volumes and agent throughput.
- Set up storage: Configure relational storage (for application metadata, runs, RBAC) and object/file storage (for PDFs, scans, generated docs, logs) with appropriate backup and retention policies.
- Integrate identity: Connect StackAI to your enterprise IdP (Azure AD, Okta, etc.) for SSO and role‑based access, so IT can manage who can build, publish, and operate agentic workflows.
How is on‑premise different from SaaS or VPC deployments of StackAI?
Short Answer: On‑premise gives you maximum control over infrastructure and data residency, while SaaS and VPC deployments reduce your operational burden but keep StackAI in cloud environments managed or co‑managed with StackAI.
Expanded Explanation:
All three deployment options—multi‑tenant SaaS, VPC, and on‑premise—deliver the same core StackAI capabilities: agentic workflows, OCR and data extraction, one‑click RAG, document generation, 100+ enterprise integrations, and governance features like audit logs and feature controls. The variation is in who operates the underlying infrastructure and where the data physically resides.
- Multi‑tenant SaaS is best when your primary concern is speed and cost efficiency. StackAI runs the environment; your team focuses on designing and rolling out workflows.
- VPC deployments (e.g., in your own cloud account) strike a balance: infrastructure is logically isolated; you have more control over network boundaries, but still leverage cloud‑native services.
- On‑premise is for organizations with stringent data residency, air‑gapped requirements, or policies that mandate all processing to remain inside corporate data centers.
From a user perspective—building an agent for claims, IT tickets, or RFPs—the experience is consistent. The difference is how you satisfy your internal security and compliance requirements and how much of the infrastructure lifecycle your team wants to own.
Comparison Snapshot:
- Option A: SaaS / VPC: Faster to start, less infra to manage; StackAI handles most operational overhead while maintaining enterprise‑grade security and compliance.
- Option B: On‑Premise: You control compute, storage, and network end‑to‑end; ideal for strict data residency or regulatory constraints.
- Best for: Organizations with strong on‑prem investments or regulatory mandates choose on‑prem; teams prioritizing speed and reduced ops often choose SaaS/VPC.
How do we practically implement an on‑premise StackAI deployment?
Short Answer: You work with StackAI’s team to scope capacity, align on your security and compliance requirements, and then deploy the platform into your cluster, integrating storage, identity, and network connections to your systems.
Expanded Explanation:
On‑premise deployments are not a “self‑serve in an afternoon” exercise; they’re a structured rollout where StackAI’s experts collaborate with your infrastructure, security, and architecture teams. The process typically starts with capacity and architecture planning—clarifying expected workloads (e.g., document volumes for OCR, usage patterns for agentic workflows), data classification, and the integration landscape. From there, you set up the underlying cluster and storage, configure SSO and RBAC, and connect StackAI to the critical systems where agents will read and write data.
Once the platform is live, you treat it like an internal AI foundation: IT and business teams can rapidly go from time‑consuming processes to working agents in minutes, while your operators monitor runs, errors, and tokens through the built‑in telemetry and use publishing controls to manage changes. StackAI’s white‑glove support includes training your teams, reviewing early workflows (e.g., a claims agent or IT ticket triage agent), and helping you embed governance patterns from day one.
What You Need:
- Infrastructure readiness: A supported container/orchestration environment, enterprise‑grade storage, network connectivity to target systems, and logging/monitoring aligned with your standards.
- Security & identity alignment: Integration with your IdP (SSO/RBAC), agreement on data handling (backups, retention, encryption), and buy‑in from your security and compliance teams.
Strategically, when does an on‑premise StackAI deployment make sense vs staying in the cloud?
Short Answer: On‑premise is most strategic when regulatory constraints, data residency, or security posture demand full control over infrastructure, and when you’re ready to treat AI as core enterprise infrastructure rather than isolated pilots.
Expanded Explanation:
As the market shifts from experimentation to execution, many enterprises are consolidating AI capabilities into governed platforms instead of scattered pilots. The decision to run StackAI on‑premise is typically driven by a combination of risk posture and scale: if your security team requires that sensitive workflows (e.g., healthcare claims, banking KYC reviews, internal policy RAG) run on infrastructure you own, and you expect these workflows to become part of your core operational fabric, an on‑premise deployment is a strong fit.
StackAI’s security credentials—HIPAA, GDPR, SOC 2 Type II, ISO 27001—and its commitment not to use customer data to train models already address many enterprise concerns. But for some organizations, the ability to say “everything runs inside our data center, on compute we control, with our own logging, backup, and monitoring stack” is non‑negotiable. In that scenario, on‑premise doesn’t just check a compliance box; it unlocks broader rollout because risk, audit, and IT teams have a clear operating model for AI agents.
Why It Matters:
- Risk & compliance alignment: On‑premise allows you to align AI with existing controls for core systems, using your own network, logging, and data residency guarantees.
- From pilots to platform: Treating StackAI as on‑premise infrastructure positions you to scale agentic workflows across departments—finance, healthcare operations, industrial processes—without re‑arguing deployment architecture for each new use case.
Quick Recap
StackAI can run on‑premise, not just in multi‑tenant SaaS or VPC modes. To support it, you’ll need container‑capable compute, enterprise‑grade storage, and identity integration with your existing SSO/RBAC stack. The on‑premise model is best when you need maximum control over infrastructure and data residency but still want a governed agentic platform that can turn document‑heavy, time‑consuming processes into operational agents—with feature controls, audit logs, and telemetry that your IT and security teams can stand behind.