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Cloud vs. On-Premises AI Agents: How to Choose
TL;DR: Cloud AI agents run on managed Google Cloud infrastructure - shared or dedicated - and get you automating fastest. On-premises agents run in a private cloud or fully air-gapped on your own hardware, so zero data leaves your control. If a regulator or a contract requires your data to stay on infrastructure you control, on-premises is the answer; for most other teams, cloud is the practical choice. The privacy architecture is the same in both.
What’s the difference?
With a cloud deployment, your agents run on Google Cloud infrastructure that’s managed for you: either a shared Google Kubernetes Engine (GKE) cluster or a GKE namespace dedicated to your business, running the AI model you choose. You get automation without buying or operating any hardware.
With an on-premises deployment, agents run on Google Distributed Cloud (GDC) - Google Cloud infrastructure installed in your own data center or at the edge - or in your own private cloud. For the strictest requirements, the Air-Gapped plan runs local models like Gemma 4, Google’s open model, on local hardware with no outside connection. The model, your data, and every inference stay on infrastructure you control.
How the deployment models compare
Revenaite’s four agent tiers map directly onto these deployment models, each one a step up in security:
| Shared cloud (Starter) | Dedicated cloud (Professional) | Private cloud (Enterprise) | Air-gapped (Air-Gapped) | |
|---|---|---|---|---|
| Infrastructure | Shared GKE Cluster | Dedicated GKE Namespace | Private cloud (GDC or your own cloud) | Your own hardware, fully offline |
| AI model | Your choice of model | Your choice of model + sensitive data filters | Models deployed in your private cloud + custom fine-tuning | Local models (e.g. Gemma 4) on local hardware |
| Where your data lives | On Google Cloud, encrypted and secure | On Google Cloud, in a namespace dedicated to you, with sensitive data filtered before it reaches the model | In a private cloud, fully isolated | On your own hardware, with no outside connection |
| Who manages it | Revenaite, on shared infrastructure | Revenaite, on infrastructure dedicated to you | Deployed into your private cloud | Installed on-site on your hardware |
Full plan details - features, support levels, and how plans are scoped - are on the pricing page.
When cloud is the right choice
Cloud is the default for a reason:
- You want to automate right away. There’s no hardware to buy, rack, or operate. The Starter tier is built for small teams that want to automate right away; Professional is built for growing teams of 10-50 people that need multiple agents working together.
- You want infrastructure that manages itself. Agents run with auto-scaling, self-healing workloads - they scale up during peak demand and scale down to save costs, automatically.
- Encryption and isolation already meet your obligations. Starter data is encrypted and secure on a shared cluster; Professional adds sensitive data filters on a namespace dedicated to you, so private details are caught before they reach the model.
And honestly: if your workloads already live in the cloud and nothing in your regulatory or contractual environment says otherwise, adding an on-premises footprint just for AI agents adds cost and operational burden without adding much benefit.
When on-premises is the right choice
Choose on-premises when data sovereignty is a requirement rather than a preference:
- Regulatory or contractual data residency. Some organizations are bound - by regulators, client contracts, or internal policy - to keep certain data on infrastructure they own. On-premises deployment satisfies that directly: zero data leaves your premises.
- Complete data isolation. Enterprise deploys through a private cloud with complete data isolation on a dedicated GKE cluster.
- No outside connection at all. Air-Gapped runs local models like Gemma 4 on local hardware, fully disconnected, so the model, your data, and every inference stay on-site.
- Custom fine-tuning. Enterprise includes Vertex AI custom model training, so the model can be tuned to your business without your data going anywhere.
One more honest note: if you have a strong platform-engineering team, building your own agent stack in-house on GKE and Vertex AI is a legitimate alternative to any vendor - the trade is your team’s time and attention. Working with us makes sense when you want the deployment model and the agents without staffing that build yourself.
What stays the same in both
The deployment model changes where agents run - not how they treat your data. In every tier:
- Every agent runs in isolated containers on GKE.
- Agents are built, trained, and managed through Vertex AI, with model versioning and automated retraining pipelines.
- Every action is recorded in a tamper-proof audit trail you can review, replay, and understand.
- Agents never share your data with third parties. Privacy is the architecture, not a plan feature.
The trade-offs
No option is free of trade-offs, so here they are plainly:
- On-premises means infrastructure you operate. GDC runs in your data center - if you don’t have that footprint today, standing it up is a real project. Cloud gets you running much sooner.
- Cloud means your data lives in Google Cloud data centers. It’s encrypted, runs in isolated containers, and is never shared with third parties - but it isn’t on hardware you physically own. For most businesses that’s fine; for some obligations it isn’t.
- Different models on each side. On Starter and Professional you choose the model that fits your work. Air-Gapped runs local models like Gemma 4, Google’s open model, on your own hardware. You’re trading the widest model choice for a model that never leaves your premises.
Choosing
If nothing binds your data to your own premises, start in the cloud - Starter or Professional depending on team size - and revisit if your requirements change. If data residency is regulatory or contractual, go straight to Enterprise for a private cloud, or Air-Gapped if data can never touch the internet.
You can compare plans on the pricing page and read more about the stack in our infrastructure overview. There are no public prices - every plan is scoped in a free 45-minute consultation - so if you’re unsure which model fits, book a call and we’ll work through it with you.