# Self-Hosted AI vs Google Vertex AI

> Source: https://ibl.ai/resources/comparisons/self-hosted-ai-vs-google-vertex-ai


*An owned, model-agnostic, deploy-anywhere AI platform — vs. Google's GCP-native Vertex AI for building agents and apps on Gemini and Model Garden*

Google Vertex AI gives enterprises a GCP-native platform to build agents and apps on Gemini, with Model Garden for third-party models and Vertex AI Agent Builder for orchestration. It runs on Google Cloud, integrates deeply with the GCP stack, and Google manages the model catalog.

Self-hosted AI is a different shape: a model-agnostic platform you own, that runs on any cloud, on-premise, or fully air-gapped. You bring any LLM — including Gemini and the Model Garden roster — and operate the platform under perpetual license.

Both are legitimate enterprise options. The decision is whether you want a deeply GCP-native AI cloud built around Gemini, or an owned platform that can run anywhere and route to any model.

## Feature Comparison

### Platform Capabilities

| Criteria | Self-Hosted AI | Google Vertex AI |
|----------|--------------------|--------------------|
| Foundation Model Catalog | Run any open or commercial model — including Gemini — through your own routing layer. | Strong first-party Gemini family plus Model Garden third-party models managed inside Vertex AI. |
| Agent & Workflow Builder | Full agentic OS — agents, workflows, learning, and content — owned end to end. | Vertex AI Agent Builder is capable, bounded to GCP's runtime and tooling. |
| Enterprise Search & RAG | Permissions-aware retrieval over your knowledge, with embeddings of your choice. | Strong RAG via Vertex AI Search and GCP's data plane. |
| Any-LLM Routing | Route any LLM by cost, latency, and capability — switch per workload. | Routing inside Vertex AI's catalog; non-Vertex models require integration work. |

### Ownership & Cloud Posture

| Criteria | Self-Hosted AI | Google Vertex AI |
|----------|--------------------|--------------------|
| Multi-Cloud / On-Prem / Air-Gapped | Run on AWS, Azure, GCP, on-premise, or fully air-gapped — same platform across environments. | GCP-native; on-prem via Distributed Cloud is limited; not built for air-gap. |
| Data Sovereignty & Residency | Prompts, embeddings, and audit logs stay entirely in your environment. | Strong GCP region controls; data is processed in the Google cloud under shared-responsibility. |
| Source-Code & Platform Ownership | Own the full platform code under perpetual license — fork, extend, exit. | Managed cloud service; you consume Vertex AI rather than owning the platform. |
| Cloud Vendor Lock-In | Cloud-agnostic — migration between clouds doesn't change the platform. | Tied to GCP account, Vertex services, and Google pricing. |

### Cost & Compliance

| Criteria | Self-Hosted AI | Google Vertex AI |
|----------|--------------------|--------------------|
| Cost Model at Scale | Flat platform fee + usage on compute you own; cost grows with consumption, not seats or services. | GCP consumption + Vertex service fees; predictable but tied to Google pricing. |
| Compliance Fit (HIPAA / FedRAMP / FERPA / NIST) | Compliance posture sits inside your perimeter; air-gap satisfies the strictest mandates. | Broad Google compliance coverage, including GovCloud-style federal options. |
| Time-to-Value | Requires infrastructure and setup, or a partner to deploy and operate it. | Turn on Vertex services inside your GCP project and ship quickly for GCP-standardized teams. |
| Support & Service Catalog | Forward-deployed engineering + enterprise support across the platform. | Google enterprise support and the broader GCP ecosystem. |

## Detailed Analysis

### GCP-Native AI Cloud vs an Owned Platform

**Self-Hosted AI:** Self-hosted AI is the right shape when ownership, multi-cloud, and air-gap matter — when you want one platform across GCP, AWS, Azure, on-prem, and air-gapped.

**Google Vertex AI:** Vertex AI is the right shape when GCP is the strategic cloud and Gemini is the model bet you want to make.

**Verdict:** Choose Vertex AI for GCP depth; choose self-hosted AI when you want one owned platform across clouds and deployment models.

### Model Freedom vs Curated Catalog

**Self-Hosted AI:** A model-agnostic platform runs any LLM — including Gemini — and routes per workload as the frontier moves.

**Google Vertex AI:** Vertex AI centers on Gemini, with Model Garden for breadth — strong, but selection and routing live with Google.

**Verdict:** If model freedom matters more than centering on Gemini, self-hosted AI fits; if Gemini is the strategic model, Vertex AI is sufficient.

### Lock-In: Cloud + Catalog vs Cloud-Agnostic + Owned

**Self-Hosted AI:** Owning the platform code on infrastructure you choose means migrations between clouds don't replatform your AI — and air-gap is always an option.

**Google Vertex AI:** Vertex AI assumes GCP as the durable strategic cloud; leaving Google means rebuilding the AI layer.

**Verdict:** For organizations that want optionality on cloud and models simultaneously, the owned platform wins on lock-in posture.

## FAQ

**Q: Is ibl.ai an alternative to Google Vertex AI?**

Yes. Both let enterprises build agents and apps; ibl.ai is model-agnostic and owned, running on any cloud or on-prem, while Vertex AI is GCP-native and consumed as managed services.

**Q: Can I still use Gemini on a self-hosted platform?**

Yes. ibl.ai is model-agnostic, so Gemini can be one of the LLMs you route to — alongside open and other commercial models, without tying your platform to GCP.

**Q: Can ibl.ai run on Google Cloud?**

Yes. It deploys on GCP, Azure, AWS, or on-premise — the same platform across environments, including in your GCP project as a managed VPC deployment.

**Q: Can it run air-gapped, unlike Vertex AI?**

Yes. ibl.ai can run fully on-premise or air-gapped with local models and zero external calls; Vertex AI is a managed cloud service and is not built for air-gap.

**Q: Is ibl.ai cheaper than Vertex AI at scale?**

Often, because cost grows with consumption on compute you own rather than across a hyperscaler's catalog, and you're not paying GCP's premium for every adjacent service.

**Q: How does ibl.ai fit in?**

ibl.ai is a model-agnostic, self-hosted AI Operating System you own and run on any cloud, on-premise, or air-gapped — for enterprise agents and apps, with SOC 2, HIPAA, and FERPA compliance by design.