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.
by ibl.ai
Owned agentic AI platformby Google Cloud
Hyperscaler AI platform (Vertex AI + Agent Builder + Model Garden)| 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. |
| 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. |
| 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. |
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.
Vertex AI is the right shape when GCP is the strategic cloud and Gemini is the model bet you want to make.
Choose Vertex AI for GCP depth; choose self-hosted AI when you want one owned platform across clouds and deployment models.
A model-agnostic platform runs any LLM — including Gemini — and routes per workload as the frontier moves.
Vertex AI centers on Gemini, with Model Garden for breadth — strong, but selection and routing live with Google.
If model freedom matters more than centering on Gemini, self-hosted AI fits; if Gemini is the strategic model, Vertex AI is sufficient.
Owning the platform code on infrastructure you choose means migrations between clouds don't replatform your AI — and air-gap is always an option.
Vertex AI assumes GCP as the durable strategic cloud; leaving Google means rebuilding the AI layer.
For organizations that want optionality on cloud and models simultaneously, the owned platform wins on lock-in posture.
If GCP is the strategic cloud and Gemini is the central model bet, Vertex AI delivers the deepest native AI services.
Self-hosted AI runs the same platform across GCP, AWS, Azure, on-prem, and air-gapped — without rebuilding when cloud strategy shifts.
Full source-code ownership and air-gap deployment meet mandates a hyperscaler-managed cloud can't satisfy.
Routing across any open or commercial model — including Gemini — beats centering on a single first-party catalog.
Timeline: A few weeks, depending on infrastructure and MLOps maturity
Timeline: Days to a couple of weeks
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.