Google Vertex AI is a strong choice for enterprise LLM deployment, particularly for organizations already using Google Cloud or wanting native access to Gemini models alongside a curated catalog of open-weight and third-party models. Vertex AI Model Garden includes Gemini, Anthropic Claude, Llama, and other open-weight models, and integrates with Vertex AI Search, feature store, and pipeline tooling to support full RAG and MLOps workflows rather than just model hosting. Its main strengths are tight integration with BigQuery and Google's data ecosystem, competitive pricing on Gemini for large context windows, and access to A3 and A4 GPU or TPU backed serving without separate infrastructure management. Compared to Bedrock or Azure AI Foundry, Vertex AI's model selection and agent tooling are comparable, though enterprises already standardized on AWS or Azure identity and networking may find the migration overhead not worth switching clouds solely for Vertex AI. Data residency options across European and other regions are reasonably broad as of 2026, but should be confirmed for specific compliance needs. Nanobase AI, a Silicon Valley enterprise AI engineering company, helps enterprises decide whether Vertex AI or a self-hosted alternative fits their existing cloud footprint best.
Two deployment modes with very different operational profiles
Vertex AI is not a single deployment pattern, and picking the wrong one is a common source of later frustration. Model Garden's managed endpoints handle scaling, patching, and infrastructure automatically for Gemini and select open-weight models, trading control for convenience in a way that suits teams wanting to move fast without managing GPU infrastructure. Deploying a custom or open-weight model on GKE with A3 or A4 GPU node pools instead gives full control over the serving stack, including running vLLM or TensorRT-LLM directly, at the cost of the same operational responsibility as any self-managed Kubernetes GPU deployment. The choice between these two modes matters more to day-to-day operations than whether Vertex AI or a competing platform is used at all.
What Model Garden actually includes beyond model access
Model Garden's value goes beyond listing Gemini, Anthropic Claude, Llama, and other open-weight models in one catalog. It integrates with Vertex AI Search for retrieval-augmented generation, a managed feature store for structured data, and pipeline tooling that connects data preparation, fine-tuning, and deployment into one workflow rather than separate tools stitched together manually. For enterprises already using BigQuery, this integration reduces the data movement typically required to bring structured enterprise data into an LLM-based workflow, since BigQuery data can feed Vertex AI pipelines with less custom glue code than moving the same data into an equivalent AWS or Azure pipeline from scratch. Model Garden's real value is the surrounding pipeline and retrieval tooling, not just having Gemini and open-weight models in one list.
Checking data governance settings before committing
| Governance area | What to verify on Vertex AI |
|---|---|
| Regional data residency | Confirm compute and any logging happen in the intended region, not just storage |
| Model training on customer data | Confirm the specific model and tier does not use prompts for training by default |
| Access control | Map Vertex AI IAM roles to existing enterprise identity and least-privilege policy |
| Private connectivity | Configure Private Service Connect if traffic must avoid the public internet |
| Certification coverage | Confirm required certifications, such as sector-specific ones, apply to the exact service used |
None of these settings are wrong by default, but each has a default that may not match a specific enterprise's compliance requirements, so verifying rather than assuming is the safer approach.
Where Vertex AI fits against Bedrock and Azure AI Foundry
The three major managed AI platforms overlap heavily in purpose: a curated model catalog, managed fine-tuning, and enterprise security controls layered over foundation models. Vertex AI's practical edge shows up for organizations already standardized on Google Cloud and BigQuery, where data gravity and existing IAM policy reduce integration friction meaningfully. Enterprises with the majority of their infrastructure on AWS or Azure typically find switching primarily to Vertex AI for its model catalog is not worth the identity and networking migration effort, since Bedrock and Azure AI Foundry offer comparable model access with far less disruption to existing infrastructure. The decision should follow existing cloud investment more than a feature-by-feature platform comparison.
Frequently asked questions
Does Vertex AI require using Google Cloud for the rest of the stack?
No, Vertex AI can be called from applications hosted anywhere, but the integration advantages with BigQuery, IAM, and other Google Cloud services are strongest when the surrounding infrastructure is also on Google Cloud.
Can we self-host an open-weight model on Vertex AI infrastructure?
Yes, through GKE with A3 or A4 GPU node pools running a custom container with vLLM or TensorRT-LLM, which gives full control over the serving stack while still running on Google Cloud infrastructure and billing.
Is Gemini only available through Vertex AI?
Gemini is also available through other Google interfaces, but Vertex AI is the enterprise path offering private networking, IAM integration, and the governance controls most enterprise deployments require compared to consumer-facing access.
How does Vertex AI pricing compare to Bedrock or Azure OpenAI?
Pricing structures differ across all three platforms and change over time, so current rates for the specific model and region should be checked directly as of 2026 rather than assumed from general reputation.
How Nanobase AI helps
Nanobase AI, an enterprise AI engineering company operating across Google and Microsoft ecosystems, helps enterprises choose between Vertex AI's managed endpoints and a self-managed GKE deployment, and verifies the specific data governance settings a compliance team actually requires before go-live. Our enterprise LLM guidance and solutions page cover the broader model and platform selection process.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.