“Our cloud already offers AI tools”: Why native hyperscaler services aren’t enough for enterprise AI.
When IT decision-makers discuss their GenAI strategy, the following argument often comes up very quickly in meetings with cloud architects: “We’re already using Azure, AWS, or Google Cloud. They offer their own AI services—Azure AI Foundry, Amazon Bedrock, and Vertex AI. So why should we introduce an additional layer of abstraction like an AI gateway?”
At first glance, this seems logical. Why introduce another tool when the existing cloud provider already has AI models in its catalog? Examining the architectural reality reveals why native hyperscaler tools alone are insufficient and why a cross-platform gateway is essential for achieving true digital sovereignty.
Why doesn’t a single hyperscaler offer all the leading AI models
However, anyone who takes this approach in isolation will suddenly fall into the multi-cloud fragmentation trap. What works for a single prototype in a single cloud quickly becomes unmanageable at the enterprise level.
Large language model development proceeds in extreme leaps in performance. No cloud provider consistently has the best model for every use case:
(Note: This article is from July 2026, so the models may be outdated or have been renamed.)
- Google Cloud (GCP) scores big with Gemini 3.1 on Vertex AI for massive context windows and multimodal data analysis.
- AWS offers direct access via Bedrock to top-tier models such as Anthropic’s Claude Sonnet or Claude Opus, which are extremely popular among developers for coding tasks.
- Microsoft Azure impresses with its deeply integrated OpenAI models, such as GPT-5.5 via Azure AI Foundry.
- European hyperscalers like STACKIT or Evroc are becoming indispensable for highly sensitive, sovereign data in the public or regulated sectors.
Relying solely on the native AI consoles of a single cloud provider automatically locks your teams out of the innovations offered by other platforms. Otherwise, your developers will start setting up decentralized accounts with multiple hyperscalers, which means shadow IT chaos will start all over again.
How does an enterprise AI gateway solve the FinOps and cost control problem
How can you manage AI costs when one team is developing on Azure, another is testing on AWS, and the data science team is working on Google Cloud?
Each hyperscaler has its own proprietary billing views, quotas, and cost controls. There is no overarching control mechanism for users or teams. The result?
- You have no central single sign-on (SSO) connection to manage access rights and API keys globally.
- You cannot define dynamic, per-user budgets that apply regardless of the model a developer is accessing or the cloud they are using.
- Offboarding employees becomes a security risk because API keys must be manually deleted across different cloud tenants.
How does a unified API endpoint reduce developer overhead
Each cloud provider requires developers to use its own software development kits (SDKs), authentication methods, and interfaces. Switching from an Azure model to a Vertex AI model, for example, often requires adapting code, testing new libraries, and reconfiguring CI/CD pipelines.
The PCG AI Gateway eliminates this overhead by providing a preconfigured LiteLLM proxy. The gateway provides a single, standardized OpenAI-compatible endpoint:
┌──► GCP (Vertex AI: Gemini 3.1)
[One OpenAI-API] ├──► Azure (AI Foundry: GPT-5.5)
► PCG Gateway ──┼──► AWS (Bedrock: Claude Opus)
└──► Sovereign EU (STACKIT / Evroc)Your developers only need to integrate a single API. The gateway allows you to centrally control which hyperscaler processes the request in the background without having to change a single line of code in your applications.
Comparison of Native Hyperscaler Tools vs. PCG AI Gateway
| Challenge | Native Hyperscaler Services (AWS / Azure / GCP) | PCG AI Gateway |
|---|---|---|
|
Multi-Cloud Access |
Only models from the respective cloud provider available |
A single endpoint for all relevant providers (GCP, Azure, AWS, STACKIT, Evroc) |
|
Governance & SSO |
Fragmented per cloud tenant |
Central dashboard via SSO with automated on/offboarding |
|
Cost Control |
Complex cloud billing views without per-user budgets |
Per-user budgets with email alerts (50 / 85 / 95%) |
|
API Standard |
Proprietary SDKs per provider |
Uniform OpenAI-compatible API endpoint |
|
Data Sovereignty |
Often dependent on the provider’s US cloud strategy |
100% GDPR & EU AI Act compliant with zero data retention in the EU |
Neutrality Beats Vendor Lock-in
The hyperscalers’ native AI tools are excellent building blocks. However, they are designed to lock you into their own ecosystem as deeply as possible.
As a leading multi-cloud specialist, PCG understands that the reality in modern enterprises is almost always multi-cloud. The PCG AI Gateway adds a neutral, highly secure layer of governance and control over your entire cloud infrastructure. You can use the best models from all providers, retain absolute control over your FinOps, and remain 100% independent.
Would you like to simplify your multi-cloud AI architecture? Schedule a scoping session with PCG’s Data & AI team to learn how to seamlessly connect the PCG AI Gateway to your existing AWS, Azure, or GCP accounts.
