The Golden Path to Enterprise AI Isn’t One or the Other
Why Platform Teams Must Design for Both Abstraction and Control — and Know When to Lean Hard Into Each
In the rush to adopt AI, platform teams face a critical question:
How will developers access AI capabilities — and who owns the platform that governs scale, cost, and security?
This question shapes what we call the golden path — the productized, supported, and secure way teams get to use AI responsibly inside the enterprise.
The truth is, no one is choosing just cloud-native or just on-prem.
But how you design and govern your primary path — abstraction vs. control — sets the tone for everything else.
Two Archetypes, One Strategic Spectrum
At The Advisor Bench, we define the golden path as:
The opinionated, governed, and supported workflow through which internal teams consume AI services — intentionally constrained to reduce cognitive load, risk, and rework.
To illustrate the trade-offs, we use two real-world archetypes:
🟧 Cloud-Native: AWS SageMaker / Bedrock
API-first, fully managed
Ideal for experimentation and rapid scaling
Reduces platform overhead through abstraction
Trade-offs: Less control, more dependency on cloud-native tools and roadmap
🟦 Infrastructure-Centric: Dell AI Factory + Private Cloud Automation
Stack-aware and hardware-optimized
Prioritizes performance, data locality, and TCO
Delivered through validated designs, APEX-style economics, and automation
Trade-offs: Higher operational responsibility, but more sovereignty
📝 These aren’t the only players. The same dynamics apply to Google’s Vertex AI, Azure AI Studio, or a pure NVIDIA DGX stack. We use AWS and Dell here as clear proxies for opposite ends of the platform spectrum.
The Strategic Trade-offs for Platform Teams
🔁 Abstraction vs. Control
Cloud-native: You get speed and simplicity. But this abstraction can obscure cost drivers, utilization patterns, and latency zones — which hinders optimization, compliance, and troubleshooting.
Infrastructure-centric: You see and manage the full stack. That visibility empowers tuning, observability, and secure placement — but comes with real operational ownership.
⚡ Speed-to-Market vs. Specialization
Cloud: Ideal for launching prototypes, iterating fast, and integrating prebuilt models.
On-prem: Delivers when workloads require tight coupling with physical environments — like secure enclaves or edge inference using next-generation Blackwell-based GPU platforms (the likely successors to today’s Ada Lovelace-class workstations).
💵 OpEx Flexibility vs. TCO Predictability
Cloud: Pay-as-you-go sounds appealing early. But large-scale training, data egress, and inferencing costs can quickly spiral.
Dell (via APEX): Brings financial predictability and performance optimization — but demands upfront planning from platform teams.
Hybrid AI, When Done Right
Most enterprises will use both. But hybrid isn’t automatic — and it’s never free.
Here’s what hybrid AI looks like when designed well:
Train at Scale, Serve with Precision: Run large multi-week foundation model training jobs in SageMaker. Then distill and fine-tune a smaller, specialized model on a Dell AI Factory system using Blackwell-class GPUs for ultra-low latency inference behind the firewall.
Orchestrate Across Boundaries: Use Amazon Bedrock’s agentic workflows to automate a complex business process — but call into a Dell-hosted RAG system to retrieve data that legally can’t leave the premises.
These aren’t exceptions. They’re becoming the rule.
And platform engineers are the ones connecting it all.
Who owns the integration? Who supports it, governs it, and explains it to the business?
That’s where strategy meets platform engineering.
Dell’s Fourth Cloud, Quietly Under Construction
Dell hasn’t said this out loud yet — but we’ve raised it directly with their leadership:
Private Cloud Automation + AI Factory = The foundation of Dell’s Fourth Cloud thesis.
Programmable, sovereign infrastructure. Delivered with cloud-like agility.
It’s the direction many enterprise IT shops are trying to head — even if the vocabulary isn’t consistent yet.
This is about meeting the enterprise where it is:
With data gravity
With security obligations
With real infrastructure that needs real automation
And it’s a viable alternative to public cloud vendor lock-in — not just for cost, but for long-term control.
Final Word: You’re Not Picking a Product — You’re Defining a Platform
This isn’t about SageMaker vs. Dell.
It’s about choosing — and governing — your golden path.
Cloud-native tools offer abstraction. Infrastructure-centric approaches offer control.
You will need both.
But the success of your AI initiatives won’t come from the models.
It will come from the path your developers take to get there — and the platform your team builds to support them.
Your AI platform isn’t the product.
The golden path is.
📩 Want to talk through what this golden path looks like in your environment?
I’d love to hear what you're building — just shoot me a note: keith@advbench.com