Stop Running Blind AI RFPs: Introducing the 4+1 AI Platform RFP Framework
Most enterprise IT teams don’t realize this, but running a proper technology RFP easily costs $50,000 or more.
You either see that invoice directly, or it hides inside:
Analyst subscriptions
Advisory retainers
Consulting packages
Months of internal staff time trying to sanitize vendor-provided templates
One way or another, you pay for it — and most RFPs still fail to evaluate the things that actually matter in modern AI platforms.
And vendors?
They’re more than happy to hand you a “free” RFP that—shockingly—tilts the entire process toward their product.
By the time you’ve edited it enough to feel neutral, you’ve already absorbed their worldview, their assumptions, their definition of an “AI platform,” and their blind spots. You end up comparing apples vs. orchestras vs. power grids, depending on which vendor wrote the template.
This is how enterprises get locked in before they ever negotiate pricing.
This is how avoidable risks make their way into production.
This is how “AI platforms” become multi-year regrets.
So I decided to build something different.
Something that starts from your architectural bias, not the vendor’s.
And I’m giving it away.
Why I Built the 4+1 AI Platform RFP Framework
When I published the 4+1 Layer AI Infrastructure Model, it wasn’t intended to be a cute thought experiment. It came out of real-world pain — specifically, the pain of moving a cloud-native AI application off a hyperscaler and onto a DGX-based environment.
Same code. Same models. Same use case.
Completely different behavior.
Not because of compute.
But because hyperscalers silently provide a Reasoning Plane that enterprises don’t see, don’t control, and don’t know they’re relying on.
When you leave the cloud, that layer goes missing.
That’s when the architecture collapses.
Once I named that missing layer — Layer 2C, the Agentic Infrastructure / Reasoning Plane — everything else snapped into place. A layered model brings order to what vendors keep insisting is a monolithic “AI platform.”
From there, the next logical step was to turn that model into something usable by CIOs, CTOs, architects, and procurement teams.
That’s what this RFP is.
What This RFP Actually Does
Most AI vendors talk in abstractions that don’t map cleanly to real infrastructure. They blend runtime with orchestration with governance with storage with agents. It makes their diagram look powerful and your evaluation process impossible.
The 4+1 RFP forces vendors to declare, explicitly:
Which layers they cover.
Which layers they assume you will cover.
Which layers they outsource to someone else.
The RFP breaks AI platforms into the layers that actually determine success:
Layer 0: Compute & Network
Layer 1A–1C: Storage, Retrieval, Pipelines
Layer 2A–2C: Control Plane, Execution Plane, Reasoning Plane
Layer 3: Copilots & applications
Cross-cutting: Observability, FinOps, compliance, reliability
This structure exposes hidden assumptions and makes vendor claims falsifiable.
It prevents someone from calling themselves an “AI platform” simply because they have a vector database, or a model server, or a chat UI.
It stops the hand-waving.
The Four Strategic Risks Most RFPs Never Ask About
I put these right at the beginning of the RFP — before any architecture, any vendor questions, any model explanation — because these are the risks with the biggest long-term consequences.
1. Vector Lock-In
If you leave the vendor, can you take your embeddings with you?
Or will you need to re-embed terabytes of data at enormous cost?
2. Autonomous Compliance Liability
If a reasoning engine (Layer 2C) decides to move a PII-heavy workload to a cheaper region, and that violates residency laws — who is responsible?
Vendors love to say “you misconfigured your policy.”
Regulators don’t care.
3. Policy DSL Traps
If your reasoning logic is expressed in a proprietary DSL, you’re locked in for years.
Open standards matter.
4. Model Contamination & Data Rights
Does the vendor reserve the right to use your telemetry, prompts, or embeddings to “improve” their own models?
If you can’t opt out without breaking the product, you don’t own your AI.
These four questions alone are worth more than most RFP processes I’ve seen in the last decade.
Why Give This Away for Free?
This is the part people misunderstand.
I’m not a consultant selling templates.
I’m an advisor — and my credibility only grows as my frameworks become widely adopted.
I want this RFP to become a standard, not a product.
The more enterprises using the 4+1 model
The more architects who rely on layered evaluation
The more vendors forced to map their offering to a common structure
The more the market gravitates toward clarity and away from hype
…the more valuable my work becomes.
Because at that point, I’m not selling templates or workshops.
I’m shaping how the industry thinks about AI platforms.
That is far more important than a paywall or a download form.
So you get the entire RFP — a process worth ~$50,000 of advisory time — with a single click.
No email capture.
No signup.
No “contact sales.”
No funnel.
Just value.
Who This RFP Is For
This is for you if:
You’re evaluating AI platforms
You’re running a formal RFP and don’t want vendor-written documents
You’re building an internal AI platform and want structure
You’re a CTO trying to map capabilities to outcomes
You’re a procurement team tasked with “de-risking AI purchases”
You’re a vendor trying to understand how enterprise buyers actually think
This RFP gives every stakeholder a common language.
What’s Included
Here’s a taste of what’s inside:
A full question set for all layers
Not “Do you support RAG?”
But questions about:
governance metadata exposure
retrieval portability
telemetry freshness for reasoning
kill-switch architecture
cross-region safety
identity propagation
policy hysteresis
decision audit trails
multi-model routing
pipeline lineage
Questions that actually matter.
The 2C Differentiation Table
This is the section vendors hate and architects love — because it draws a bright line between:
Kubernetes operators
Autoscalers
Rule engines
Actual multi-objective reasoning layers
Red Flags Checklist
A one-page sanity check for procurement and legal.
Glossary
Clear definitions for:
Reasoning Plane
Execution Plane
Hysteresis
Kill Switch
Dry-run mode
Vector lock-in
Model contamination
Policy DSLs
GitOps for policy
And more
Deployment Model Guidance
How the layers behave differently on-prem, in hybrid, at the edge, and across clouds.
Use-case mapping
How to align platform capabilities to real business outcomes.