---
name: cloud-vendor-comparison-and-selection
description: "Produces a weighted cloud vendor scoring matrix comparing hyperscalers and private cloud options across capability fit, pricing model, security and compliance, geographic availability, ecosystem maturity, and lock-in risk — culminating in a ranked recommendation with migration and exit considerations. Use when selecting a cloud provider, consolidating multi-cloud spend, evaluating a hyperscaler switch, or building a vendor decision document for executive or procurement sign-off."
argument-hint: "[workload-profile] [vendor-shortlist]"
allowed-tools: ["Read"]
---

# Cloud Vendor Comparison and Selection

## Purpose
Produce a defensible, weighted comparison of candidate cloud vendors against the organization's actual workload and compliance requirements, ending in a ranked recommendation that procurement and executives can sign off on. Replaces vendor-marketing-driven decisions and gut feel with a transparent scoring model that surfaces lock-in risk and total cost — not just sticker price.

## When to Use
Invoke this skill when the user asks to:
- Select a primary cloud provider for a new platform or workload migration
- Compare AWS, Azure, GCP, Oracle Cloud, or a private/sovereign cloud option side by side
- Consolidate multi-cloud spend onto fewer providers and justify the choice
- Evaluate switching hyperscalers or adding a second cloud for resilience or compliance
- Build a cloud vendor decision document for a steering committee or procurement review
- Assess vendor lock-in risk before committing to a multi-year enterprise agreement

## Inputs Required
Before starting, collect:
- **Workload profile**: What's being deployed — compute-heavy, data/analytics, AI/ML training, transactional apps, legacy lift-and-shift, etc.
- **Vendor shortlist**: Which providers to compare (default to AWS, Azure, GCP if none given; add private/sovereign cloud only if named)
- **Compliance requirements**: Regulatory regimes in scope — GDPR, HIPAA, SOC 2, FedRAMP, PCI-DSS, data residency mandates
- **Geographic footprint**: Regions where workloads and users must be served, and any data-residency constraints
- **Existing estate**: Current cloud/on-prem investments, incumbent vendor relationships, and team skill concentration
- **Budget and term**: Approximate annual cloud budget and contract term being considered (1/3/5 year)
- **Strategic priorities**: Rank what matters most — cost, time-to-market, AI capability, sovereignty, multi-cloud portability

If criteria weights aren't specified, use the default weighting in Step 1 and flag it as an assumption the buyer should confirm.

## Instructions

### Step 1: Establish the Weighted Criteria Model
Score each vendor 1–5 on six criteria, then apply weights. Use this default weighting unless the buyer's strategic priorities dictate otherwise:

| Criterion | Default Weight | What a 5 looks like |
|---|---|---|
| Capability Fit | 25% | Native services directly match the workload profile with minimal custom engineering |
| Pricing Model | 20% | Predictable, discountable, favorable egress/commit terms for this usage pattern |
| Security & Compliance | 20% | All in-scope certifications held; controls map cleanly to requirements |
| Geographic Availability | 15% | Regions and AZs in every required location with data-residency guarantees |
| Ecosystem & Skills | 10% | Deep partner network, mature tooling, abundant talent the team already has |
| Lock-In Risk (inverted) | 10% | Open standards, portable services, low egress cost, clear exit path |

Adjust weights to reflect stated priorities (e.g., a sovereignty mandate pushes Security & Compliance and Geographic to 25%+ each). State the final weighting explicitly before scoring.

### Step 2: Score Capability Fit
Map the workload profile to each vendor's service catalog:
- **Compute/networking**: instance flexibility, serverless maturity, networking primitives
- **Data & AI**: managed databases, warehouse, ML/LLM tooling relevant to the profile
- **Operational gaps**: where the workload would require self-managed engineering vs. a native service
- Note any single service that is a decisive differentiator (e.g., a specific AI accelerator, a managed database with no equivalent elsewhere)

### Step 3: Score Pricing Model
Go beyond list price:
- **Usage pattern fit**: spot/reserved/committed-use discounts against this workload's profile
- **Egress and inter-region cost**: a primary lock-in and surprise-bill driver — score it explicitly
- **Commitment terms**: enterprise discount programs, ramp deals, and break clauses for the proposed term
- **TCO estimate**: produce a rough annual TCO band per vendor for the stated budget and term, noting the largest cost variable

### Step 4: Score Security & Compliance
For each in-scope regime, mark each vendor: **Met / Met-with-config / Not Met**.
- List the certifications each vendor holds that satisfy the requirements
- Flag any requirement only one vendor satisfies — this can be decisive regardless of total score
- Note shared-responsibility implications: what the vendor covers vs. what the buyer's team must operate

### Step 5: Score Geographic Availability
- Confirm every required region has a presence (with AZ count) for each vendor
- Flag any required location served by only some vendors
- Assess data-residency guarantees and sovereign-region options against the compliance footprint

### Step 6: Score Ecosystem & Skills
- Partner/SI network depth for this workload type
- Maturity of IaC, observability, and migration tooling
- **Team skills match**: weight existing in-house expertise heavily — retraining cost is real and recurring

### Step 7: Score Lock-In Risk (Inverted)
Higher score = lower lock-in. Assess:
- Reliance on proprietary, non-portable services vs. open standards/Kubernetes/open data formats
- Egress cost as an exit tax
- Realistic exit effort and timeline if the relationship must end
- Multi-cloud portability of the proposed architecture

### Step 8: Build the Scoring Matrix and Rank
- Produce the full matrix: vendors as columns, six criteria as rows, raw score (1–5), weighted score, and weighted total per vendor
- Rank vendors by weighted total
- Add a **decisive-factor callout**: any single criterion (e.g., a missed compliance regime) that overrides the numeric ranking

### Step 9: Recommendation and Decision Guidance
- Name the recommended primary vendor with a 3–4 sentence rationale tied to the highest-weighted criteria
- State the runner-up and the specific condition under which it would win instead
- Give a single-paragraph **lock-in mitigation note**: what to negotiate or architect now to keep an exit path open
- Note whether a multi-cloud or fallback posture is warranted given the priorities

## Output Format
- **Structure**: Weighting model → six scored criteria sections → consolidated scoring matrix → ranked recommendation
- **Length**: 1,100–1,500 words
- **Format**: Markdown with two tables (weighting model and final scoring matrix); scored sections as structured bullets; recommendation as prose
- **Sections**: Criteria & Weights → Capability Fit → Pricing → Security & Compliance → Geographic → Ecosystem & Skills → Lock-In Risk → Scoring Matrix → Recommendation & Mitigation
- **Tone**: Enterprise architect briefing a steering committee — analytical, vendor-neutral, decision-oriented; no marketing language for any provider

## Quality Checklist
Before delivering output, verify:
- [ ] All six criteria are scored 1–5 for every vendor on the shortlist
- [ ] Final criteria weights sum to 100% and are stated before scoring begins
- [ ] The scoring matrix shows raw scores, weighted scores, and a weighted total per vendor
- [ ] Every in-scope compliance regime is marked Met / Met-with-config / Not Met per vendor
- [ ] Pricing assessment addresses egress cost explicitly, not just compute list price
- [ ] A single primary recommendation is named with a runner-up and a switch condition — no hedging

## Notes
- The numeric ranking is a guide, not a verdict — a single unmet compliance requirement or missing region can override a higher total score. Always surface these as decisive-factor callouts.
- Egress cost and proprietary-service dependence are the two most underweighted factors in most cloud decisions; treat them as first-class, not footnotes.
- Existing team skills are a recurring cost center — a "better" platform the team can't operate often loses on TCO once retraining and ramp time are counted.
- If the buyer's priorities clearly conflict with the default weighting, adjust the model and show both the default and adjusted rankings so the tradeoff is visible.

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## How to use this skill

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1. Drag this file into a new chat, or copy and paste its contents
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