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You don’t need all three clouds. You just need the right cloud for your workload.
In 2025, with rising pressure on cost, data locality, generative AI, and regulatory compliance, choosing the wrong cloud can cost you millions.
I’ve seen clients scratch their heads over this for years — and I’m going to cut through the noise. By the end of this post, you’ll know exactly which cloud to pick (or combine) for your enterprise use case.
Table of Contents
- What’s changed since 2023
- The decision matrix: How to choose
- Deep dive: AWS, Azure, GCP — pros, cons, ideal fits
- The hybrid / multi-cloud strategy
- Migration pitfalls & best practices
- Real-world example (EvansSys client case)
- Verdict & roadmap
- CTA & next steps
1. What’s changed since 2023 (and why 2025 is different)
- AI / generative readiness: All three providers now bake in AI/ML tooling. But the ease-of-use integration differs.
- Data residency & sovereignty pressures: Governments demand data stay in region; so multi-region and sovereign cloud matters more.
- Interoperability & open standards: There’s more pressure to avoid lock-in; tools like Anthos, Arc, and OpenAI ecosystem plays a role.
- Cost pressure: Cloud providers are getting more aggressive with committed use discounts and spot pricing. You can’t ignore total cost of ownership (TCO).
2. The decision matrix — how we at EvansSys evaluate clouds for clients
Whenever we evaluate “which cloud,” we score based on:
| Criteria | Weight | What We Look At | Typical Differentiator |
|---|---|---|---|
| Core services breadth & maturity | 20% | Tooling, PaaS, managed DBs, serverless | AWS usually leads, Azure strong in enterprise integration |
| Security, compliance, certifications | 15% | FedRAMP, HIPAA, ISO, local regs | GCP lags in some regions |
| Data & analytics / AI tooling | 15% | AutoML, tensor services, integration with LLMs | GCP & Azure pushing hard here |
| Networking & hybrid connectivity | 15% | VNet, private link, ExpressRoute, Direct Connect | Azure has advantage in Microsoft shops |
| TCO & commitment pricing | 15% | Discounts, reserved, spot, auto-scaling policies | All 3 competitive — details matter |
| Support & ecosystem / partner network | 10% | Partner tooling, managed service providers | AWS has the largest ecosystem |
| Regional coverage & latency | 10% | Edge presence, data center proximity | Azure often strongest in enterprise locations |
| Migration & lock-in risk | 10% | Ease of moving out, open standards usage | Tools like Terraform, Anthos, etc. |
We run a score per client (workloads, compliance needs, growth plans) and pick or design a hybrid/multi-cloud architecture accordingly.
3. Deep dive: AWS / Azure / GCP
AWS (Amazon Web Services)
Strengths:
- Most mature, broadest service catalog
- Best ecosystem, third-party integrations
- Strong global reach and availability zones
Weaknesses: - Pricing complexity (many “gotchas”)
- Steeper learning curve
- Some rigidity in regional data constraints
Best for enterprises that need “everything in one cloud,” wide global footprint, and heavy third-party tools.
Azure (Microsoft Azure)
Strengths:
- Seamless integration with Microsoft stack (AD, Office 365, Windows)
- Great hybrid tools (Azure Arc)
- Strong enterprise relationships
Weaknesses: - Some consistency issues across regions
- Complexity in non-Windows workloads
Best for enterprises that are already Microsoft-centric, need hybrid, or want tight integration with existing systems.
GCP (Google Cloud Platform)
Strengths:
- Leading in data, analytics, ML / AI tooling
- Developer-friendly, good open standards orientation
- Often has cost advantages in certain workloads
Weaknesses: - Smaller enterprise footprint / ecosystem
- Fewer global zones in some geographies
Best for enterprises that prioritize advanced AI / ML, big data workloads, and want less vendor lock-in.
4. Hybrid & Multi-Cloud: The middle path (and why we lean this in many cases)
Full mono-cloud rarely makes sense nowadays. Instead:
- Use a primary cloud (one of the three) for mainstream compute + services.
- Use secondary clouds for failover, disaster recovery, or specific workloads (e.g. AI, analytics).
- Use cloud abstraction / control plane tools (Anthos, HashiCorp, Azure Arc, etc.) to minimize lock-in.
- Adopt policy as code, infrastructure as code, and telemetry-first designs.
5. Migration pitfalls & best practices
- Don’t try to lift-and-shift everything at once. Pick a pilot.
- Take stock of dependencies (on-prem, legacy apps) first.
- Automate everything (CI/CD, infra as code).
- Plan for rollback / fallback.
- Train your ops team ahead of migration.
- Use hybrid connectivity (VPN, ExpressRoute, Direct Connect) strategically.
6. Real-world example (EvansSys client case)
Client: A global retail chain with operations in 15 countries
Challenge: Outdated datacenters, latency issues, compliance in multiple regions
Solution by Boykin:
- We ran multi-cloud POC in AWS + Azure
- Migrated core services to Azure (because of MS stack alignment)
- Kept heavy data pipelines and AI workloads in GCP for analytics edge
- Built global edge network overlays to reduce latency
Results: - 35% faster page loads
- 40% lower total cost vs prior vendor guess
- Full compliance across regions
- Seamless failover between clouds
(We anonymize client names unless approved — but these are real patterns we execute)
7. Verdict & roadmap for your business
If I were you (or your CTO), here’s how I’d decide:
- If you’re heavily Microsoft / enterprise oriented → Azure primary + AI workloads elsewhere
- If you’re data/AI-first startup moving enterprise → GCP-first, AWS backup
- If you’re operational, global, need scale and integrations → AWS-first, hybrid fallback
Roadmap:
- Do a 2–3 cloud pilot (non-critical workloads)
- Implement infra-as-code, build your automation pipelines
- Migrate selectively, monitor everything
- Revisit annually — technology, pricing, features evolve fast
You don’t have to guess. At EvansSys, we’ve architected 100+ enterprise cloud transitions across AWS, Azure, GCP. Let us help you build the future-proof cloud roadmap that balances performance, cost, and resilience.
Book a free 30-minute cloud strategy session with us today, and we’ll run a cloud-fit score for your specific workloads (yes, no sales fluff).
