In 2025, every cloud giant offers a “ready-to-use” platform for running LLMs in enterprise environments. But choosing a cloud isn’t a technical decision — it’s a strategic one. A wrong move today leads to vendor lock-in, rising TCO, and compliance risks tomorrow.
I’m an independent architect — not tied to AWS, Google, or Microsoft. Below is a candid comparison based on real deployments in fintech, healthcare, and the public sector.
🔍 Key Selection Criteria: Beyond Marketing Hype
Most comparisons focus on models. But what truly matters for business:
- Security & regulatory compliance (GDPR, HIPAA, Israel’s Privacy Protection Law, Russia’s FZ-152);
- Data control — do your inputs stay within your perimeter or leak into third-party processes?
- Architectural flexibility — can you swap models, add custom ones, or integrate with MS SQL and internal APIs?
- Hidden costs — not just per-token pricing, but engineering overhead, monitoring, and maintenance.
⚖️ Platform Comparison by Key Parameters (2025)
☁️ AWS Bedrock
- Models: Anthropic, Meta (Llama), Cohere, Amazon Titan — broad choice, no single-vendor lock-in.
- Data: Never leaves your AWS account. Full control via VPC and IAM policies.
- Compliance: Strong HIPAA/GDPR support. Flexible enough for Israel and Russia with proper configuration.
- Integration: Seamless with RDS, S3, Lambda. Works well in hybrid environments.
- Pricing: Pay only for usage. No idle charges or resource reservations.
🟣 Google Vertex AI
- Models: PaLM 2, Gemma, Mistral, partial Llama access — but some models are gated.
- Data: Processed within Google Cloud, but may be used for analytics by default — privacy requires manual hardening.
- Compliance: Excellent for GDPR, weaker for HIPAA. Challenging for Israeli and Russian regulatory frameworks.
- Integration: Optimal inside Google’s ecosystem. Integrating with MS SQL or legacy systems requires extra layers.
- Pricing: High total cost: training, deployment, experiments, and monitoring are billed separately.
🔵 Azure OpenAI
- Models: Only OpenAI’s GPT-4 Turbo, GPT-4o, etc. No access to Llama, Mistral, or other open-weight models.
- Data: ⚠️ By default, inputs may be used to improve OpenAI’s models (U.S.-based). Opt-out is possible but not obvious.
- Compliance: Certified for GDPR/HIPAA, but remains under U.S. jurisdiction and OpenAI’s policy control.
- Integration: Perfect for full Microsoft-stack shops (Azure AD, Azure SQL). Outside that ecosystem — compromises abound.
- Pricing: Highest per-token cost. Enterprise agreements often require reserved capacity (“pay for idle”).
💡 What Vendors Don’t Tell You
- Azure OpenAI is an API — not a platform. You can’t fine-tune GPT-4, can’t switch models, and are fully dependent on OpenAI’s decisions in San Francisco.
- Vertex AI looks powerful in demos, but true data isolation demands deep expertise in IAM, VPC, and DLP. Many teams accidentally violate privacy policies.
- AWS Bedrock offers the most flexibility — but requires mature cloud practices. If your infrastructure is fragmented, rollout takes time.
✅ How to Choose — Step by Step
- Map where your data lives. If you’re all-in on Azure, Azure OpenAI may make sense. For hybrid setups, Bedrock is safer.
- Review regulatory requirements. In Israel, Russia, or healthcare — avoid platforms that route data outside your jurisdiction.
- Assess your team’s maturity. Bedrock and Vertex need DevOps. Azure OpenAI is easier to start with but harder to scale securely.
- Calculate 2-year TCO. Include engineering hours, monitoring, training, and downtime risk — not just tokens.
📬 Why I Recommend a Hybrid Approach
In most engagements, I advise avoiding single-platform dependency. For example:
- Use Bedrock for sensitive document processing (full data control);
- Leverage Vertex AI for multimodal tasks (PDFs, image analysis);
- Avoid Azure OpenAI if you need sovereignty from U.S.-based AI providers.
This approach takes slightly more effort upfront — but delivers long-term flexibility, compliance, and resilience.
📬 How I Can Help
I’m Emil Slavin, an independent IT architect with 20+ years of experience in enterprise systems. I help CTOs and CIOs:
- Conduct vendor-neutral AI platform audits;
- Design hybrid, multilingual (English/Hebrew/Russian) AI architectures;
- Integrate AI with your MS SQL, WebForms, and legacy systems — without lock-in.
Don’t buy a cloud based on hype. Choose architecture based on strategy.
