Picking the right cloud AI service comes down to more than raw model performance. Your decision hinges on how well it meshes with your current stack, what it costs, and whether it meets your compliance needs.
Having shipped production workloads on each of these platforms, here's what I've learned.
The Quick Version
| Platform | Ideal Use Case | What Sets It Apart |
|---|---|---|
| AWS Bedrock | Switching between multiple models | Smart routing that picks the right model automatically |
| Azure OpenAI | Enterprise access to GPT | Tight Microsoft 365 connectivity |
| Gemini API | Processing huge documents | Context window up to 2M tokens |
AWS Bedrock
Bedrock is Amazon's managed gateway to foundation models from Anthropic, Meta, Mistral, Cohere, and others—all through one unified API.
Why it stands out:
- Access Claude, Llama, Mistral, and Stable Diffusion without juggling multiple integrations
- Automatic prompt routing selects the most cost-effective model for each request (potential 30% savings)
- Plugs directly into S3, Lambda, and SageMaker
- Native RAG support with built-in vector storage
Pricing snapshot (Claude 3.5 Sonnet): $3/million input tokens, $15/million output tokens. Batch processing cuts costs in half.
Best fit: Teams already on AWS who want model flexibility and strong compliance credentials.
Azure OpenAI
Microsoft's enterprise-grade wrapper around OpenAI's models, with security and governance baked in.
Why it stands out:
- Direct access to GPT-4o, o1, DALL-E 3, and Whisper
- Seamless hooks into Teams, Power Platform, and the broader Microsoft ecosystem
- Your data stays private and isn't used for training
- Provisioned Throughput Units (PTUs) for predictable billing
Pricing snapshot (GPT-4o): $2.50/million input tokens, $10/million output tokens.
Best fit: Organizations already running Microsoft infrastructure who specifically need OpenAI models.
Gemini API
Google's multimodal platform with an industry-leading context window and native support for text, images, audio, and video.
Why it stands out:
- 2M token context—roughly 8x what GPT-4 offers
- True multimodal processing without preprocessing steps
- Built-in web search grounding for real-time information
- Generous free tier (1,500+ daily requests)
Pricing snapshot (Gemini 2.5 Pro): $1.25/million input tokens (under 200K context), $10/million output tokens.
Best fit: Document-heavy applications, multimodal use cases, or teams prototyping on a budget.
How to Decide
- Already deep in AWS? → Bedrock
- Need GPT-4 specifically? → Azure OpenAI
- Processing documents over 200K tokens? → Gemini
- Early-stage or budget-conscious? → Gemini's free tier
- Want to experiment across models? → Bedrock
Saving Money
- Bedrock: Use batch mode and smart routing; enable prompt caching
- Azure: Reserve PTUs for steady workloads; use batch API for non-urgent tasks
- Gemini: Max out the free tier during development; use Flash models when speed matters less
Bottom Line
Each platform excels in different scenarios. Bedrock offers unmatched model flexibility. Azure OpenAI delivers the smoothest experience for Microsoft-centric teams. Gemini's massive context window changes what's possible for document analysis.
No single platform wins across the board—your best choice depends on your existing infrastructure, specific model requirements, and budget. And honestly? You'll probably end up using more than one. 😅
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