Enterprise AI Platforms

Enterprise AI platforms—Azure AI Foundry, AWS Bedrock, and Google Vertex AI—provide unified, managed services for deploying multiple AI models with enterprise-grade security, compliance, and integration. Unlike calling individual provider APIs (OpenAI, Claude, Gemini directly), these cloud platforms offer centralized governance, multi-model access, cloud ecosystem integration, and enterprise support. They’re designed for organizations wanting AI capabilities embedded within their existing cloud infrastructure with consistent security, compliance, and operational frameworks.

The choice between platforms typically aligns with existing cloud investment rather than AI-specific features—Microsoft-centric organizations favor Azure AI Foundry, AWS customers choose Bedrock, and Google Cloud users select Vertex AI. However, understanding each platform’s strengths, model availability, and unique capabilities enables informed decisions for multi-cloud or greenfield AI strategies.

Platform Overview

Azure AI Foundry (Microsoft)

What it is: Microsoft’s unified AI platform providing access to 1,800+ models including exclusive OpenAI partnership, integrated with Azure cloud services.

Model Availability:

• OpenAI: GPT-4, GPT-5, o-series (primary partnership—earliest access)
• DeepSeek: R1 (reasoning model)
• Open-source: Llama, Mistral
• 1,800+ model catalog: Broadest selection

Key Features:

• Direct OpenAI access (Azure OpenAI Service)
• Azure AI Content Safety (built-in guardrails with opt-out flexibility)
• Deep Microsoft 365, Dynamics 365, Active Directory integration
• Azure AI Search for retrieval-augmented generation ( RAG

  Retrieval-Augmented Generation (RAG)

  See this term in the glossary
  )
• Rigorous red teaming and safety evaluations
• MLOps integration with Azure ML

Pricing:

• Model pricing varies (typically slight premium over direct API)
• Unified Azure billing
• Enterprise agreements available

Best For:

• Microsoft-centric organizations (Azure, Microsoft 365, Active Directory)
• Need OpenAI models with enterprise SLA/BAA
• Want largest model catalog (1,800+)
• Prioritize safety guardrails and compliance frameworks

Strengths:

• Broadest model selection (1,800+ models)
• Best OpenAI access (primary partnership)
• Microsoft ecosystem integration
• Strong enterprise compliance (HIPAA, SOC 2, ISO 27001)
• Unified platform for governance across models

Limitations:

• Best value for Microsoft-invested organizations
• Pricing premium vs direct APIs
• Claude availability limited vs AWS Bedrock

AWS Bedrock (Amazon)

What it is: Amazon’s multi-vendor AI model marketplace providing managed access to leading models with serverless deployment and deep AWS integration.

Model Availability:

• Claude (Anthropic): Primary partnership—full access to Sonnet 4.5, Opus, Haiku
• Llama (Meta): All variants
• Cohere, AI21 Labs, Stability AI, Amazon Titan
• Multi-vendor approach (no single provider dominance)

Key Features:

• Fully managed and serverless
• AgentCore for enterprise-grade agent systems (Oct 2025)
• Robust access management via IAM
• Deep AWS service integration (Lambda, S3, DynamoDB, etc.)
• Knowledge Bases for RAG

  Retrieval-Augmented Generation (RAG)

  See this term in the glossary
• Guardrails for responsible AI

Pricing:

• Model-specific pricing (varies by provider)
• Pay-per-use (no minimum commitments)
• Unified AWS billing

Best For:

• AWS-centric organizations
• Claude preferred for coding/analysis use cases
• Multi-model strategies requiring vendor diversity
• Serverless deployment preference

Strengths:

• Best Claude access (primary partnership for Anthropic)
• Multi-vendor flexibility (avoid single model dependency)
• Serverless simplicity
• Deep AWS integration
• Strong security and compliance (AWS frameworks)

Limitations:

• No direct OpenAI access (use Azure for GPT models)
• Smaller model catalog than Azure AI Foundry
• Best value for AWS-invested organizations

Google Vertex AI (Google)

What it is: Google’s unified ML platform providing native Gemini access plus Model Garden for third-party and open-source models.

Model Availability:

• Gemini (Google): Primary offering—2.5 Pro, Flash, all variants
• Claude: Available via Model Garden
• Llama, Mistral: Via Model Garden
• Open-source models: Broad selection (BERT, T5, etc.)

Key Features:

• Native Gemini access (1M context, multimodal)
• Broadest fine-tuning suite (prompt tuning, adapter tuning, full fine-tuning)
• Deep Google Cloud integration (BigQuery, Cloud Storage)
• AutoML capabilities
• Advanced MLOps and model monitoring
• Unified data and AI platform

Pricing:

• Model-specific pricing
• Generally competitive with direct APIs
• Google Cloud billing integration

Best For:

• Google Cloud Platform organizations
• Gemini preferred (1M context, multimodal, price-performance)
• Data-heavy ML workflows (BigQuery integration)
• Organizations requiring extensive model customization/fine-tuning

Strengths:

• Best Gemini access (native Google models)
• Advanced fine-tuning (most comprehensive options)
• Unified data+AI platform
• Multimodal excellence (Gemini’s video/audio capabilities)
• Strong MLOps capabilities

Limitations:

• Best value for Google Cloud customers
• No OpenAI models (use Azure for GPT)
• Gemini-centric model selection (intentional strategy)

Platform Comparison Matrix

Decision Framework

Choose Azure AI Foundry When:

Primary Criteria:

• Microsoft-centric organization (Azure, Microsoft 365, Active Directory)
• Need OpenAI models (GPT-4, GPT-5, o-series) with enterprise controls
• Want largest model catalog (1,800+ models)
• Unified Microsoft ecosystem prioritized

Use Cases:

• Organizations already on Azure infrastructure
• Enterprises using Microsoft 365 Copilot + custom AI (unified strategy)
• Need HIPAA-compliant OpenAI access
• Multi-model experimentation (1,800+ options)

Strategic Fit:

• Single Microsoft vendor relationship
• Unified security/compliance framework
• Deepest OpenAI integration

Choose AWS Bedrock When:

Primary Criteria:

• AWS-centric organization (heavy Lambda, S3, DynamoDB usage)
• Claude preferred for coding, analysis, or agentic workflows
• Multi-vendor strategy (avoid single AI provider dependency)
• Serverless deployment preference

Use Cases:

• Software development teams favoring Claude (77.2% SWE-bench)
• AWS-native applications requiring AI
• Organizations wanting vendor diversity
• Serverless architecture patterns

Strategic Fit:

• Deep AWS ecosystem integration
• Multi-vendor flexibility
• Claude as best coding model

Choose Google Vertex AI When:

Primary Criteria:

• Google Cloud Platform organization
• Gemini preferred (1M context, multimodal, price-performance)
• Data-heavy ML workflows (BigQuery, data science focus)
• Advanced fine-tuning and customization required

Use Cases:

• Data analytics teams using BigQuery extensively
• Multimodal applications (video/audio processing)
• ML teams requiring extensive model customization
• Cost-conscious with Gemini Flash’s exceptional price-performance

Strategic Fit:

• Unified data and AI platform
• Best fine-tuning capabilities
• Gemini’s unique strengths (context, multimodal, cost)

Hybrid and Multi-Cloud Strategies

Pattern 1: Best Model on Best Platform

Strategy:

• Azure AI Foundry: OpenAI models (GPT-4, o-series)
• AWS Bedrock: Claude (coding, analysis)
• Google Vertex AI: Gemini (large documents, multimodal, cost-sensitive)

Benefits:

• Access best model for each use case
• Avoid single-platform lock-in
• Optimize cost and capability

Challenges:

• Multiple vendor relationships
• Complex governance across platforms
• Higher operational overhead

Pattern 2: Primary + Specialized

Strategy:

• Primary platform aligned with cloud investment (e.g., Azure)
• Specialized access to models unavailable on primary (e.g., Claude on AWS for coding)

Benefits:

• Consolidated governance on primary
• Access specialized capabilities when justified
• Manageable complexity

Pattern 3: Workload Segregation

Strategy:

• Production workloads: Cloud platform with enterprise SLA
• Development/testing: Alternative platforms (Hugging Face, Together.ai)
• Sensitive data: Self-hosted (Llama, Mistral)

Benefits:

• Appropriate deployment model per workload
• Cost optimization
• Risk management

Enterprise Features Comparison

Key Insight: All three platforms provide enterprise-grade compliance and security. Choice based on cloud ecosystem fit rather than compliance gaps.

Pricing Considerations

General Pricing Patterns

Platform Premium:

• Cloud platforms typically charge 10-30% premium over direct API pricing
• Premium includes: enterprise SLA, compliance frameworks, integration, support

When Premium Justified:

• HIPAA/regulated data (need BAA from cloud provider)
• Enterprise support and SLA requirements
• Deep integration with cloud services
• Unified billing and governance

When Direct API Cheaper:

• Non-sensitive data
• Startups/SMBs without enterprise requirements
• Flexibility to switch providers easily

TCO Comparison Example (1B tokens/month)

Direct API (e.g., GPT-4o):

• Cost: ~$30-60/month input + $100-150/month output = $130-210/month
• Benefits: Lowest cost
• Limitations: No enterprise SLA, compliance framework

Cloud Platform (e.g., Azure OpenAI):

• Cost: ~$35-70/month input + $110-165/month output = $145-235/month
• Premium: ~$15-25/month (10-15%)
• Benefits: Enterprise SLA, BAA, compliance, integration

Value Analysis:

• For regulated industries (healthcare, finance): $15-25/month premium trivial vs compliance value
• For non-regulated startups: Direct API lower cost acceptable

Common Deployment Patterns

Enterprise Healthcare

Requirements: HIPAA, BAA, PHI protection

Recommendation: Cloud platform with BAA

• Azure OpenAI (if OpenAI models required)
• AWS Bedrock (if Claude preferred)
• Google Vertex AI (if Gemini suitable)

Avoid: Direct APIs (typically no BAA)

Global Enterprise (Multi-Cloud)

Requirements: Avoid vendor lock-in, multi-region

Recommendation: Multi-cloud with abstraction layer

• Azure AI Foundry: OpenAI access
• AWS Bedrock: Claude access
• Google Vertex AI: Gemini access
• Abstraction layer enables model switching

Startup (Fast, Cheap)

Requirements: Speed, low cost, flexibility

Recommendation: Direct APIs or alternative platforms

• Direct OpenAI/Claude/Gemini APIs
• Hugging Face for experimentation
• Migrate to cloud platform as enterprise needs grow

High-Volume Cost-Sensitive

Requirements: Minimize per-token costs

Recommendation: Self-hosted open-source

• Llama 4 on cloud VMs or on-premise
• Mistral (Apache 2.0)
• Supplement with cloud platforms for specialized needs

When to Use Each Platform

Azure AI Foundry Decision Criteria

Choose when:

• ✓ Already on Azure
• ✓ Microsoft 365/Active Directory integration needed
• ✓ OpenAI models required
• ✓ Want largest model catalog
• ✓ Unified Microsoft ecosystem

Avoid when:

• ✗ No Azure investment (prefer AWS/Google platform)
• ✗ Claude strongly preferred (use AWS Bedrock)
• ✗ Gemini primary requirement (use Vertex AI)

AWS Bedrock Decision Criteria

Choose when:

• ✓ Already on AWS
• ✓ Claude preferred (best coding model)
• ✓ Serverless architecture
• ✓ Multi-vendor strategy
• ✓ Deep AWS integration needed

Avoid when:

• ✗ OpenAI models required (use Azure)
• ✗ No AWS investment (different platform may be better)
• ✗ Gemini required (use Vertex AI)

Google Vertex AI Decision Criteria

Choose when:

• ✓ Already on Google Cloud
• ✓ Gemini capabilities suit needs (1M context, multimodal, cost)
• ✓ BigQuery integration valuable
• ✓ Advanced fine-tuning required
• ✓ Data science/ML ops focus

Avoid when:

• ✗ OpenAI required (use Azure)
• ✗ Claude strongly preferred (use AWS)
• ✗ No Google Cloud investment

Summary

Strategic Guidance:

1. Align with existing cloud investment — Platform choice typically follows infrastructure investment rather than AI-specific features

2. Model availability drives exceptions — If strongly prefer specific model unavailable on your primary cloud, consider multi-cloud strategy

3. All platforms enterprise-ready — Compliance, security, support comparable across Azure, AWS, Google

4. Cost premium justifiable for enterprise — 10-30% premium vs direct APIs worthwhile for compliance, SLA, integration

5. Hybrid strategies common — Many organizations use multiple platforms, leveraging each for specific strengths

The “best” enterprise AI platform is the one that aligns with your existing cloud infrastructure while providing access to models that fit your use cases. For most organizations, this means Azure for Microsoft shops, AWS for AWS users, and Google for Google Cloud customers—with multi-cloud strategies when specific model capabilities justify additional complexity.