TL;DR

• AWS Bedrock: Best for AWS-native teams, widest model selection (Claude, Llama, Titan), serverless. Rating: 4.4/5
• Azure AI Studio: Best for Microsoft ecosystem, strong enterprise tooling, Copilot integration. Rating: 4.3/5
• Google Vertex AI: Best for ML-heavy teams, superior AutoML, tightest Google Cloud integration. Rating: 4.2/5
• Pricing: Azure cheapest for low volume, AWS cheapest at scale (Reserved Instances)
• Recommendation: Choose based on existing cloud commitment; AWS if cloud-agnostic

Enterprise AI Agent Platforms Comparison

Deployed production agents on all three platforms. Here's what enterprise teams need to know.

Quick Comparison Matrix

AWS Bedrock

Overview

Amazon's managed service for foundation models. Launched Sept 2023, now enterprise-ready.

Model Selection: 9/10

Available Models:

• Anthropic Claude 3.5 Sonnet
• Meta Llama 3 70B
• Mistral Large
• Cohere Command R+
• Amazon Titan (text, embeddings, image)
• AI21 Jurassic
• Stability AI (image generation)

Advantage: Widest third-party model selection. Not locked into single provider.

Multi-Agent Orchestration: 7/10

Bedrock Agents feature:

• Define agent instructions
• Connect to Lambda functions for tool execution
• Sequential agent handoffs
• Knowledge base integration (vector search via OpenSearch)

Limitation: No native parallel execution. Must orchestrate via Step Functions.

Code Example:

import boto3

bedrock_agent = boto3.client('bedrock-agent')

response = bedrock_agent.create_agent(
    agentName='customer-support-agent',
    foundationModel='anthropic.claude-3-5-sonnet-20241022-v2:0',
    instruction='You are a customer support agent...',
    actionGroups=[{
        'actionGroupName': 'crm-tools',
        'actionGroupExecutor': {
            'lambda': 'arn:aws:lambda:us-east-1:123456789012:function:crm-tools'
        }
    }]
)

Pricing: 7/10

Claude 3.5 Sonnet on Bedrock:

• Input: $0.003/1K tokens
• Output: $0.015/1K tokens
• Same as Anthropic API pricing

Titan Embeddings:

• $0.0001/1K tokens (cheaper than OpenAI)

Cost Optimization:

• Provisioned Throughput (Reserved Instances): 30-50% savings for predictable workloads
• Model Distillation: Fine-tune smaller Titan models on Claude outputs

Monthly Cost (50K queries, avg 2K tokens):

• On-demand: ~£360
• Provisioned (3-month): ~£220

Security & Compliance: 10/10

• Data isolation: Inputs/outputs never used for training
• VPC deployment: Keep traffic within private network
• Encryption: At-rest (KMS) and in-transit (TLS)
• Compliance: SOC 2, HIPAA, GDPR, ISO 27001
• IAM integration: Fine-grained access control

MLOps Tooling: 7/10

• Monitoring: CloudWatch metrics (latency, tokens, errors)
• Logging: CloudTrail for audit logs
• Versioning: Model versioning via aliases
• A/B Testing: Manual setup via Lambda routing

Missing: Built-in experiment tracking, prompt versioning, evaluation framework.

Best For

✅ AWS-native organizations ✅ Teams wanting model flexibility (avoid vendor lock-in) ✅ Serverless-first architecture ✅ High compliance requirements (healthcare, finance)

❌ Teams needing rich MLOps (no built-in experiment tracking) ❌ Multi-cloud deployments (AWS-only)

Rating: 4.4/5

Azure AI Studio

Overview

Microsoft's unified AI development platform. Tight integration with Azure OpenAI Service.

Model Selection: 7/10

Available Models:

• OpenAI GPT-4 Turbo, GPT-4o
• OpenAI GPT-3.5 Turbo
• Meta Llama 3
• Mistral models
• Phi-3 (Microsoft's small model)

Advantage: Best OpenAI integration (often get new models first).

Limitation: Smaller selection than Bedrock. Claude not available.

Multi-Agent Orchestration: 8/10

Prompt Flow for visual orchestration:

• Drag-and-drop agent workflows
• Parallel execution supported
• Human-in-the-loop nodes
• Built-in evaluation metrics

Code Example:

from promptflow import PFClient

pf = PFClient()

# Deploy multi-agent flow
deployment = pf.deployments.create_or_update(
    name="support-agent-flow",
    flow="./support-flow",
    endpoint="https://support-agents.azurewebsites.net"
)

Advantage over Bedrock: Visual builder, easier for non-ML teams.

Pricing: 8/10

GPT-4 Turbo on Azure:

• Input: $0.01/1K tokens
• Output: $0.03/1K tokens

Cheaper than AWS/Google for low volume due to monthly free credits (£150/month for Enterprise customers).

Monthly Cost (50K queries, avg 2K tokens):

• On-demand: ~£450
• With Enterprise credits: ~£300

Unique advantage: Copilot Credits bundled with Microsoft 365 E5 licenses (some enterprises already paying).

Security & Compliance: 10/10

• Data residency: Choose region for data storage
• Private endpoints: VNet integration
• Encryption: Customer-managed keys (CMK) support
• Compliance: SOC 2, HIPAA, GDPR, ISO 27001, FedRAMP
• Content filtering: Built-in harmful content detection

Advantage: Entra ID (Azure AD) integration for SSO.

MLOps Tooling: 9/10

Best-in-class MLOps:

• Experiment tracking: Track prompts, parameters, outputs
• Model registry: Version control for models and prompts
• Evaluation: Built-in evaluation metrics (groundedness, relevance, coherence)
• Monitoring: Application Insights integration
• CI/CD: Azure DevOps integration

Example evaluation:

from promptflow.evals import RelevanceEvaluator

evaluator = RelevanceEvaluator()
results = evaluator(
    query="What is your refund policy?",
    response=agent_response,
    ground_truth=expected_response
)
# Returns relevance score 0-5

Best For

✅ Microsoft-centric organizations (365, Teams, Dynamics) ✅ Teams needing rich MLOps tooling ✅ OpenAI-first strategy ✅ Visual workflow builders (non-technical teams)

❌ Multi-model flexibility (limited compared to AWS) ❌ Cost-sensitive at high volume (more expensive than AWS Reserved)

Rating: 4.3/5

Google Vertex AI

Overview

Google Cloud's unified ML platform. Strongest for teams building custom models alongside agents.

Model Selection: 7/10

Available Models:

• Google Gemini 1.5 Pro (2M token context)
• Google PaLM 2
• Meta Llama 3
• Claude 3.5 Sonnet (via Model Garden)
• Mistral models

Unique advantage: Gemini 1.5 Pro's 2M context window (10x larger than competitors).

Multi-Agent Orchestration: 7/10

Vertex AI Agent Builder:

• Define agents with natural language instructions
• Connect to Google Search, Cloud Functions
• Sequential handoffs supported
• RAG integration with Vertex AI Search

Limitation: Less mature than Azure's Prompt Flow. No visual builder.

Code Example:

from google.cloud import aiplatform

agent = aiplatform.Agent.create(
    display_name="support-agent",
    model="gemini-1.5-pro",
    instruction="You are a customer support agent...",
    tools=[{
        "function_declarations": [{
            "name": "lookup_order",
            "description": "Lookup order by ID",
            "parameters": {...}
        }]
    }]
)

Pricing: 7/10

Gemini 1.5 Pro:

• Input (<128K): $0.00125/1K tokens (cheapest)
• Input (>128K): $0.0025/1K tokens
• Output: $0.005/1K tokens

Trade-off: Gemini cheaper than GPT-4, but GPT-4 more accurate on complex reasoning.

Monthly Cost (50K queries, avg 2K tokens):

• Gemini 1.5 Pro: ~£120
• Claude 3.5 on Vertex: ~£360

Unique pricing: Pay-per-use AutoML training (no upfront costs).

Security & Compliance: 10/10

• VPC Service Controls: Network perimeter security
• Customer-managed encryption keys (CMEK)
• Compliance: SOC 2, HIPAA, GDPR, ISO 27001
• Data residency: Regional control
• Workload Identity: Kubernetes-native auth

MLOps Tooling: 9/10

Strongest ML engineering features:

• Vertex AI Experiments: Compare model runs
• Feature Store: Centralized feature management
• Model Monitoring: Drift detection, skew detection
• Explainable AI: Understand model predictions
• Pipelines: Kubeflow-based orchestration

Advantage: Best for teams building custom models + agents together.

Best For

✅ GCP-native organizations ✅ ML-heavy teams (data scientists, ML engineers) ✅ Long-document processing (2M context window) ✅ AutoML + agents hybrid approach

❌ Non-ML teams (steeper learning curve than Azure) ❌ OpenAI lock-in (less integrated than Azure)

Rating: 4.2/5

Decision Framework

Choose AWS Bedrock if:

• Already on AWS
• Want model flexibility (avoid lock-in)
• High compliance requirements
• Serverless-first

Choose Azure AI Studio if:

• Microsoft ecosystem (365, Teams)
• Need best MLOps tooling
• OpenAI-first strategy
• Want visual workflow builder

Choose Google Vertex AI if:

• Already on GCP
• ML-heavy team
• Need 2M token context (Gemini)
• Building custom models + agents

Cost Comparison (50K queries/month)

Winner on cost: Google Vertex AI (Gemini 1.5 Pro) at £120/month.

Trade-off: Gemini less accurate than GPT-4/Claude for complex reasoning. Test on your use case.

Multi-Cloud Strategy

If deploying across clouds:

1. Use OpenAI API directly (works everywhere, but no enterprise features)
2. Abstract via LangChain (write once, deploy anywhere)
3. Build cloud-agnostic CI/CD (Terraform for infra, Docker for apps)

Cost: 10-20% engineering overhead for abstraction vs single-cloud.

Recommendation

Start with your existing cloud provider. Migration costs (re-architecting, re-training team, re-negotiating contracts) exceed platform differences.

If cloud-agnostic: Default to AWS Bedrock for widest model selection.

If Microsoft shop: Azure AI Studio (Copilot integration, MLOps tooling).

If GCP + ML-heavy: Vertex AI (best ML platform, cheapest with Gemini).

Sources:

• AWS Bedrock Documentation
• Azure AI Studio Docs
• Google Vertex AI Docs