VertexAI vs Bedrock vs Azure AI — Comparison

Indian enterprises choosing an AI platform in 2026 face a three-way decision between Google VertexAI, AWS Bedrock, and Azure AI. Each platform offers access to frontier LLMs, enterprise security features, and India-region data residency — but the differences in model selection, pricing, compliance certifications, and ecosystem integration make the choice consequential.

This guide provides a direct comparison across every dimension that matters for Indian enterprise AI adoption.

What You'll Learn

• Feature-by-feature comparison of all three platforms
• Model availability and pricing in INR
• India region capabilities and data residency options
• Compliance certifications for regulated industries
• Decision matrix to choose the right platform

Platform Overview

Google VertexAI

Google's unified AI platform provides access to Gemini models, open-source models via Model Garden, and enterprise features like grounding, evaluation, and vector search. VertexAI is tightly integrated with BigQuery, making it strong for data-heavy AI workloads.

Standout feature: Model Garden — access 200+ open-source models (Llama, Mistral, Stable Diffusion) alongside Gemini, all through one API.

For a hands-on setup walkthrough, see our VertexAI Enterprise Setup Guide.

AWS Bedrock

Amazon's fully managed service for accessing foundation models from Anthropic (Claude), Meta (Llama), Amazon (Titan), Mistral, and others. Bedrock differentiates with Knowledge Bases (managed RAG), Guardrails API, and deep integration with AWS services.

Standout feature: Guardrails API — native content filtering, PII detection, and topic restriction without building custom middleware.

For a hands-on setup walkthrough, see our AWS Bedrock Enterprise Setup Guide.

Azure AI

Microsoft's AI platform built around OpenAI GPT models with additions from Meta, Mistral, and others. Azure AI's strength is enterprise integration — Active Directory, Microsoft 365, Dynamics 365, and existing enterprise agreements.

Standout feature: Enterprise agreement pricing — many Indian enterprises already have Microsoft EA contracts that include Azure AI credits.

Head-to-Head Comparison

Model Availability

| Model Family | VertexAI | AWS Bedrock | Azure AI | |-------------|:--------:|:-----------:|:--------:| | Gemini (Google) | Yes (native) | No | No | | Claude (Anthropic) | Yes | Yes (native) | No | | GPT-4o/o3 (OpenAI) | No | No | Yes (native) | | Llama 3/4 (Meta) | Yes | Yes | Yes | | Mistral/Mixtral | Yes | Yes | Yes | | Titan (Amazon) | No | Yes (native) | No | | Open-source (Model Garden) | 200+ models | Limited | Limited |

Winner: VertexAI for model variety. Bedrock for Claude-specific workloads. Azure for GPT-exclusive shops.

Pricing Comparison (Per 1M Tokens, March 2026)

Prices converted to INR at approximately ₹84 per USD.

| Model | Platform | Input (₹/1M tokens) | Output (₹/1M tokens) | |-------|----------|--------------------:|---------------------:| | Gemini 2.5 Pro | VertexAI | ₹105 | ₹420 | | Gemini 2.5 Flash | VertexAI | ₹6.3 | ₹25.2 | | Claude 3.7 Sonnet | Bedrock | ₹252 | ₹1,260 | | Claude 3.5 Haiku | Bedrock | ₹67 | ₹336 | | GPT-4o | Azure AI | ₹210 | ₹630 | | GPT-4o mini | Azure AI | ₹12.6 | ₹50.4 | | Llama 3.1 70B | Bedrock | ₹55 | ₹55 |

Winner: VertexAI (Gemini Flash) for high-volume, cost-sensitive workloads. Azure (GPT-4o mini) for budget GPT. Bedrock (Haiku) for affordable Claude.

India Cost Note: For a typical enterprise processing 100M tokens/month, the annual cost difference between platforms can exceed ₹50 lakh. Run proof-of-concept workloads on each platform before committing.

India Region Availability

| Feature | VertexAI | AWS Bedrock | Azure AI | |---------|----------|-------------|----------| | India regions | Mumbai, Delhi | Mumbai, Hyderabad | Pune, Chennai | | All models in India region | Most (some US-only) | Most (some US-only) | Most (some US-only) | | Data residency guarantee | Yes (VPC-SC) | Yes (PrivateLink) | Yes (Private endpoints) | | DPDP Act compliant config | Yes | Yes | Yes |

Winner: Tie. All three have robust India presence. Check that your specific models are available in India regions — some newer models launch in US regions first.

Compliance Certifications

| Certification | VertexAI | AWS Bedrock | Azure AI | |--------------|:--------:|:-----------:|:--------:| | SOC2 Type II | Yes | Yes | Yes | | HIPAA BAA | Yes | Yes | Yes | | PCI-DSS | Yes | Yes | Yes | | ISO 27001 | Yes | Yes | Yes | | ISO 27701 (Privacy) | Yes | Yes | Yes | | India DPDP Act | Supported | Supported | Supported | | EU AI Act | In progress | In progress | In progress |

Winner: Tie for standard certifications. For HIPAA-specific healthcare AI, AWS has the deepest healthcare stack. For EU-facing requirements, Azure has the most mature EU data boundary.

Enterprise Features

| Feature | VertexAI | AWS Bedrock | Azure AI | |---------|----------|-------------|----------| | Managed RAG | Vertex AI Search | Knowledge Bases | Azure AI Search | | Guardrails/Safety | Safety Settings | Guardrails API | Content Safety API | | Fine-tuning | Yes (Gemini, open models) | Yes (Titan, custom models) | Yes (GPT, open models) | | Evaluation | Vertex AI Eval | Manual/custom | Azure AI Evaluation | | Vector database | Vertex Vector Search | OpenSearch Serverless | Azure AI Search | | Prompt management | Vertex AI Studio | Bedrock Playground | Azure AI Studio | | Batch processing | Yes | Yes | Yes |

Ecosystem Integration

| Your Existing Stack | Best Platform | Why | |-------------------|---------------|-----| | GCP + BigQuery | VertexAI | Native BigQuery integration, unified billing | | AWS + S3 + Lambda | Bedrock | IAM integration, S3 as data source for RAG | | Microsoft 365 + Azure AD | Azure AI | SSO, Copilot extensions, EA pricing | | Multi-cloud | Bedrock or VertexAI | Best model variety, LangChain compatibility |

Decision Matrix

Choose Google VertexAI When

• Cost efficiency is a primary concern (Gemini Flash is the cheapest frontier model)
• You need access to 200+ open-source models via Model Garden
• Your data stack is BigQuery-centric
• You want Gemini's strong multilingual capabilities (Hindi, Tamil, Telugu support)
• Grounding with Google Search is valuable for your use case

Choose AWS Bedrock When

• Claude (Anthropic) is your primary model
• You need the Guardrails API for content filtering and security
• Your infrastructure is already on AWS
• Knowledge Bases (managed RAG with S3) solves your retrieval needs
• You value multi-model flexibility (Claude + Llama + Titan in one API)

Choose Azure AI When

• GPT-4o is your primary model and you need the latest OpenAI features
• You have an existing Microsoft Enterprise Agreement with AI credits
• Integration with Microsoft 365 (Copilot, Teams, SharePoint) is required
• Your enterprise identity is on Azure Active Directory
• You are building Copilot extensions for internal tools

Multi-Cloud AI Strategy

Large Indian enterprises (Reliance, TCS, Infosys, HDFC) increasingly adopt multi-cloud AI strategies. The rationale:

• No single provider has all the best models — Gemini on VertexAI, Claude on Bedrock, GPT on Azure
• Negotiate better pricing by maintaining credible alternatives
• Reduce concentration risk — if one provider has an outage, failover to another
• Compliance flexibility — some regulations may favor specific providers

Implementation approach:

# Abstraction layer using LangChain
from langchain_google_vertexai import ChatVertexAI
from langchain_aws import ChatBedrock
from langchain_openai import AzureChatOpenAI

# Switch providers without changing application code
def get_llm(provider: str, model: str):
    if provider == "vertexai":
        return ChatVertexAI(model=model, project="my-project", location="asia-south1")
    elif provider == "bedrock":
        return ChatBedrock(model_id=model, region_name="ap-south-1")
    elif provider == "azure":
        return AzureChatOpenAI(deployment_name=model)

Use an abstraction layer from day one. Even if you start with a single cloud, the ability to switch providers is a strategic advantage.

Official Resources

• Google Vertex AI Documentation — Official VertexAI developer docs
• AWS Bedrock Documentation — Official Bedrock developer docs
• Azure AI Services Documentation — Official Azure AI docs
• Google Cloud India Regions — Mumbai and Delhi availability
• AWS Asia Pacific Regions — Mumbai and Hyderabad availability

Next Steps

• Set up Google VertexAI with a hands-on guide including India pricing and region configuration
• Set up AWS Bedrock with IAM, Knowledge Bases, and Guardrails
• Understand AI compliance requirements before choosing a platform
• Build AI security guardrails regardless of which platform you choose