AWS Bedrock vs Azure OpenAI vs Google Vertex AI: Enterprise LLM Platform Comparison 2026

Choosing where to run your LLM workloads is no longer just about which model you prefer. In 2026, the three hyperscaler AI platforms have diverged meaningfully in their strengths, pricing models, compliance postures, and developer experiences. This comparison is built for engineering teams making actual procurement and architecture decisions — not for a blog post that will be outdated in three months.

Photo by Andres Urena on Unsplash

Quick Overview

AWS Bedrock

Model Catalog

Bedrock’s multi-model approach is its defining strength. You get:

• Anthropic: Claude 4 (Sonnet, Opus, Haiku) + all prior versions
• Meta: Llama 3.3, 4 Scout, 4 Maverick
• Mistral: Mistral Large 2, Mixtral models
• Amazon: Nova Pro, Nova Lite, Nova Micro (surprisingly capable for cost)
• Cohere: Command R+, Embed models
• Stability AI: Image generation models
• AI21: Jamba models

No OpenAI GPT models — this is Bedrock’s biggest gap for teams standardized on GPT-4/GPT-5.

Bedrock-Specific Features

Agents: Full agent orchestration with tool use, built natively:

import boto3

bedrock_agent_runtime = boto3.client(
    'bedrock-agent-runtime',
    region_name='us-east-1'
)

response = bedrock_agent_runtime.invoke_agent(
    agentId='XXXXXXXXXX',
    agentAliasId='XXXXXXXXXX',
    sessionId='session-001',
    inputText='Analyze Q2 sales data and identify top performing regions'
)

Knowledge Bases: Managed RAG with S3 ingestion + multiple vector store backends (OpenSearch Serverless, Aurora pgvector, Pinecone).

Guardrails: Policy-based content filtering that applies across all models — define once, enforce everywhere.

Model Evaluation: Run automated evals against your custom dataset before committing to a model.

Pricing Highlights (June 2026, US-East-1)

Cross-region inference is worth noting: Bedrock automatically routes to available regions to handle capacity, which improves reliability but can affect latency.

Compliance & Security

• VPC Endpoints: Full PrivateLink support — traffic never leaves AWS network
• Customer-managed KMS: Encrypt prompts and responses at rest
• AWS Artifact: SOC 2, PCI DSS, HIPAA BAA available
• No data training: Prompts/responses not used for model training by default
• AWS CloudTrail: Full audit logging integration

Azure OpenAI Service

Why Teams Choose Azure OpenAI

One reason: GPT-5. If your product is built on OpenAI’s models and needs enterprise compliance, Azure OpenAI is the path. Azure gives you GPT-4o, GPT-4.1, GPT-5, o3, o4-mini, and the DALL-E/Whisper/TTS APIs — the full OpenAI portfolio under Microsoft’s enterprise cloud.

The API is intentionally OpenAI-compatible:

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    api_version="2025-01-01-preview",
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"]
)

response = client.chat.completions.create(
    model="gpt-5",              # Your deployment name
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    temperature=0.7
)

Provisioned Throughput Units (PTU)

Azure’s PTU model is the most developed provisioned capacity offering among the three:

• Guaranteed throughput: PTU gives you committed tokens-per-minute, eliminating rate limit headaches
• Lower cost at scale: At high volume, PTU per-token cost undercuts standard pay-as-you-go
• Minimum commitment: PTU requires monthly commitments (typically 100+ PTU minimum purchase)

For high-volume production workloads, the PTU model is often 40-60% cheaper than PAYG.

Azure OpenAI-Specific Features

Content Safety: Granular harm categories with adjustable thresholds (hate, violence, sexual, self-harm) — independently configurable per deployment.

Assistants API: Full Assistants v2 API with file search, code interpreter, and tool use.

Batch API: Submit large jobs asynchronously with 50% cost reduction — useful for data processing pipelines.

Azure AI Studio: Integrated development environment for prompt engineering, evaluation, and deployment.

Compliance

Azure OpenAI inherits Azure’s full compliance portfolio:

• HIPAA, PCI DSS, SOC 1/2/3, ISO 27001
• FedRAMP High authorization (US government)
• EU Data Boundary option (all data stays in EU)
• GDPR-compliant data processing agreements

Google Vertex AI

The Gemini Advantage

Vertex AI’s showcase is Gemini 2.5 Pro/Ultra, and for specific workloads, it’s genuinely ahead:

• Multimodal: Native audio, video, image + text — not bolted on
• 1M+ context window: Gemini 2.5 Pro handles up to 1M tokens, Flash up to 1M
• Code generation: Gemini consistently scores at the top of coding benchmarks
• Grounding: Built-in Google Search grounding for up-to-date information

import vertexai
from vertexai.generative_models import GenerativeModel, Part

vertexai.init(project="my-project", location="us-central1")

model = GenerativeModel("gemini-2.5-pro")

# Multimodal with video
video_part = Part.from_uri(
    uri="gs://my-bucket/product-demo.mp4",
    mime_type="video/mp4"
)

response = model.generate_content([
    video_part,
    "Summarize the key product features shown in this video"
])

Vertex AI Agent Builder

Google’s managed RAG and agent platform:

• Data Store: Ingest from GCS, BigQuery, Cloud SQL, web crawl, or Drive
• Search: Enterprise-grade semantic + keyword hybrid search
• Grounding: Attribute responses to source documents with citations

Model Garden

Vertex hosts both Google models and third-party:

• All Gemini variants
• Claude (via Anthropic partnership)
• Llama models
• Mistral models
• Imagen 3 for image generation

The third-party model selection is narrower than Bedrock but growing.

Pricing (June 2026)

Gemini Flash pricing is aggressively competitive — often the best cost/performance ratio for high-volume inference.

Decision Framework

Choose AWS Bedrock When:

• Your infrastructure is AWS-native
• You need flexibility to switch models (Claude today, Llama tomorrow)
• You want granular compliance controls (Guardrails, KMS, PrivateLink)
• Amazon Nova models are competitive enough for your use case

Choose Azure OpenAI When:

• You need GPT-4.1, GPT-5, or o3/o4 family models
• You’re a Microsoft shop (Azure AD, Entra, M365 integrations)
• You need PTU for predictable cost at high volume
• FedRAMP or government compliance is required

Choose Google Vertex AI When:

• Long context windows are critical (1M+ tokens)
• Multimodal (video/audio) is a core use case
• You need real-time web grounding
• Your infrastructure is GCP-native

Multi-Cloud LLM: A Real Pattern

Many mature teams use all three via a routing layer:

class LLMRouter:
    async def route(self, request: LLMRequest) -> LLMResponse:
        if request.context_tokens > 200_000:
            return await self.gemini_client.complete(request)
        
        if request.model_preference == "gpt5":
            return await self.azure_client.complete(request)
        
        if request.compliance_level == "hipaa" and request.model == "claude":
            return await self.bedrock_client.complete(request)
        
        # Default: cost-optimize with fallback
        return await self.cheapest_available(request)

LiteLLM, Portkey, and OpenRouter provide managed versions of this routing layer.

The Bottom Line

All three platforms are enterprise-ready in 2026. The decision is mostly about:

1. Which models you need
2. Which cloud you’re already on
3. Whether you prioritize model flexibility (Bedrock) or OpenAI compatibility (Azure) or multimodal/long-context (Vertex)

For greenfield projects: start with Bedrock if you’re model-agnostic, Azure OpenAI if you’re locked to GPT models, and Vertex AI if Gemini’s multimodal or context capabilities are core to your product.

References

• AWS Bedrock Pricing
• Azure OpenAI Pricing
• Google Vertex AI Pricing
• LiteLLM — Multi-provider LLM SDK

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