--- source_url: "https://www.agilesoftlabs.com/blog/2026/05/aws-bedrock-vs-azure-openai-vs-google" title: "Bedrock vs Azure OpenAI vs Vertex AI: 2026 Comparison" mirrored_at: 2026-08-13T13:01:53.953Z host: www.agilesoftlabs.com cited_in_42a: true mirror_canonical: "https://index.42a.ai/www.agilesoftlabs.com/blog/2026/05/aws-bedrock-vs-azure-openai-vs-google" --- > **Original source:** https://www.agilesoftlabs.com/blog/2026/05/aws-bedrock-vs-azure-openai-vs-google **Published:** May 28, 2026 | **Reading Time:** 18 minutes  > #### **About the Author** > > #### Murugesh R is an AWS DevOps Engineer at AgileSoftLabs, specializing in cloud infrastructure, automation, and continuous integration/deployment pipelines to deliver reliable and scalable solutions. ## Key Takeaways - **Platform choice matters more than model choice in 2026** – model gaps are small (5–15%); compliance, data residency, pricing, and integration drive real enterprise impact. - **AWS Bedrock’s main edge is model breadth** – one API for Claude, Llama, Mistral, Cohere, Stability AI, and Amazon Nova, so you can swap models without re‑architecting. - **Azure OpenAI’s main edge is OpenAI‑model depth** – early access to the latest GPT/o‑series plus Microsoft‑stack compliance and Entra ID, making it the default for Microsoft‑first shops. - **Vertex AI’s main edges are context, integration, and cost** – 1M‑token context (Gemini 1.5 Pro), tight BigQuery integration, and Gemini 1.5 Flash at $0.075 per million input tokens, making it the cheapest at scale. - **Compliance is the first filter** – AWS Bedrock and Azure OpenAI have FedRAMP High; Vertex AI’s FedRAMP High is in progress, so it’s off the table for some US‑federal workloads. - **Costs vary dramatically at scale** – at 100M inputs + 30M outputs/month, Vertex Gemini Flash can cost ~$16.50 vs. Bedrock Claude 3.5 Sonnet at ~$750, a 45× spread that makes platform choice a financial decision. - **Multi‑platform setups are common** – Azure OpenAI for GPT‑4o, Bedrock for Claude‑based tasks, and Vertex Gemini Flash for high‑volume, cost‑sensitive workloads, all behind a unified gateway (e.g., LiteLLM or LangChain). ## Introduction Enterprises deploying LLMs in 2026 are not primarily choosing between models — they are choosing between cloud AI platforms that determine security posture, compliance coverage, pricing at scale, and integration depth with existing infrastructure. AWS Bedrock, Azure OpenAI Service, and Google Vertex AI have all matured significantly, but they have matured differently and in different directions. At [AgileSoftLabs](https://www.agilesoftlabs.com/), we have built production AI systems on all three platforms for clients ranging from regulated financial services firms to high-growth SaaS companies. This comparison is based on real deployments across healthcare, fintech, analytics, and enterprise software — not benchmarks from vendor documentation. [Cloud Development Services](https://www.agilesoftlabs.com/cloud-development) and [AI & Machine Learning Development Services](https://www.agilesoftlabs.com/ai-ml-solutions) deploy production AI workloads across all three platforms, with platform selection driven by the framework described in this guide. ## Why Platform Choice Matters More Than Model Choice GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro are all exceptional models. The performance gap between top-tier models on most enterprise tasks is 5–15% — meaningful, but not the primary decision driver for most production deployments. The factors that actually drive platform selection are: - **Your existing cloud infrastructure** — an AWS-first organisation avoids IAM complexity and billing fragmentation by staying on Bedrock; similarly for Azure and GCP shops.  - **Compliance requirements** — HIPAA Business Associate Agreements, FedRAMP authorisation levels, SOC 2 Type II, and PCI-DSS coverage differ materially between platforms. - **Data residency constraints** — EU customers, regulated industries, and data sovereignty requirements constrain region availability. - **Deployment model** — public API versus private endpoints versus dedicated capacity versus on-premises. - **Developer ecosystem preferences** — Python-first versus .NET-heavy versus Google toolchain familiarity. - **Pricing at your specific usage tier** — cost structures diverge significantly above 10M tokens per day. ## Platform Overview Dimension AWS Bedrock Azure OpenAI Google Vertex AI Launch year 2023 2023 2023 Primary differentiator Model breadth, AWS integration OpenAI model access, Microsoft integration Google's own models, multimodal leadership Deployment model Managed API + private endpoints Managed API + provisioned throughput Managed API + dedicated endpoints Pricing model Pay-per-token + provisioned capacity Pay-per-token + PTU (Provisioned Throughput Units) Pay-per-token + committed use Regions available 18 24 38 ## Model Access and Selection This is the most significant differentiator between platforms — not in quality, but in which models are accessible and through what API surface. ### AWS Bedrock: Model Breadth Across Providers Bedrock's strength is the only platform offering a single unified API across multiple model providers: Model Family Access via Bedrock Claude (Anthropic) Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku Llama (Meta) Llama 3.1 405B, 70B, 8B Mistral Mistral Large, Mistral 7B Titan (Amazon) Titan Text, Titan Embeddings, Titan Image Cohere Command R+, Embed Stability AI SDXL Amazon Nova Nova Pro, Nova Lite, Nova Micro (new 2025) **Bedrock advantage:** Run experiments across models with identical SDK code, swap models without changing architecture, and fine-tune models with your own data — all within one AWS account and IAM policy structure. ### Azure OpenAI: Depth with OpenAI Models Azure OpenAI's strength is exclusive early access to the latest GPT and o-series models with Microsoft's enterprise wrapper: Model Status GPT-4o (May 2024) GA — global deployment GPT-4o mini GA — lower cost tier o1 (reasoning) GA o3 (latest reasoning) Preview GPT-4 Turbo GA — legacy DALL-E 3 GA Whisper GA **Azure advantage:** Get OpenAI's latest models inside your enterprise VNet with RBAC integration through Microsoft Entra ID (formerly Azure Active Directory), zero-shot fine-tuning on custom datasets, and Microsoft's enterprise SLA coverage. ### Google Vertex AI: Multimodal Leadership and Context Scale Vertex's strength is Google's native multimodal models and tight integration with the Google Cloud data ecosystem: Model Strength Gemini 1.5 Pro 1M token context window, multimodal (vision + text + audio) Gemini 1.5 Flash Fast, cost-efficient for high-volume tasks Gemini 2.0 Flash Latest, fastest in the Gemini family PaLM 2 Legacy text tasks Imagen 3 Image generation Third-party (Llama, Mistral) Available via Model Garden **Vertex advantage:** Native BigQuery integration for analytics use cases, 1M token context window for document-heavy workloads, and best multimodal performance for applications combining vision and text reasoning. [AI Meeting Assistant](https://www.agilesoftlabs.com/products/ai-agents/ai-meeting-assistant) and [AI Sales Agent](https://www.agilesoftlabs.com/products/ai-agents/ai-sales-agent) enterprise deployments use platform selection logic directly from this framework — Bedrock for Claude-powered long-document analysis, Azure OpenAI for GPT-4o-based conversation interfaces in Microsoft Teams-integrated workflows. ## Security and Compliance For enterprise deployments, this section often decides the platform selection before any other criterion is evaluated. Compliance Certification AWS Bedrock Azure OpenAI Vertex AI SOC 2 Type II ✔ ✔ ✔ ISO 27001 ✔ ✔ ✔ HIPAA (BAA available) ✔ ✔ ✔ PCI-DSS ✔ ✔ ✔ FedRAMP Moderate ✔ ✔ ✔ FedRAMP High ✔ ✔ ✘ (in progress) GDPR ✔ ✔ ✔ Data residency (EU) ✔ ✔ ✔ Private endpoints (VPC) ✔ Bedrock VPC ✔ Private Endpoint ✔ VPC Service Controls Customer-managed keys ✔ ✔ ✔ **Key differentiator — data processing agreements:** Azure OpenAI has the clearest enterprise data processing agreement — your data is not used to train OpenAI models by default, and Microsoft's enterprise commitments are well-documented and contractually enforceable. AWS Bedrock has equivalent guarantees. Google Vertex AI's data handling posture was less clear historically, but has improved significantly in 2025–2026. **FedRAMP High is the decisive filter** for US federal agencies and regulated government contractors. Azure and Bedrock qualify; Vertex AI does not yet. For healthcare organisations requiring HIPAA BAA coverage with private VPC endpoints, all three platforms qualify — making the decision shift to integration depth and cost. [CareSlot AI](https://www.agilesoftlabs.com/products/healthcare/careslot-ai) healthcare platform deployments route through AWS Bedrock for Claude-based clinical document analysis, specifically because the Bedrock VPC endpoint model keeps PHI within the customer's VPC without traversing the public internet — a requirement under HIPAA's Technical Safeguards. ## Pricing Comparison at Scale _Prices as of May 2026. Always verify current pricing directly at each provider's pricing page._ ### Input/Output Token Pricing (per 1M tokens) Model Platform Input Output Claude 3.5 Sonnet AWS Bedrock $3.00 $15.00 GPT-4o Azure OpenAI $2.50 $10.00 Gemini 1.5 Pro Vertex AI $1.25 (≤128K context) $5.00 Llama 3.1 70B AWS Bedrock $0.99 $0.99 Gemini 1.5 Flash Vertex AI $0.075 $0.30 GPT-4o mini Azure OpenAI $0.15 $0.60 ### Monthly Cost at 100M Input + 30M Output Tokens Platform + Model Monthly Cost AWS Bedrock — Claude 3.5 Sonnet $750 Azure OpenAI — GPT-4o $550 Vertex AI — Gemini 1.5 Pro $275 AWS Bedrock — Llama 3.1 70B $129 Vertex AI — Gemini 1.5 Flash $16.50 The 45× cost spread between Claude 3.5 Sonnet and Gemini Flash for equivalent token volume underscores why the right model-platform pairing is a financial decision, not just a technical one. Provisioned throughput pricing adds further complexity — Azure PTUs, AWS Committed Throughput, and Google Committed Use Discounts each have different break-even points. As a general rule, provisioned capacity pays off at sustained utilisation above 60% of peak capacity. For [AI-Powered Loan Management Software](https://www.agilesoftlabs.com/products/finance/loaner-management-app) deployments processing high-volume document analysis, the Vertex Gemini Flash pricing tier enables cost-effective AI at scale for routine document classification tasks, reserving the more capable (and expensive) models for complex underwriting analysis. ## Developer Experience ### AWS Bedrock ``` import boto3 import json client = boto3.client('bedrock-runtime', region_name='us-east-1') response = client.invoke_model( modelId='anthropic.claude-3-5-sonnet-20241022-v2:0', body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Explain RAG in one paragraph."} ] }) ) result = json.loads(response['body'].read()) print(result['content'][0]['text']) ``` **DX verdict:** Familiar for AWS teams. Bedrock's Converse API (unified interface across all models) simplifies model switching — the same API call works for Claude, Llama, and Titan with only the `modelId` change. IAM integration is excellent. Configuration is verbose for developers without AWS background. ### Azure OpenAI ``` from openai import AzureOpenAI import os client = AzureOpenAI( azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], api_key=os.environ["AZURE_OPENAI_API_KEY"], api_version="2024-10-21" ) response = client.chat.completions.create( model="gpt-4o", # deployment name messages=[{"role": "user", "content": "Explain RAG in one paragraph."}] ) print(response.choices[0].message.content) ``` **DX verdict:** Best for teams already using the OpenAI SDK — Azure OpenAI is fully OpenAI SDK compatible. Migration from the direct OpenAI API requires only endpoint and API key changes, not code changes. Excellent ergonomics for Python and .NET teams. Deployment names (rather than model names) add a layer of configuration that trips up newcomers. ### Google Vertex AI ``` import vertexai from vertexai.generative_models import GenerativeModel vertexai.init(project="your-project", location="us-central1") model = GenerativeModel("gemini-1.5-pro") response = model.generate_content("Explain RAG in one paragraph.") print(response.text) ``` **DX verdict:** Cleanest Python SDK among the three — the fewest lines of boilerplate to a working API call. BigQuery integration is genuinely powerful for analytics-adjacent AI workloads. Google Cloud IAM is intuitive for GCP-native teams. A steeper learning curve for teams without a GCP background, and the project/location configuration model is unfamiliar to AWS and Azure developers. ## Enterprise Integration Depth Integration Layer AWS Bedrock Azure OpenAI Vertex AI Identity and access AWS IAM Microsoft Entra ID / AAD Google Cloud IAM Monitoring CloudWatch + Bedrock metrics Azure Monitor + OpenAI metrics Cloud Monitoring + Vertex metrics Data pipeline S3, Glue, SageMaker Azure Data Factory, Synapse BigQuery, Dataflow, Vertex Pipelines Logging and audit CloudTrail Azure Audit Logs Cloud Audit Logs Secret management AWS Secrets Manager Azure Key Vault Secret Manager Native vector store OpenSearch Serverless Azure AI Search Vector Search (Vertex) Best fit AWS-heavy organizations Microsoft/Azure-first organizations GCP/Google Workspace organizations The integration column is often the deciding factor before pricing is even evaluated. An organisation running its data warehouse in Snowflake on AWS, its identity in Okta federated to AWS IAM, and its application infrastructure on ECS is not choosing Vertex AI — the integration tax is prohibitive regardless of Gemini's technical merits. ## Performance and Reliability Metrics from production deployments (May 2026): Metric AWS Bedrock Azure OpenAI Vertex AI p50 TTFT (flagship model) 680ms 520ms 590ms p99 TTFT 2.4s 1.8s 2.1s SLA 99.9% 99.9% 99.9% Rate limits (default tier) Model-dependent Model-dependent; PTU bypasses Model-dependent Global CDN routing ✔ Bedrock Global ✔ Global Standard ✔ Multi-region endpoints Azure OpenAI has the lowest latency for GPT-4o — expected, given direct access from source. Bedrock performance varies by model; Claude on Bedrock runs slightly slower than direct Anthropic API access due to the additional routing layer. Vertex AI Gemini Flash is the fastest among all options for speed-prioritised, cost-sensitive use cases. For sub-500ms Time-to-First-Token requirements, streaming is mandatory on all three platforms. Provisioned throughput (PTU on Azure, Committed Throughput on Bedrock) eliminates cold-start overhead for latency-critical applications. ## Real Deployment Scenarios **Scenario 1: Healthcare SaaS (HIPAA required, long document analysis)** AWS Bedrock or Azure OpenAI — both have clear, contractually defined HIPAA BAAs. Claude 3.5 Sonnet on Bedrock for the 200K context window needed for clinical document analysis. Azure OpenAI if the development team is .NET-heavy and already operating on Azure infrastructure. **Scenario 2: Financial Services (FedRAMP High required)** AWS Bedrock or Azure OpenAI only — Vertex AI FedRAMP High authorisation is not yet GA as of May 2026. Both Bedrock and Azure maintain FedRAMP High authorisation in the relevant US government regions. **Scenario 3: Analytics-Heavy AI (BigQuery data warehouse, BI teams)** Google Vertex AI overwhelmingly — native BigQuery integration, Looker connection, Google Workspace identity, and Vertex Pipelines all remove the data movement friction that would otherwise dominate integration cost. **Scenario 4: Cost-Optimised High-Volume Inference** Vertex AI Gemini Flash for routine classification, summarisation, and extraction tasks at $0.075 per million input tokens. AWS Bedrock Llama 3.1 70B for teams comfortable with open-weight models and the inference consistency tradeoffs they carry. **Scenario 5: Microsoft-First Enterprise** Azure OpenAI without significant evaluation of alternatives — seamless Entra ID identity federation, existing Azure billing consolidation, Teams integration for AI assistant deployment, and the existing Microsoft enterprise agreement covering AI service spend. ## Decision Framework Decision Tree: Cloud AI Platform Selection #### Step 1: Compliance filter - FedRAMP High required? → AWS Bedrock or Azure OpenAI only - HIPAA + VPC private endpoints? → All three qualify - No specific compliance constraint? → Proceed to Step 2 #### Step 2: Infrastructure fit - Primarily AWS? → AWS Bedrock - Primarily Microsoft/Azure? → Azure OpenAI - Primarily GCP / Google Workspace? → Vertex AI - Cloud-agnostic or multi-cloud? → Proceed to Step 3 #### Step 3: Use case fit - Need Claude, Llama, Mistral from one API? → AWS Bedrock - Need latest GPT/o-series with enterprise SLA? → Azure OpenAI - Need 1M token context window? → Vertex AI (Gemini 1.5 Pro) - Need lowest cost/token at scale? → Vertex AI (Gemini Flash) - Heavy BigQuery / analytics workload? → Vertex AI #### Step 4: Confirm with pricing at your expected volume - Calculate 6-month TCO at projected token usage - before committing to provisioned capacity **Choose AWS Bedrock if** your infrastructure is primarily AWS, you want model diversity from one API, you need to experiment across Claude/Llama/Mistral without re-architecting, or you have FedRAMP High requirements. **Choose Azure OpenAI if** your organisation is Microsoft/Azure-first, you need the latest OpenAI models with enterprise SLAs, your dev team works in the OpenAI SDK, or you are deploying AI within Microsoft 365/Teams environments. **Choose Google Vertex AI if** you need the 1M token context window for document-heavy workloads, your data lives in BigQuery, you want the lowest cost per token at scale, or you are already operating on GCP. Review [AgileSoftLabs case studies](https://www.agilesoftlabs.com/case-studies) across enterprise AI platform deployments for real-world outcomes on each platform across healthcare, fintech, and SaaS verticals. [Web Application Development Services](https://www.agilesoftlabs.com/web-app-development) builds the enterprise application layer — the APIs, dashboards, and user interfaces — that sits above whichever cloud AI platform is selected. ## Ready to Deploy Enterprise AI on the Right Platform? Platform selection is the architectural decision that shapes every subsequent AI deployment choice — compliance posture, model availability, integration depth, and cost trajectory all follow from it. The decision framework in this guide has guided production deployments across healthcare, financial services, analytics platforms, and enterprise SaaS — and the right answer varies significantly by organisational context. [AgileSoftLabs](https://www.agilesoftlabs.com/) has deployed production AI on AWS Bedrock, Azure OpenAI, and Google Vertex AI and provides architecture consultation to help enterprises select and implement the right platform for their specific constraints. Explore the [full AI products and services portfolio](https://www.agilesoftlabs.com/products) or [contact our team](https://www.agilesoftlabs.com/contact) for an architecture consultation. ## Frequently Asked Questions ### 1\. Which cloud AI platform is best for enterprises in 2026: AWS, Azure, or Google? The best choice depends on your stack: AWS Bedrock excels if you’re AWS‑native and multi‑model‑heavy, Azure OpenAI fits Microsoft‑centric environments, and Vertex AI wins for GCP‑native, data‑heavy, and ML‑first teams. ### 2\. How do AWS Bedrock, Azure OpenAI, and Vertex AI differ in model access? Bedrock gives you multi‑model flexibility (Anthropic, Meta, Titan, etc.), Azure OpenAI focuses tightly on OpenAI models, and Vertex AI leans into Gemini and Google’s own ecosystems, as well as custom ML models. ### 3\. Which platform is best if my company is Microsoft‑centric? For Microsoft‑centric companies, Azure OpenAI integrates most smoothly with Azure AD, M365, Power Platform, and Copilot, reducing friction and licensing complexity. ### 4\. Which platform is best if we’re AWS‑native but want OpenAI‑grade models? AWS Bedrock lets AWS‑native teams bring OpenAI‑aligned models via Bedrock while staying inside AWS IAM, VPC, KMS, and S3, minimising cross‑cloud sprawl. ### 5\. Which platform is best for data‑heavy, analytics‑driven AI workloads? Vertex AI shines for data‑heavy workloads thanks to tight integration with BigQuery, Looker, and Google’s ML stack, making it ideal for analytics‑centric, ML‑heavy teams. ### 6\. How do Bedrock, Azure OpenAI, and Vertex AI differ in compliance and governance? All three support FedRAMP, HIPAA, and SOC‑2‑style controls, but Azure OpenAI inherits Microsoft’s enterprise‑security posture, Bedrock follows AWS security‑services standards, and Vertex AI aligns with Google’s data‑governance and ML‑ops toolkit. ### 7\. Which platform is best for RAG, guardrails, and content safety? Bedrock and Vertex AI offer strong RAG and grounding capabilities, while Azure OpenAI integrates with Microsoft’s Purview, Defender, and content‑safety stacks; the “best” choice depends on whether you prioritise AWS‑native, Microsoft‑native, or GCP‑native tooling. ### 8\. How do pricing and FinOps work for Bedrock, Azure OpenAI, and Vertex AI? Bedrock typically charges per‑token with model‑tier pricing, Azure OpenAI uses per‑token with PTU‑style overage, and Vertex AI mixes per‑token, per‑character, and complex ML‑compute pricing; FinOps complexity is highest on Vertex, moderate on Bedrock, and lowest on Azure OpenAI for Microsoft‑native shops. ### 9\. What are the main tradeoffs between Bedrock, Azure OpenAI, and Vertex AI? Bedrock trades some compliance overhead for model flexibility, Azure OpenAI trades flexibility for deep Microsoft integration, and Vertex AI trades simplicity for data‑ and ML‑stack complexity. ### 10\. How should an enterprise choose among AWS, Azure, and Google AI in 2026? Start with your existing cloud stack, data platform, and security requirements: pick Bedrock for AWS‑native multi‑model use, Azure OpenAI for Microsoft‑centric organizations, and Vertex AI for GCP‑heavy or data‑science‑driven teams.