Complete pricing guide for Microsoft Azure. Compare all plans, analyze costs, and find the perfect tier for your needs.
Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Microsoft Azure is worth it →
mo
mo
mo
Pricing sourced from Microsoft Azure · Last verified March 2026
Detailed feature comparison coming soon. Visit Microsoft Azure's website for complete plan details.
View Full Features →Microsoft Azure AI Foundry is used to build, deploy, and manage AI applications, agents, and models on Azure infrastructure. Microsoft positions Foundry as a unified Azure platform experience for enterprise AI operations, model builders, and application developers. The provided page at https://ai.azure.com/ identifies the experience as Microsoft Foundry and the application as "ai-studio", which aligns with a studio-style workflow for AI development rather than only low-level infrastructure. It is most useful for teams that need AI deployment to fit into an existing Microsoft Azure environment.
Microsoft Azure AI Foundry does not have one fixed monthly SaaS price for all users. Microsoft cost guidance says Foundry costs should be estimated through the Azure pricing calculator and monitored through Azure portal cost tools because workloads are billed through the Azure resources, models, services, partner models, compute, storage, and other components used: https://learn.microsoft.com/en-us/azure/ai-foundry/model-inference/how-to/manage-costs. Public paid examples include GPT-4.1 at $2.00 per 1 million input tokens and $8.00 per 1 million output tokens, GPT-4.1 mini at $0.40 per 1 million input tokens and $1.60 per 1 million output tokens, and GPT-4.1 nano at $0.10 per 1 million input tokens and $0.40 per 1 million output tokens. Translator commitment-tier examples include $2,055 per month for 250 million characters, $6,000 per month for 1 billion characters, and $45,000 per month for 4 billion characters, while Translator Standard pay-as-you-go is commonly listed at $10 per million characters. Teams should verify exact region, currency, model, and agreement pricing in the Azure pricing calculator or Azure portal before committing.
The scraped https://ai.azure.com/ page exposes several concrete implementation details: the app name is "ai-studio", the app region is "centralus", and the cloud API host is "centralus.api.azureml.ms". It also references the loader package "@ms/centro-hvc-loader" at version "3.6.0" and attempts to restore React Query data from IndexedDB using the key "REACT_QUERY_OFFLINE_CACHE". These details are useful for technical validation, but they are not a substitute for full product documentation. They mainly confirm that the service is a Microsoft-hosted AI studio experience connected to Azure ML infrastructure.
Choose Microsoft Azure when your team needs AI deployment to align with broader cloud infrastructure, Microsoft identity, enterprise governance, and existing Azure operations. Based on our analysis of 870+ AI tools, Azure is more appropriate for organizations with platform engineering, cloud security, and procurement processes than for solo builders seeking a quick model demo. Smaller teams should compare the operational overhead and variable Azure billing against narrower model deployment platforms before deciding.
The scraped ai.azure.com application shell does not itself show dated release notes, so dated update claims should be tied to Microsoft documentation rather than the page shell. Microsoft Foundry documentation for March 2026 lists new articles and capabilities around Foundry IQ preview, Fireworks models preview, hosted agent lifecycle management, Claude Code configuration for Microsoft Foundry, and quotas and limits for Microsoft Foundry Agent Service: https://learn.microsoft.com/en-us/azure/ai-foundry/whats-new-azure-ai-foundry. Microsoft documentation also describes February 2026 Azure OpenAI updates in Foundry Models, including GPT-Realtime-1.5 and GPT-Audio-1.5 availability: https://learn.microsoft.com/en-us/azure/ai-services/openai/whats-new.
AI builders and operators use Microsoft Azure to streamline their workflow.
Try Microsoft Azure Now →Amazon SageMaker is an AWS platform for building, training, and deploying machine learning and AI models. It provides tools for data, analytics, and AI workflows in a managed cloud environment.
Compare Pricing →Google Cloud's unified platform for machine learning and generative AI, offering 180+ foundation models, custom training, and enterprise MLOps tools.
Compare Pricing →Unified analytics platform that combines data engineering, data science, and machine learning in a collaborative workspace.
Compare Pricing →A collaborative platform where the machine learning community builds, shares, and deploys AI models, datasets, and applications.
Compare Pricing →Run, fine-tune, and deploy thousands of community AI models with a single HTTP API — covering image, video, audio, language, and embedding models, billed per-second of GPU time.
Compare Pricing →