Microsoft API Pricing

Azure AI pricing and Azure OpenAI Service costs

Microsoft pricing searches often mix several Azure AI products: Azure OpenAI Service, Azure AI Foundry model catalog, language services, sentiment analysis, and enterprise deployment options. Use this page as the Microsoft provider hub, then verify the exact service meter on the official Azure pricing page before production.

• Use Azure OpenAI Service pricing when your workload runs OpenAI-family models through an Azure resource.
• Use Azure AI Foundry pricing when you compare model catalog deployment, serverless endpoints, or enterprise AI workflows.
• Use Azure language or NLP pricing for sentiment analysis, entity extraction, translation, and other non-LLM services.
• Keep Azure Container Apps, custom domains, and unrelated infrastructure costs outside this model-pricing comparison.

How to estimate Azure OpenAI pricing

For token-billed Azure OpenAI workloads, estimate input tokens, output tokens, cached input where available, and batch or provisioned capacity separately. Some enterprise workloads may use provisioned throughput or regional deployment choices, so the final Azure bill can differ from a simple public API token estimate.

• Start with the model family and region that your Azure resource can deploy.
• Separate input and output tokens because the rates often differ.
• Check whether your workload uses pay-as-you-go, provisioned throughput, batch processing, or enterprise terms.
• Use the Azure OpenAI page and calculator links on AI Pricing Hub for a token-level estimate before checking Azure billing.

Azure AI Foundry, NLP, and sentiment analysis pricing

Queries such as Azure sentiment analysis pricing and Azure NLP pricing usually belong to Azure AI Language services rather than pure LLM token pricing. They are useful supporting intent for this Microsoft page, but they should not replace the page's main focus on AI model and API cost comparison.

• Treat sentiment analysis and NLP services as supporting Azure AI cost checks, not as separate model rows.
• Use Azure AI Foundry when you need a catalog view, deployment workflow, and model-management context.
• Use official Azure pricing calculators for service-specific meters that are not token-based LLM API charges.

When Microsoft is the right provider choice

Microsoft is often the strongest fit when security, procurement, compliance, and Azure integration matter as much as token price. If you only need the lowest general chat cost, compare Microsoft with direct OpenAI, Google, Anthropic, DeepSeek, and other providers before committing production traffic.

• Choose Microsoft for Azure-native governance, private networking, enterprise billing, and Microsoft ecosystem fit.
• Compare direct OpenAI pricing when Azure-specific compliance or infrastructure is not required.
• Use Phi models or smaller alternatives when edge deployment and cost-sensitive workloads matter.

Related search terms