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Pricing Examples - Demonstration Purposes Only

Provides estimated reference costs for a Retrieval-Augmented Generation (RAG) solution over one month, broken down by components.

Note

Use these values in the Azure Pricing Calculator as a reference. While prices may be adjusted, this guide serves as a foundational resource for the procedure to be followed. Keep in mind that costs will depend on the specific architecture you choose and your unique requirements. This breakdown provides an overview of key components you may require all, some, or even additional elements based on your use case.

List of References (Click to expand)
Table of Content (Click to expand)

Document Intelligence

Azure AI Document Intelligence is a cloud-based AI service (SaaS) that provides an API for document processing. It extracts text, tables, and key data from PDFs, images, and scanned documents using AI models. It automates workflows, supports prebuilt and custom models, and integrates with Azure AI services. Common uses include invoice processing, contract analysis, and healthcare forms. Developers can access it via APIs or SDKs in Python, .NET, and Java.

Please click here Demonstration: Azure AI Document Intelligence Pricing to get more information.

Azure AI Search is a cloud-based search-as-a-service from Microsoft that enables developers to build intelligent, scalable search experiences into web, mobile, and enterprise applications. It combines full-text search with AI-powered capabilities like semantic ranking, natural language processing, and cognitive skills for content enrichment. With built-in scalability, enterprise-grade security, and global availability, helps users quickly find relevant information across large datasets, ideal for use cases like e-commerce, knowledge management, and digital content discovery.

Please click here Demonstration: Azure AI Search Pricing to get more information.

Azure Open AI or AI Foundry

Azure Open AI is a cloud-based generative AI service that exclusively provides OpenAI models (e.g., GPT‑3.5 and GPT‑4) for natural language processing, text generation, and semantic understanding.
Azure AI Foundry supports every type of AI model, allowing developers to deploy and manage a wide range of models (from OpenAI’s to other machine learning architectures) for diverse intelligent applications.

Please click here Demonstration: Azure Open AI & AI Foundry Pricing to get more information.

Storage Account

Azure Storage Account is a cloud-based Infrastructure-as-a-Service (IaaS) offering from Microsoft that provides highly available, durable, and scalable storage for a wide range of data types. It supports blobs, files, queues, tables, and disks, making it ideal for structured and unstructured data. With features like geo-redundancy, encryption at rest, and fine-grained access control, it ensures secure and resilient data storage. Azure Storage integrates seamlessly with Azure services, DevOps pipelines, and data analytics tools, making it a core component for cloud-native applications, backup and disaster recovery, and big data workloads.

Cosmos DB

Azure Cosmos DB is a cloud-based Platform-as-a-Service (PaaS) offering from Microsoft that delivers a globally distributed, multi-model NoSQL database designed for high availability, low latency, and elastic scalability. It supports multiple data models including document, key-value, graph, and column-family, and offers APIs for SQL, MongoDB, Cassandra, Gremlin, and Table. With features like automatic indexing, multi-region writes, and 99.999% availability SLA, Cosmos DB is ideal for real-time applications, IoT, personalization engines, and mission-critical workloads that demand instant responsiveness and global reach.

Key Vault

Azure Key Vault is a cloud-based Platform-as-a-Service (PaaS) offering from Microsoft that provides a secure and centralized solution for managing secrets, encryption keys, and certificates. It enables developers and IT teams to safeguard cryptographic keys and secrets used by cloud applications and services. With features like hardware security module (HSM) protection, role-based access control, and audit logging, Key Vault ensures compliance, data protection, and access governance. It integrates seamlessly with Azure services, DevOps pipelines, and identity management systems, making it ideal for secure application development, key lifecycle management, and regulatory compliance.

Virtual Machine

Azure Virtual Machine is a cloud-based Infrastructure-as-a-Service (IaaS) offering from Microsoft that provides on-demand, scalable computing resources in the cloud. It allows users to deploy and manage virtualized Windows or Linux servers, giving full control over the operating system, storage, and installed software. With features like custom VM sizes, automated scaling, availability sets, and integration with Azure networking, it supports a wide range of workloads from development and testing to enterprise-grade applications. Azure VMs offer high availability, disaster recovery options, and pay-as-you-go pricing, making them ideal for lift-and-shift migrations, legacy app hosting, and customized compute environments.

App Service

Azure App Service is a cloud-based Platform-as-a-Service (PaaS) offering from Microsoft that enables developers to build, host, and scale web applications and APIs quickly and efficiently. It supports multiple languages like .NET, Java, Node.js, Python, and PHP, and provides built-in capabilities like auto-scaling, custom domains, SSL certificates, and CI/CD integration. App Service includes two key components:

  • Web App: for hosting websites, RESTful APIs, and backend services with full support for custom code, frameworks, and containers.
  • Function App: for running event-driven serverless functions that scale automatically and are ideal for background tasks, data processing, and microservices.

Function App

Azure Function App is a cloud-based Platform-as-a-Service (PaaS) offering from Microsoft that enables developers to run event-driven, serverless code without managing infrastructure. It allows you to execute small, modular functions in response to triggers like HTTP requests, timers, queue messages, or blob storage events. With features like automatic scaling, pay-per-execution pricing, and deep integration with Azure services, Function Apps are ideal for background tasks, data processing, and microservice architectures.

In Retrieval-Augmented Generation (RAG) solutions, Azure Function Apps play a key role in handling orchestration logic, such as query preprocessing, calling vector databases, invoking AI models, or post-processing responses. They enable a lightweight, scalable backend that connects components like Azure Cognitive Search, Azure OpenAI, and Azure Cosmos DB, making Function Apps a powerful tool for building modular, intelligent, and cost-efficient RAG pipelines.

Network

Key Azure networking components commonly used in a Retrieval-Augmented Generation (RAG) architecture, and how they contribute to security, performance, and connectivity:

  1. Virtual Network (VNet): is a private, isolated network in Azure that allows resources like VMs, Azure Functions, and databases to securely communicate with each other. In a RAG solution, it ensures that components like Azure OpenAI, Cognitive Search, and Cosmos DB are connected securely and efficiently.
  2. Subnet: is a segment within a VNet that helps organize and isolate resources. For example, you might place Function Apps in one subnet and vector databases in another to apply different security rules or routing policies.
  3. Private Endpoint (PE): allows you to connect securely to Azure services (like Azure OpenAI, Cognitive Search, or Blob Storage) over a private IP address within your VNet. This avoids exposure to the public internet, which is critical for data privacy in RAG pipelines.
  4. Network Security Group (NSG): acts as a virtual firewall that controls inbound and outbound traffic to Azure resources. In a RAG setup, NSGs help enforce strict access rules between components like Function Apps, APIs, and databases.
  5. Azure DDoS Protection: provides automatic detection and mitigation of Distributed Denial of Service (DDoS) attacks. It helps protect public-facing endpoints in a RAG solution, such as APIs or web apps, ensuring availability under attack.
  6. VNet Integration: allows services like Azure App Service or Function Apps to securely access resources inside a VNet. This is essential for connecting to private endpoints or internal databases in a RAG pipeline.
  7. Service Endpoints: extend your VNet to Azure services over the Azure backbone network. While not as secure as Private Endpoints, they are simpler to configure and still avoid public internet exposure.
  8. Application Gateway with WAF (optional): provides Layer 7 load balancing and includes a Web Application Firewall (WAF) to protect against common web vulnerabilities. Useful for exposing RAG APIs securely to external users.
  9. Azure Firewall (optional): is a managed, cloud-based network security service that protects Azure Virtual Network resources. It provides centralized control and logging for traffic flowing in and out of your RAG environment.

AI Services

  1. Azure AI Prompt Flow: A low-code tool in Azure AI Studio that helps you design, evaluate, and optimize prompt engineering workflows. It’s useful for orchestrating RAG pipelines, chaining together retrieval, grounding, and generation steps.
  2. Azure Cognitive Services – Language: Offers capabilities like entity recognition, key phrase extraction, and text summarization, which can be used to preprocess or enrich documents before feeding them into a RAG pipeline.
  3. Azure Cognitive Services – Translator: Enables multilingual RAG solutions by translating user queries or retrieved documents into the desired language before passing them to the LLM.
  4. Azure AI Content Safety: Helps detect and filter harmful or inappropriate content in both retrieved documents and generated responses, ensuring responsible AI use in RAG applications.
  5. Azure AI Custom Neural Voice & Speech Services (for voice-based RAG): Useful if your RAG solution includes voice input/output, enabling transcription, synthesis, and voice interaction.
  6. Azure Machine Learning: Can be used to train or fine-tune custom models, manage vector embeddings, or evaluate RAG performance using custom metrics and pipelines.

Front Door

Azure Front Door is a cloud-native global application delivery network and a Platform-as-a-Service (PaaS) offering from Microsoft that provides high-performance, secure, and scalable entry points for your web applications and APIs. Support SaaS applications and APIs by acting as a secure, intelligent gateway that improves performance, availability, and protection for cloud-based services—including those used in RAG (Retrieval-Augmented Generation) solutions. It offers features like:

  • Global HTTP/HTTPS load balancing
  • SSL offloading
  • Web Application Firewall (WAF)
  • URL-based routing
  • Caching and acceleration via edge POPs
graph TD A[User] --> |Request| B[Azure Front Door] B --> C[Function App - RAG logic] C --> D[Azure AI Search / Vector DB] D --> E[Azure OpenAI - LLM] E --> |Response| B B --> |Response| A

Important

The information provided and any document (such as scripts, sample codes, etc.) is provided AS-IS and WITH ALL FAULTS. Pricing estimates are for demonstration purposes only and do not reflect final pricing. Microsoft assumes no liability for your use of this information and makes no guarantees or warranties, expressed or implied, regarding its accuracy or completeness, including any pricing details. Please note that these demos are intended as a guide and are based on my personal experiences. For official guidance, support, or more detailed information, please refer to Microsoft's official documentation or contact Microsoft directly: Microsoft Sales and Support