Intelligent Apps leverage advanced technologies like artificial intelligence (AI), machine learning (ML), and data analytics. In the commercial sphere, they harness business intelligence to facilitate and automate everyday employee and customer actions.
The democratization of these technologies has broadened access to Intelligent Apps, with AI- and ML-driven cloud platforms like Azure greatly reducing technical barriers. Scalable infrastructures, pre-built AI services, data storage and processing capabilities, and robust security and compliance features simplify development processes, foster collaboration, and accelerate innovation.
While incorporating Intelligent Apps into your workflow comes with financial considerations, smart budgetary planning helps maximize their value without sacrificing your bottom line.
Understanding Costs in Intelligent App Development​
Various factors affect the cost of generating and maintaining cloud-based Intelligent Apps, which are complex and often necessitate processing voluminous data.
A key expense is the cloud platform itself, which normally offers tiered pricing plans that factor in data volume, number of users, and additional features. Next, you must account for compute resources — CPU, GPU, and TPU usage — and storage, which are essential for training data. Don’t forget data transfer fees, staff and training, and developer tools and resources (like IDEs and CI/CD pipelines).
Key Azure Services and Their Cost Implications​
Several essential Azure services help facilitate Intelligent App development, each with unique cost implications.
Azure Kubernetes Service (AKS)​
Azure Kubernetes Service (AKS) is crucial in Intelligent App development, enabling scalable, reliable, and efficient deployment and management of containerized applications.
The Free tier represents an excellent starting point, offering free cluster management and pay-for-use virtual machines (VMs), associated storage, and networking resources. For building at scale, consider upgrading to the Standard tier for a scalable Kubernetes control plane, a guaranteed service level agreement (SLA), and an elevated node limit per cluster.
If you are trying to understand Retrieval Augmented Generation (RAG) applications, scenarios and how to leverage the power of GenAI in your enterprise apps, then checkout this live learning session with SMEs on how to build intelligent apps with Azure OpenAI, Semantic Kernal, Azure Functions and Azure Container Apps.
Azure Functions​
Azure Functions provides a scalable, event-driven compute platform that integrates with other Azure AI services and supports microservices architecture. It also refines your development and deployment processes, blending easily into DevOps pipelines.
There are two straightforward pricing options:
- Pay as you go — Based on compute capacity per second
- Azure savings plan for compute — A fixed hourly amount, accommodates fluctuations and dynamic workloads, one- or three-year commitment
Azure OpenAI Service​
Azure OpenAI Service provides access to state-of-the-art Open AI models and scalable computing resources. It integrates with Azure ecosystem services and developer-friendly APIs and remains committed to ethical AI practices.
Like Azure Functions, Azure OpenAI Service offers two plans:
- Pay-As-You-Go (PAYG) — Pay only for resources you use.
- Provisioned Throughput Units (PTUs) — Receive guaranteed throughput for scalable, predictable AI solutions.
Azure Container Apps​
Azure Container Apps provides a platform for building and deploying cloud-native applications utilizing serverless containers. A Kubernetes-based application platform, Azure Container Apps offers detailed observability, dynamic scaling, and end-to-end developer productivity capabilities in a scalable, portable managed platform. The pricing model includes two options:
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Pay as you go — Pay for compute capacity by the second and increase or decrease consumption as needed. Consumption is measured by resource consumption in vCPU-seconds and GiB-seconds and the number of HTTP requests.
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Azure savings plan for compute — A fixed hourly amount, this model accommodates fluctuations and dynamic workloads, one- or three-year commitment.
Checkout the quick demo bytes for Intelligent Apps with Azure Container Apps for a detailed walkthrough from the product team on using open-source vector database, Qrdrant, and building a multi-LLM chat application.
Azure Machine Learning​
Azure Machine Learning is also committed to ethical Intelligent App development at scale. It creates value through industry-leading ML operations (MLOps), open-source compatibility, and integrated tools.
This service has a three-tier payment model:
- Pay as you go — Charges for compute capacity by the second
- Azure savings plan for compute — Offers fixed pricing for one or three years, accommodates dynamic workloads
- Reservations — Offers Azure Reserved Virtual Machine Instances for substantial cost savings. It’s ideal for stable, predictable workloads.
Azure Cosmos DB​
Azure Cosmos DB provides a scalable, globally distributed database service supporting multiple data models, low-latency data access, high availability, and seamless integration with other Azure services. Its pricing model centers on your preferred resource metric:
- Compute — Billed per second using Request Units (RU), which are a proxy for compute, memory, and IO
- Storage — You pay for transactional and analytical data and indexes, as well as backups.
- Bandwidth — Azure charges for data that leaves the Azure cloud or travels through the Azure WAN between regions or availability zones.
Additionally, Azure offers numerous AI/ML developer tools, data storage, processing, and hosting, enabling versatile and powerful Intelligent App creation.
When budgeting and planning, consider the Azure Pricing Calculator. It transforms expected resource usage into a projected cost, simplifying Azure services budgeting.
Azure Kubernetes Service Cost Analysis Add-On​
For more sophisticated and comprehensive cost analysis, there’s the Azure Kubernetes Service (AKS) cost analysis add-on.
This tool provides detailed insights into the costs of AKS clusters and streamlines cost management within the Azure ecosystem. It’s built on top of OpenCost, an open-source project under the Cloud Native Computing Foundation (CNCF) that tracks Kubernetes costs with high granularity. By leveraging OpenCost's capabilities within an Azure-native framework, the AKS cost analysis add-on eliminates the need for third-party solutions like Kubecost or the standalone OpenCost.
The AKS cost analysis add-on requires the AKS cluster to be in either the Standard or Premium tier. You also need certain Azure command-line interface (CLI) versions to register the ClusterCostAnalysis feature flag on your subscription.
Here are the key benefits of the AKS cost analysis add-on.