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.
Granular Cost Insights​
The AKS cost analysis add-on is integrated with Microsoft Cost Management (MCM), so you can break down your AKS costs into discrete categories, like compute (including CPU cores and memory), storage, and networking. You can distinguish between costs associated with individual applications and shared infrastructure costs, giving you a clear picture of where resources are consumed and where you can make savings.
Azure-Native​
The AKS cost analysis add-on is Azure-native. You can access cost data through the Azure portal via the MCM Cost Analysis portal experience. This direct integration with Azure ensures a seamless user experience and reduces the complexity and overheads of third-party cost management solutions.
Simplified Management​
You can easily activate and deactivate the AKS cost analysis feature with simple commands in the Azure CLI. This ease of management extends to viewing cost information, with the Azure portal providing a comprehensive overview of costs once the add-on is enabled. The ability to enable this feature when creating a new AKS cluster or when updating an existing cluster gives you flexibility in managing your cost analysis preferences.
Enhanced Cost Attribution​
By offering a detailed cost drill-down scoped to Kubernetes constructs, such as clusters and namespaces, in addition to Azure-specific categories like Compute, Network, and Storage, the add-on enhances cost attribution. This detailed view aids organizations in identifying high-cost areas and optimizing resource utilization for cost efficiency.
Managing Costs with Platform Engineering​
It’s crucial to select the right service tier and scaling options to balance performance needs with cost efficiency. One way to ensure this balance is through platform engineering. As a linchpin of cloud management, platform engineering remains essential in developing Intelligent Apps — particularly for forecasting and managing costs as you incorporate them into your workflow.
Platform engineering practices enhance predictability for cloud cost management, helping you stay proactive throughout the process. Following these practices involves implementing cost monitoring tools, establishing budget thresholds, and enforcing cost limits.
Controlling your costs also entails more thoughtful use of your resources. Adjusting resource allocation based on workload demand and performance metrics helps maximize your cloud infrastructure’s efficiency.
Adopting platform engineering principles also enhances operational simplicity. For example, you can minimize the potential for human error and increase reliability by standardizing infrastructure deployments, automating repetitive tasks, and promoting collaboration across teams.
These principles help you plan, implement, and maintain your Intelligent App development strategically and thoughtfully.
Strategies for Cost-Effective Intelligent App Development​
Cost-effective Intelligent App development necessitates a strategic approach that maintains functionality and quality without straining budgets. Strategies to consider include:
- Leveraging managed services — Transferring maintenance responsibility to cloud providers reduces operational burdens.
- Implementing auto-scaling — Aligning resources with demand avoids unnecessary expenses during lulls.
- Continuous monitoring and analysis — Cloud environments require consistent assessments to identify inefficiencies and optimize resource allocation.
- Track costs closely with AKS Cost Analysis add-on — Gain detailed insights into how resources are consumed using the cost analysis add-on native to AKS.
You can also adjust your VM instance options for cost efficiency. For workloads with flexible timing, opt for your cloud provider’s spot instances — spare compute capacity at significantly discounted prices compared to on-demand instances. For predictable workloads with fixed commitments, reserved instances provide further cost reductions.
These strategies help you make informed decisions, balancing cost-effectiveness with dynamic development requirements.
Summary​
Understanding and managing costs effectively is paramount to developing Intelligent Apps, requiring strategic planning and continuous optimization. Combining the right approach with tools like the AKS Cost Analysis add-on and Azure Pricing Calculator ensures that Intelligent App creation remains manageable and becomes a catalyst for innovation.
As you explore further, delve into Microsoft Customer Stories for real-world insights. And mark your calendar for the final episode of the Serverless on Azure learn live series.