This guide, the second of a three-part series, explores Azure serverless computing offerings, detailing key components and services and the differences between Azure and other serverless workloads. This article further details use cases, challenges, and monitoring best practices that can help organizations maximize the benefits of serverless computing on Azure. To learn more about serverless compute, read part one on Amazon Web Services (AWS) and part three on Google Cloud Platform serverless computing offerings.
What are the advantages and disadvantages of serverless computing?
Serverless computing is a cloud-based model that lets developers build and deploy applications without managing infrastructure. Using serverless computing on Azure helps developers focus on code, increase development speed, implement event-driven architecture, and scale easily to meet demand.
Serverless applications also have downsides, including vendor lock-in, managing security risks beyond what serverless providers offer, and—more importantly—limited debugging and monitoring. Because serverless applications are composed of decoupled microservices, troubleshooting can be challenging. Serverless computing depends on microservices that often run on a complex, distributed backend. Requests across cloud services and providers can involve a complex web of service calls using APIs, blob storage, event triggers, and more. Tracing requests and pinpointing the root cause of errors across the environment can be daunting.
What are Azure serverless technologies?
Azure serverless is a collection of cloud services technologies from Microsoft that enables organizations to develop, test, and deploy code and applications without managing server infrastructure. The Azure cloud computing, AI models, security functions, and data services are provided through a global network of data centers.
Azure serverless components: In-depth
Initially, platform-as-a-service (PaaS) solutions were offered to build, test, and deploy applications without managing infrastructure. AWS, Microsoft, and Google all launched their first PaaS offerings to build and host web applications. Later, cloud-based service offerings were integrated with PaaS to include infrastructure-as-a-service (IaaS) and software-as-a-service (SaaS) solutions. With the rise of Kubernetes and container-based orchestration, cloud-based serverless providers began to incorporate function-as-a-service (FaaS) solutions. Serverless computing has since expanded in several ways.
Azure serverless is a powerful set of cloud services that allows developers to focus solely on writing code, without the burden of managing infrastructure. Its development began in 2016 when Microsoft introduced Azure Functions, an event-driven compute platform designed to run small pieces of code in response to various triggers. Building on this, Durable Functions was launched, providing capabilities for orchestrating long-running, stateful workflows.
A major milestone was reached in November 2021 with the announcement of Azure Container Apps, built to support microservice architectures with serverless scaling based on Kubernetes-native technologies. The ecosystem now emphasizes integration with AI services and orchestration features via Durable Functions and Logic Apps.
Today, the Azure serverless ecosystem includes Azure Functions, Durable Functions, Logic Apps, Event Grid, Azure Static Web Apps, and Azure Container Apps.
Azure serverless has been conceived as a toolbox for building scalable, resilient, and cost-effective solutions meant for small tasks or an entire enterprise platform. Unlike AWS and Google Cloud Platform serverless computing, Azure provides deep integration with Microsoft products and technologies, which can benefit developers focused on those technologies.
Key Azure features provide expanded capabilities and powerful services to complement application and back-end development. Azure is particularly suited for complex workflows, web and mobile app development, and offloading key processing to serverless containers.
Challenges associated with serverless compute on Azure
When considering a serverless solution, serverless computing on Azure presents several key challenges for organizations, as shown below.
Security:
Ensuring the security of data and applications in the cloud is a major concern.
Organizations should identify security risks such as data breaches, compliance violations, and misconfigurations for user permissions.
Adherence to security features such as Azure Security Center, Azure Policy, and Microsoft Entra ID (formerly Azure Active Directory) is crucial.
Cost management:
Additional support for monitoring, tracing, and observability should be factored into the overall cost.
Organizations should optimize resource utilization while avoiding unnecessary spending.
Scalability and flexibility:
- Organizations need to recognize that Azure resources can scale up or down to meet changing demands and costs.
Integration:
Integrating Azure with existing systems and applications can be complex.
Some legacy applications were not designed for the cloud.
Expertise:
- Organizations should consider investing in expertise or monitoring solutions to help with implementation and cloud infrastructure.
Azure services use cases
Consider the following use case examples for Azure serverless technologies:
Azure Functions: Azure Functions are well-suited for tasks such as processing data streams, running scheduled jobs, building lightweight web APIs, and integrating with other Azure services. For example, a backend chatbot for Microsoft Teams might include message handling/delivery and running background tasks.
Azure Container Apps: Examples of Azure Container Apps include running APIs and web applications within containers, container-based jobs that need to start and stop on demand, and batch processing.
Azure Logic Apps: Use cases for Azure Logic Apps include automating business processes or workflows that involve integration with multiple services without writing a large amount of code.
Azure App Service: Azure App Service use cases include hosting web applications, REST APIs, and mobile back ends that need fast deployment, built-in scaling, and integrated DevOps tools.
Monitoring for serverless: what features should users look for?
Through end-to-end distributed tracing, developers can visualize the full journey of a request and pinpoint any performance failures or bottlenecks that occur along the way. Distributed tracing makes it clear where an error occurs and identifies which team is responsible for fixing that error. Datadog offers seamless integration with Azure serverless components:
Datadog offers monitoring and tracing solutions for Azure Functions, Azure App Services, Azure Functions, Azure Container Apps, and Logic Apps.
Datadog’s monitoring capabilities include customizable alerting, monitors, and visualization tools like out-of-the-box dashboards, making it easy and fast to investigate and resolve issues, with 900+ vendor-backed integrations, including Azure App Services, Azure Container Apps, Azure Logic Apps, and more.
Gain full context and visibility by correlating Azure Functions data alongside the rest of your Azure Apps.
Make use of out-of-the-box, end-to-end tracing for Azure Functions, Azure App Services, and Azure Container Apps to instantly visualize traces.
The Datadog sidecar instrumentation for Azure App Service offers a seamless, centralized monitoring experience.
Quickly identify root causes by automatically correlating traces with your logs, infrastructure metrics, and database queries—all in one view.
Proactively detect and address potential issues, such as errors and cold starts, to accelerate remediation. Datadog Serverless Monitoring provides end-to-end visibility into metrics, traces, and logs for serverless applications.
Learn more
Review these additional features offered by Datadog for serverless computing using Azure:
