Azure User Expresses Concern
A customer opened ticket SR#2407190040010082 as their consumption sku APIM service was stuck updating: Now that the service has exited that "updating" status I am able to resume working with it. The concern I want to share with you is my concern with how the system responds to a certificate error and gets stuck in that "updating" state. We know that network and login activities can fail on occasion. When APIM responds by getting stuck in that state it cannot be updated and it cannot be deleted and recreated. This issue lasted for a day before APIM eventually emerged from that state for reasons I am unaware. I was powerless and had to keep going back to check. Yes, this case is resolved but I hope that this feedback can be shared with the team in the hopes that a fix or enhancement to better handle this situation can be implemented.Running Self-hosted APIM Gateways in Azure Container Apps with VNet Integration
With Azure Container Apps we can run containerized applications, completely serverless. The platform itself handles all the orchestration needed to dynamically scale based on your set triggers (such as KEDA) and even scale-to-zero! I have been working a lot with customers recently on using Azure API Management (APIM) and the topic of how we can leverage Azure APIM to manage our internal APIs without having to expose a public IP and stay within compliance from a security standpoint, which leads to the use of a Self-Hosted Gateway. This offers a managed gateway deployed within their network, allowing a unified approach in managing their APIs while keeping all API communication in-network. The self-hosted gateway is deployed as a container and in this article, we will go through how to provision a self-hosted gateway on Azure Container Apps specifically. I assume there is already an Azure APIM instance provisioned and will dive into creating and configuring the self-hosted gateway on ACA. Prerequisites As mentioned, ensure you have an existing Azure API Management instance. We will be using the Azure CLI to configure the container apps in this walkthrough. To run the commands, you need to have the Azure CLI installed on your local machine and ensure you have the necessary permissions in your Azure subscription. Retrieve Gateway Deployment Settings from APIM First, we need to get the details for our gateway from APIM. Head over to the Azure portal and navigate to your API Management instance. - In the left menu, under Deployment and infrastructure, select Gateways. - Here, you'll find the gateway resource you provisioned. Click on it and go to Deployment. - You'll need to copy the Gateway Token and Configuration endpoint values. (these tell the self-hosted gateway which APIM instance and Gateway to register under) Create a Container Apps Environment Next, we need to create a Container Apps environment. This is where we will create the container app in which our self-hosted gateway will be hosted. Using Azure CLI: Create our VNet and Subnet for our ACA Environment As we want access to our internal APIs, when we create the container apps environment, we need to have the VNet created with a subnet available. Note: If we’re using Workload Profiles (we will in this walkthrough), then we need to delegate the subnet to Microsoft.App/environments. # Create the vnet az network vnet create --resource-group rgContosoDemo \ --name vnet-contoso-demo \ --location centralUS \ --address-prefix 10.0.0.0/16 # Create the subnet az network vnet subnet create --resource-group rgContosoDemo \ --vnet-name vnet-contoso-demo \ --name infrastructure-subnet \ --address-prefixes 10.0.0.0/23 # If you are using a workload profile (we are for this walkthrough) then delegate the subnet az network vnet subnet update --resource-group rgContosoDemo \ --vnet-name vnet-contoso-demo \ --name infrastructure-subnet \ --delegations Microsoft.App/environments Create the Container App Environment in out VNet az containerapp env create --name aca-contoso-env \ --resource-group rgContosoDemo \ --location centralUS \ --enable-workload-profiles Deploy the Self-Hosted Gateway to a Container App Creating the environment takes about 10 minutes and once complete, then comes the fun part—deploying the self-hosted gateway container image to a container app. Using Azure CLI: Create the Container App: az containerapp create --name aca-apim-demo-gateway \ --resource-group rgContosoDemo \ --environment aca-contoso-env \ --workload-profile-name "Consumption" \ --image "mcr.microsoft.com/azure-api-management/gateway:2.5.0" \ --target-port 8080 \ --ingress 'external' \ ---env-vars "config.service.endpoint"="<YOUR_ENDPOINT>" "config.service.auth"="<YOUR_TOKEN>" "net.server.http.forwarded.proto.enabled"="true" Here, you'll replace <YOUR_ENDPOINT> and <YOUR_TOKEN> with the values you copied earlier. Configure Ingress for the Container App: az containerapp ingress enable --name aca-apim-demo-gateway --resource-group rgContosoDemo --type external --target-port 8080 This command ensures that your container app is accessible externally. Verify the Deployment Finally, let's make sure everything is running smoothly. Navigate to the Azure portal and go to your Container Apps environment. Select the container app you created (aca-apim-demo-gateway) and navigate to Replicas to verify that it's running. You can use the status endpoint of the self-hosted gateway to determine if your gateway is running as well: curl -i https://aca-apim-demo-gateway.sillytreats-abcd1234.centralus.azurecontainerapps.io/status-012345678990abcdef Verify Gateway Health in APIM You can navigate in the Azure Portal to APIM and verify the gateway is showing up as healthy. Navigate to Deployment and Infrastructure, select Gateways then choose your Gateway. On the Overview page you’ll see the status of your gateway deployment. And that’s it! You've successfully deployed an Azure APIM self-hosted gateway in Azure Container Apps with VNet integration allowing access to your internal APIs with easy management from the APIM portal in Azure. This setup allows you to manage your APIs efficiently while leveraging the scalability and flexibility of Azure Container Apps. If you have any questions or need further assistance, feel free to ask. How are you feeling about this setup? Does it make sense, or is there anything you'd like to dive deeper into?Calculating Chargebacks for Business Units/Projects Utilizing a Shared Azure OpenAI Instance
Azure OpenAI Service is at the forefront of technological innovation, offering REST API access to OpenAI's suite of revolutionary language models, including GPT-4, GPT-35-Turbo, and the Embeddings model series. Enhancing Throughput for Scale As enterprises seek to deploy OpenAI's powerful language models across various business units, they often require granular control over configuration and performance metrics. To address this need, Azure OpenAI Service is introducing dedicated throughput, a feature that provides a dedicated connection to OpenAI models with guaranteed performance levels. Throughput is quantified in terms of tokens per second (tokens/sec), allowing organizations to precisely measure and optimize the performance for both prompts and completions. The model of provisioned throughput provides enhanced management and adaptability for varying workloads, guaranteeing system readiness for spikes in demand. This capability also ensures a uniform user experience and steady performance for applications that require real-time responses. Resource Sharing and Chargeback Mechanisms Large organizations frequently provision a singular instance of Azure OpenAI Service that is shared across multiple internal departments. This shared use necessitates an efficient mechanism for allocating costs to each business unit or consumer, based on the number of tokens consumed. This article delves into how chargeback is calculated for each business unit based on their token usage. Leveraging Azure API Management Policies for Token Tracking Azure API Management Policies offer a powerful solution for monitoring and logging the token consumption for each internal application. The process can be summarized in the following steps: ** Sample Code: Refer to this GitHub repository to get a step-by-step instruction on how to build the solution outlined below : private-openai-with-apim-for-chargeback 1. Client Applications Authorizes to API Management To make sure only legitimate clients can call the Azure OpenAI APIs, each client must first authenticate against Azure Active Directory and call APIM endpoint. In this scenario, the API Management service acts on behalf of the backend API, and the calling application requests access to the API Management instance. The scope of the access token is between the calling application and the API Management gateway. In API Management, configure a policy (validate-jwt or validate-azure-ad-token) to validate the token before the gateway passes the request to the backend. 2. APIM redirects the request to OpenAI service via private endpoint. Upon successful verification of the token, Azure API Management (APIM) routes the request to Azure OpenAI service to fetch response for completions endpoint, which also includes prompt and completion token counts. 3. Capture and log API response to Event Hub Leveraging the log-to-eventhub policy to capture outgoing responses for logging or analytics purposes. To use this policy, a logger needs to be configured in the API Management: # API Management service-specific details $apimServiceName = "apim-hello-world" $resourceGroupName = "myResourceGroup" # Create logger $context = New-AzApiManagementContext -ResourceGroupName $resourceGroupName -ServiceName $apimServiceName New-AzApiManagementLogger -Context $context -LoggerId "OpenAiChargeBackLogger" -Name "ApimEventHub" -ConnectionString "Endpoint=sb://<EventHubsNamespace>.servicebus.windows.net/;SharedAccessKeyName=<KeyName>;SharedAccessKey=<key>" -Description "Event hub logger with connection string" Within outbound policies section, pull specific data from the body of the response and send this information to the previously configured EventHub instance. This is not just a simple logging exercise; it is an entry point into a whole ecosystem of real-time analytics and monitoring capabilities: <outbound> <choose> <when condition="@(context.Response.StatusCode == 200)"> <log-to-eventhub logger-id="TokenUsageLogger">@{ var responseBody = context.Response.Body?.As<JObject>(true); return new JObject( new JProperty("Timestamp", DateTime.UtcNow.ToString()), new JProperty("ApiOperation", responseBody["object"].ToString()), new JProperty("AppKey", context.Request.Headers.GetValueOrDefault("Ocp-Apim-Subscription-Key",string.Empty)), new JProperty("PromptTokens", responseBody["usage"]["prompt_tokens"].ToString()), new JProperty("CompletionTokens", responseBody["usage"]["completion_tokens"].ToString()), new JProperty("TotalTokens", responseBody["usage"]["total_tokens"].ToString()) ).ToString(); }</log-to-eventhub> </when> </choose> <base /> </outbound> EventHub serves as a powerful fulcrum, offering seamless integration with a wide array of Azure and Microsoft services. For example, the logged data can be directly streamed to Azure Stream Analytics for real-time analytics or to Power BI for real-time dashboards With Azure Event Grid, the same data can also be used to trigger workflows or automate tasks based on specific conditions met in the incoming responses. Moreover, the architecture is extensible to non-Microsoft services as well. Event Hubs can interact smoothly with external platforms like Apache Spark, allowing you to perform data transformations or feed machine learning models. 4: Data Processing with Azure Functions An Azure Function is invoked when data is sent to the EventHub instance, allowing for bespoke data processing in line with your organization’s unique requirements. For instance, this could range from dispatching the data to Azure Monitor, streaming it to Power BI dashboards, or even sending detailed consumption reports via Azure Communication Service. [Function("TokenUsageFunction")] public async Task Run([EventHubTrigger("%EventHubName%", Connection = "EventHubConnection")] string[] openAiTokenResponse) { //Eventhub Messages arrive as an array foreach (var tokenData in openAiTokenResponse) { try { _logger.LogInformation($"Azure OpenAI Tokens Data Received: {tokenData}"); var OpenAiToken = JsonSerializer.Deserialize<OpenAiToken>(tokenData); if (OpenAiToken == null) { _logger.LogError($"Invalid OpenAi Api Token Response Received. Skipping."); continue; } _telemetryClient.TrackEvent("Azure OpenAI Tokens", OpenAiToken.ToDictionary()); } catch (Exception e) { _logger.LogError($"Error occured when processing TokenData: {tokenData}", e.Message); } } } In the example above, Azure function processes the tokens response data in Event Hub and sends them to Application Insights telemetry, and a basic Dashboard is configured in Azure, displaying the token consumption for each client application. This information can conveniently be used to compute chargeback costs. A sample query used in dashboard above that fetches tokens consumed by a specific client: customEvents | where name contains "Azure OpenAI Tokens" | extend tokenData = parse_json(customDimensions) | where tokenData.AppKey contains "your-client-key" | project Timestamp = tokenData.Timestamp, Stream = tokenData.Stream, ApiOperation = tokenData.ApiOperation, PromptTokens = tokenData.PromptTokens, CompletionTokens = tokenData.CompletionTokens, TotalTokens = tokenData.TotalTokens Azure OpenAI Landing Zone reference architecture A crucial detail to ensure the effectiveness of this approach is to secure the Azure OpenAI service by implementing Private Endpoints and using Managed Identities for App Service to authorize access to Azure AI services. This will limit access so that only the App Service can communicate with the Azure OpenAI service. Failing to do this would render the solution ineffective, as individuals could bypass the APIM/App Service and directly access the OpenAI Service if they get hold of the access key for OpenAI. Refer to Azure OpenAI Landing Zone reference architecture to build a secure and scalable AI environment. Additional Considerations If the client application is external, consider using an Application Gateway in front of the Azure APIM If "streaming" is set to true, tokens count is not returned in response. In that that case libraries like tiktoken (Python), orgpt-3-encoder(javascript) for most GPT-3 models can be used to programmatically calculate tokens count for the user prompt and completion response. A useful guideline to remember is that in typical English text, one token is approximately equal to around 4 characters. This equates to about three-quarters of a word, meaning that 100 tokens are roughly equivalent to 75 words. (P.S. Microsoft does not endorse or guarantee any third-party libraries.) A subscription key or a custom header like app-key can also be used to uniquely identify the client as appId in OAuth token is not very intuitive. Rate-limiting can be implemented for incoming requests using OAuth tokens or Subscription Keys, adding another layer of security and resource management. The solution can also be extended to redirect different clients to different Azure OpenAI instances. For example., some clients utilize an Azure OpenAI instance with default quotas, whereas premium clients get to consume Azure Open AI instance with dedicated throughput. Conclusion Azure OpenAI Service stands as an indispensable tool for organizations seeking to harness the immense power of language models. With the feature of provisioned throughput, clients can define their usage limits in throughput units and freely allocate these to the OpenAI model of their choice. However, the financial commitment can be significant and is dependent on factors like the chosen model's type, size, and utilization. An effective chargeback system offers several advantages, such as heightened accountability, transparent costing, and judicious use of resources within the organization.Reimagining App Modernization for the Era of AI
This blog highlights the key announcements and innovations from Microsoft Build 2025. It focuses on how AI is transforming the software development lifecycle, particularly in app modernization. Key topics include the use of GitHub Copilot for accelerating development and modernization, the introduction of Azure SRE agent for managing production systems, and the launch of the App Modernization Guidance to help organizations modernize their applications with AI-first design. The blog emphasizes the strategic approach to modernization, aiming to reduce complexity, improve agility, and deliver measurable business outcomesGenAI Gateway Accelerator
Every app will be reinvented with Generative AI and new apps will be built that weren’t possible before. Generative AI helps to build intelligent apps using the Large Language Model (LLM) capabilities. As the number of intelligent applications grows alongside the adoption of various large language models (LLMs), enterprises encounter significant challenges in efficiently federating and managing generative AI resources. Large enterprises look for a centralized solution “Gen AI Gateway” that must seamlessly integrate, optimize, and distribute the workloads across a federated network of GenAI resources. This blog post provides an overview of how Azure API Management can be used as a GenAI Gateway leveraging the new accelerator scenario named “GenAI Gateway Accelerator” published on the APIM Landing Zone Accelerator.AI Resilience: Strategies to Keep Your Intelligent App Running at Peak Performance
Stay Online Reliability. It's one of the 5 pillars of Azure Well-Architect Framework. When starting to implement and go-to-market any new product witch has any integration with Open AI Service you can face spikes of usage in your workload and, even having everything scaling correctly in your side, if you have an Azure Open AI Services deployed using PTU you can reach the PTU threshold and them start to experience some 429 response code. You also will receive some important information about the when you can retry the request in the header of the response and with this information you can implement in your business logic a solution. Here in this article I will show how to use the API Management Service policy to handle this and also explore the native cache to save some tokens! Architecture Reference The Azure Function in the left of the diagram just represent and App request and can be any kind of resource (even in an On-Premisse environment). Our goal in this article is to show one in n possibilities to handle the 429 responses. We are going to use API Management Policy to automatically redirect the backend to another Open AI Services instance in other region in the Standard mode, witch means that the charge is going to be only what you use. First we need to create an API in our API Management to forward the requests to your main Open AI Services (region 1 in the diagram). Now we are going to create this policy in the API call request: <policies> <inbound> <base /> <set-backend-service base-url="<your_open_ai_region1_endpoint>" /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <retry condition="@(context.Response.StatusCode == 429)" count="1" interval="5" /> <set-backend-service base-url="<your_open_ai_region2_endpoint>" /> </on-error> </policies> The first part of our job is done! Now we have an automatically redirect to our OpenAI Services deployed at region 2 when our PTU threshold is reached. Cost consideration So now you can ask me: and about my cost increment for using API Management? Even if you don't want to use any other feature on API Management you can leverage of the API Management native cache and, once again using policy and AI, put some questions/answers in the built-in Redis* cache using semantic cache for Open AI services. Let's change our policy to consider this: <policies> <inbound> <base /> <azure-openai-semantic-cache-lookup score-threshold="0.05" embeddings-backend-id ="azure-openai-backend" embeddings-backend-auth ="system-assigned" > <vary-by>@(context.Subscription.Id)</vary-by> </azure-openai-semantic-cache-lookup> <set-backend-service base-url="<your_open_ai_region1_endpoint>" /> </inbound> <backend> <base /> </backend> <outbound> <base /> <azure-openai-semantic-cache-store duration="60" /> </outbound> <on-error> <retry condition="@(context.Response.StatusCode == 429)" count="1" interval="5" /> <set-backend-service base-url="<your_open_ai_region2_endpoint>" /> </on-error> </policies> Now, API Management will handle the tokens inputted and use semantic equivalence and decide if its fit with cached information or redirect the request to your OpenAI endpoint. And, sometime, this can help you to avoid reach the PTU threshold as well! * Check the tier / cache capabilities to validate your business solution needs with the API Management cache feature: Compare API Management features across tiers and cache size across tiers. Conclusion API Management offers key capabilities for AI that we are exploring in this article and also others that you can leverage for your intelligent applications. Check it out on this awesome AI Gateway HUB repository At least but not less important, dive in API Management features with experts in the field inside the API Management HUB. Thanks for reading and Happy Coding!
Issue with Custom Domain on APIM and Cloudflare Proxying
Dear all, Last week, we attempted to configure a custom domain name for our Azure API Management (APIM) instance. We use Cloudflare as our DNS provider. The required CNAME record was created with the proxied attribute enabled. However, when configuring the custom hostname in Azure, we encountered the following error: Invalid parameter: CustomHostnameOwnershipCheckFailed. A CNAME record pointing from apim.ourowndomain.net to apim.azure-api.net was not found. As a workaround, we disabled the proxied attribute in Cloudflare, retried the configuration, and it worked successfully. We then re-enabled the proxied attribute, and the custom domain continued to function correctly. However, yesterday, we discovered that the custom domain was no longer working and returned a "404 Web site not found" error page. After extensive troubleshooting—including disabling the proxied attribute on the CNAME record—we were unable to resolve the issue. To restore functionality, we removed and reconfigured the custom domain by following the same steps: Disable the proxied attribute on the CNAME record. Configure the custom domain in APIM. Re-enable the proxied attribute. This resolved the issue again. We suspect that Azure initially validates the CNAME record during the custom domain configuration process when the proxied attribute is disabled. However, after a few days, Azure appears to revalidate the CNAME record and expects it to resolve to *.azure-api.net. Since Cloudflare returns its own IPs when proxying is enabled, Azure may reject the custom domain configuration, leading to the issue. Can anyone confirm whether our assumption is correct? Additionally, is there a recommended workaround for this issue? We are considering deploying a reverse proxy (Application Gateway) to handle Cloudflare requests and forward them to the APIM instance. Thank you in advance for your help. Best regards,API teams and Platform teams for better API management
This article is written partly as a conversation. As developers, we usually have questions and the idea is to lay out these questions and answer them in a conversational manner thereby making it easier to understand. API management Let's provide some context what API management is all about. API management is the process of creating and publishing web APIs, enforcing their usage policies, controlling access, nurturing the subscriber community, collecting and analyzing usage statistics, and reporting on performance. API management helps organizations publish APIs to external, partner, and internal developers to unlock the potential of their data and services. All that sounds like a nice pitch, but what does it really mean? Let's break it down. You're a developer, you build an API and you may even deploy it and you have a few users even. Then what happens? You need to manage it. By managing it we mean that you need to: Control who can access it, this can be as complicated as you like. You may start with an admin users and normal uses and may define therefore different access levels. That's great for a while. But what if you have a lot of users? What if your API is used in a large organization, with different departments, what if your API is used in many different Apps? You need to manage all that. Monitor its usage. Monitoring, you need to know how your API is used. You need to know how many times it's called, by whom, when, how long it takes to respond, all to ensure your users are happy and you use your resources efficiently. The latter is especially important if you have a lot of users and if you pay for resources. Secure it. In most cases, you can't ship something without securing it. You need to ensure that only the right people at the right time can access your API and you need to ensure that your API is not misused. Scale it. Of course, scaling is a big topic. Just put it in the cloud right? Well, yes, but you need to ensure that your API can handle the load. You need to ensure that your API can scale up and down as needed. There's a lot to think about here. Ok, so I pick the right libraries and deploy it to the cloud, do I need a cloud service specifically focusing on API management? Yes, well, as with everything it depends, if you feel some of the following symptoms, you might want to consider using an API management service: Symptom 1: Inconsistent API Performance: If your APIs are experiencing frequent performance issues, such as slow response times or high error rates, an Azure API Management service can help monitor, diagnose, and optimize API performance. Symptom 2: Security Concerns: If you're worried about unauthorized access to your APIs or data breaches, Azure API Management services provide robust security features, including authentication, authorization, and threat protection. Symptom 3: Complex API Lifecycle management: Managing multiple API versions, deprecating old APIs, and ensuring seamless transitions can be challenging. Azure API Management services offer tools for API lifecycle management, simplifying these processes. Symptom 4: Lack of Analytics and Monitoring: If you lack visibility into API usage, performance metrics, and error rates, Azure API Management services provide comprehensive analytics and monitoring capabilities to help you track and optimize API usage. Symptom 5: Integration Challenges: If integrating various applications, systems, and services is becoming increasingly complex, Azure API Management services can centralize and streamline these integrations, making it easier to connect different components of your technology stack. Ok, so know we know what to look for in terms of symptoms, but let's talk about API teams and platform teams next. API Teams and Platform Teams You usually don't start out with a large organization with many APIs and many users. You start small and grow your business from there. As your business grows, you may have more APIs, more users and you find the need to have different teams to manage your APIs. You might have an API team and Operations team (OPS). At some point though you start seeing problems like the below: Scalability and Standardization: Your company is growing rapidly and needs to standardize processes across multiple teams and projects, platform teams can help create consistent, reusable infrastructure and tools. Complex Integrations: If you're dealing with complex integrations between various systems and services. Security and Compliance: As your company scales, maintaining security and compliance becomes more challenging. Operational Efficiency: When operational inefficiencies start to impact productivity. Cloud Adoption: You've started using the cloud and is not using it to its full potential, for example inefficient resource allocation. Let's have a chat with Ella and Dave next to see where they are in their journey. Meet Ella and Dave Meet Ella and Dave. Ella is an experienced developer who has been using Azure API Management for a while and even workspaces. Dave is new to Azure API Management and is looking to learn more about it. New to Azure API Management Dave: Hey Ella, I recently signed up for Azure API Management to manage my APIs. I heard you’ve been using Azure API Management workspaces. Can you tell me more about it? Ella: Absolutely, Dave! Azure API Management workspaces have been a game-changer for our team. They provide a structured environment for managing APIs, which is especially useful for federated API management. "Federated" API management Dave: Federated API management? What’s that exactly? Ella: Federated API management is a decentralized approach where different teams within an organization manage their APIs independently. This allows for greater flexibility and responsiveness. However, it still maintains centralized governance to ensure consistency, security, and compliance across the organization. Dave: That sounds interesting. Why is federated API management so important? Ella: There are several reasons: Enhanced Flexibility: Teams can manage their APIs independently, allowing them to respond quickly to specific needs and changes without waiting for centralized approval. Improved Collaboration: Different teams can work on their APIs simultaneously, fostering collaboration and reducing bottlenecks. Scalability: As organizations grow, federated API management allows for scalable API management practices that can adapt to increasing complexity and volume. Centralized Governance: Despite decentralization, centralized governance ensures that all APIs adhere to organizational standards, security policies, and compliance requirements. Dave: That makes sense. So, who are the main teams involved in federated API management? API teams and Platform Teams Ella: Primarily, there are two key teams: Platform Teams: They are responsible for the overall infrastructure and tools that support API development and management. They ensure that the API management platform is robust, scalable, and secure. API Teams: These teams focus on the development, maintenance, and optimization of individual APIs. They create APIs that meet specific business needs and integrate seamlessly with other systems. How do Azure API Management workspace enable federated API management? Dave: Got it. How do Azure API Management workspaces enable federated API management? Ella: Workspaces are the only way to use federated API management in Azure API Management. It enables it through isolation of control plane, optional isolation of data plane, and platform-level governance controls. Control plane isolation. This allows teams to independently build and manage their APIs. API Runtime isolation. Isolate faults. API platform team controls. Federate API monitoring, enforce policies, and unify API discovery. Dave: So, it sounds like workspaces really help with collaboration and governance. Ella: workspaces isolate teams and give them autonomy to more effortlessly manage APIs without running into conflicts with each other. Using workspaces enables that isolation between existing teams and future teams that will be onboarded onto the platform, which makes it easier to grow, scale, and operate API ecosystems. Getting started Dave: That’s really helpful, Ella. I think I’ll look into setting up Azure API Management workspaces for my team. Any tips on getting started? Ella: Sure! Check out the below resources. Dave: Thanks, Ella. This has been really insightful. I’m excited to get started with Azure API Management workspaces! Ella: Anytime, Dave! Feel free to reach out if you have any more questions. Good luck with your APIs! Resources: Workspaces in Azure API Management | Microsoft Learn Set up a workspace in Azure API Management | Microsoft Learn Deploy an app to Azure API Management with generative AI features
