Tired of managing your Kubernetes clusters all on your own? Don’t have the time to figure out how to deploy, run, and optimize usage? Azure has just the thing for you: Azure Kubernetes Services.  This article will cover everything you need to know about Azure Kubernetes Services, how it works, what the Azure pricing will be, whether you should use it, and, if so, how to save on your cloud cost.  What is AKS (Azure Kubernetes Service)? [caption id="attachment_16615" align="aligncenter" width="512"] Source: Microsoft[/caption] Azure Kubernetes Service, or AKS, is Azure's container management service. AKS simplifies deploying and managing Kubernetes clusters by handling annoying tasks like infrastructure scaling, provisioning, and patching. You can do it all from Azure's cluster of virtual machines (VMs).  AKS is a flexible tool. It lets you integrate with other standard Azure services that make managing and optimizing your cloud usage easy (ex, Azure Monitor, Azure Policy, Azure Container Registry, and more).  You can view your new Kubernetes cluster setup from Azure's command-line interface (CLI) or a web-based dashboard. That means pushing automatic scaling, rolling updates, and self-healing features live is as easy as clicking a button.  How does AKS pricing work?   Good news: AKS has a pretty simple pricing structure.  Management of Kubernetes clusters comes completely free. You'll only have to pay for the following:  Nodes where Kubernetes clusters are stored  Virtual Machines (VMs)  Storage  Networks If you’re using AKS, you can access other Azure resources like Azure DevOps, Azure Virtual Networks, Azure Functions – you can even use Azure Backup to restore old clusters. But remember: you will be charged for these resources as you use them.    AKS prices vary from user to user. The following are the main culprits behind a larger bill:  Service type  Location  Usage  Payment plan Azure offers a wide range of payment plans, so you have some flexibility in deciding when and how much you want to pay for Kubernetes services.  Free Azure Kubernetes Service's free trial lasts for up to 12 months. You'll get access to monitoring, logging, automatic updates, and other basic features. Azure will also grant you a $200 credit that you must use within your first 30 days. Once you've used that money, you'll be moved to a pay-as-you-go Azure pricing model.  Why use the free tier?  It’s perfect if you’re just looking to test AKS’ features. It's also a good place to start if you only work with a small-scale testing and development environment. However, it's not the best choice if you're trying to find a home for larger-scale, long-term projects.  Pay-as-you-go As mentioned above, pay-as-you-go prices vary depending on your location. For the Eastern U.S., prices can start as low as $0.10 per cluster per hour and increase to $0.60 per cluster per hour. Azure offers a service level agreement promising 99.95% uptime for Availability Zone clusters and 99.9% for any other cluster.  Why go with this option?  If you need more resources than the free tier offers, you'll likely want to choose the pay-as-you-go tier. This option should also appeal if you're managing a variable workload or only need to deploy quickly.  This choice might suit you if you're uncertain about committing to a long-term contract with Azure but require more resources than the free plan offers. You'll pay a bit more, but you'll get a lot more flexibility in return.  Reserved VM instances Azure reserved instances are cheaper than the pay-as-you-go pricing model but require a one- or three-year agreement to a set amount of services. You can save up to 72% on your pay-as-you-go price, but you'll want to make sure you know exactly how many VMs and how many storage resources you need for either a one or three-year period. This is because if you use under your chosen amount, you're losing money, and if you use over, you're back to pay-as-you-go prices for however much you exceed your agreed-upon resources. If your workload is unpredictable, this isn't the best choice, but if you can estimate how many resources you'll need, you can save big with reserved instances.  Savings Plans Azure Savings Plans are still pretty new to the scene, having only been introduced in 2022. Like reserved instances, users have to commit to a fixed hourly amount for either a one- or three-year period. You can save up to 65% on pay-as-you-go compute spend.  Where Savings Plans don't offer as much discount as reserved instances, you'll have more access to other services like Azure SQL database, Azure Cosmos DB, other types of compute, various VMs, and more.  Spot VMs If you're really looking to save big, go with Spot VMs. Just be prepared for unpredictability.  Spot VMs let you purchase Azure capacity for up to 90% discounts, but Azure can evict you at will with only a 30-second notice. Azure uses Spot VMs to sell unused compute space at lower prices, but once that speed becomes needed, Azure can start to lower your discount rate, and, if necessary, remove you from the Spot VM.  Spot VMs are really only a good choice if you're managing a workload that doesn't mind being interrupted, like a production workload or a development and testing environment.  Azure Hybrid Benefit Azure Hybrid Benefit allows on-premise users with current Windows Server subscriptions, SQL licenses, or Software Assurance to get reduced rates for VMs. Applicable users can pay for a lower rate to run VMs on either a Windows or SQL Server, saving you up to 85% compared to pay-as-you-go prices.  Azure Hybrid Benefit isn't specific to AKS. You can apply it to any Azure tool if you're running it on an Azure VM.   [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] How to optimize your Azure Kubernetes Service costs Beyond picking out a savings plan that helps you cut back on costs, there are other ways you can make your accounting team happy by decreasing your AKS spend.  AKS node pools Node pools are how VMs are grouped to run your Kubernetes nodes. Depending on your application needs, you can create various node pools of different VM sizes. Save money by optimizing each for maximum performance and using cost-effective VM sizes.  Dynamic autoscaling Dynamic autoscaling is an AKS-specific feature. It lets you automatically adjust how many nodes you have in a cluster based on how your resources are used, helping you cut your budget with a single click.  Pick the right VM type  Azure offers a wide variety of VM types:  General Purpose  GPU  High-Performance  Compute Memory  Optimized Storage  Optimized Compute  Review these different options and pick the VM that best suits your resource requirements and workload type. If your workload can support lower cost-per-performance VMs, consider them.  Rightsize pods & containers Make sure your pods and containers match your resources needs. Do this by carefully monitoring containers and even adjusting container limits to prevent waste. This helps you avoid overprovisioning and makes you much more efficient. If you don’t have the time to take on this task yourself, using a third-party monitoring tool like Anodot can make this task as simple as a click of a button.  Eliminate unused resources Regularly reviewing and reducing unused resources can save you a hefty sum in the long run. Just make sure you remain diligent. Is there anything that has remained idle for a notable length of time? Consider if you really need that extra storage space or VM.  This task works great if outsourced to a cloud-monitoring organization like Anodot!  Use a cost management tool   [caption id="attachment_16617" align="aligncenter" width="512"] Source: Microsoft[/caption] As we mentioned above, AKS comes with various dashboards and (good news!) cost management tools. You can see an example of what this dashboard would look like for you.  Tools like Azure Cost Management and Azure Advisor can tell you where you should pull back or spend more... but (bad news!) there's one serious drawback: these are tools powered by Azure. They typically don't offer in-depth analyses, and they certainly can't offer you a multicloud view. If you're really looking for an AKS cloud cost management tool that can actually help you save considerable time, you're going to want to go third-party.  Get complete visibility into your AKS spend   The 2022 State of Cloud Strategy survey stated 94% of users felt they were wasting money on their cloud investments. Even with AKS managing your Kubernetes clusters and Azure-powered monitoring at your fingertips, it’s easy to overspend on the cloud. Azure’s insights are useful to start with, but cost management, especially multicloud cost management, can quickly become more than a full time job, especially when navigating such low visibility. That’s where third-party cloud cost management tools like Anodot come in.  Anodot prides itself on providing 100% visibility into your entire multicloud environment. That means access to data down to the hour for up to a two year retention period.  Anodot offers unparalleled insights into your Kubernetes deployment that no other cloud optimization platform can match. Effortlessly monitor usage and spending across clusters with comprehensive reports and dashboards. Leveraging Anodot’s advanced algorithms and multi-dimensional filters, you can delve into performance metrics and pinpoint under-utilization at the node level. Kubernetes Costs With Anodot’s continuous monitoring and in-depth visibility, engineers are empowered to eliminate unpredictable expenses. The platform automatically learns usage patterns for each service. It alerts relevant teams to any irregularities in cloud spending and usage anomalies, ensuring they have complete context for the quickest resolution.   [caption id="attachment_16619" align="aligncenter" width="457"] Source: Microsoft[/caption] Cloud Cost Alerts Anodot seamlessly consolidates all your cloud expenditures into a single platform, allowing you to optimize costs and resource utilization across AWS, GCP, and Azure. Revolutionize your FinOps, take control of cloud spending, and minimize waste with Anodot’s cloud cost management solution. Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.  

A credible source recommending the right cloud cost tool can help you make an informed choice that positively impacts your cloud cost optimization.

Autoscaling in cloud computing is the ability of a system to adjust its resources in response to changes in demand automatically. This guarantees that applications always have the resources they need to perform optimally, even during periods of high traffic. Autoscaling eliminates manual intervention, allowing your dev team time to focus on your product. All major cloud providers like AWS, Azure, and Google Cloud Platform offer robust autoscaling solutions with many features and capabilities. What is Autoscaling?   Autoscaling in cloud computing is a feature that automatically adjusts the number of computing resources allocated to an application or service based on its current demand. This dynamic allocation ensures applications maintain optimal performance during traffic spikes while reducing costs during low-traffic periods. Adding or removing resources as per requirements improves overall system reliability and user experience and allows businesses to manage their cloud infrastructure efficiently, paying only for the resources they actually use. What Are the Types of Autoscaling?   There are two types of autoscaling: Horizontal Scaling: This type of autoscaling, also called Scaling Out, involves adding or removing instances as needed. It is ideal for applications designed for distributed environments. Vertical Scaling: This type of autoscaling is also called Scaling Up. It involves increasing resources such as CPU, memory, etc. of existing server instances. It is suitable for applications running on single large servers rather than multiple distributed servers. How Does Autoscaling Operate on the Cloud?   Autoscaling works as follows: Monitoring: Autoscaling systems continuously monitor various metrics of your application or server, such as CPU utilization, memory usage, network traffic, response times etc. Scaling Policies: Scaling policies are the conditions under which the autoscaling should occur. This is dependent on monitoring metrics. When a specific criteria is met, instances are scaled up or down. Scaling Action: When the monitoring metrics reach a certain threshold set up in the Scaling Policies, the system automatically adds or removes instances to curate to the demand. Load Balancing: Autoscaling systems work in sync with load balancers, distributing traffic to the resources the autoscaling system provides. Load Balancing: Autoscaling systems work in sync with load balancers, distributing traffic to the resources the autoscaling system provides.   Source: TechTarget  What Are the Benefits of Autoscaling?   The benefits of autoscaling in the cloud are: High Availability and Reliability: Autoscaling helps maintain your services’ availability by automatically adding resources in case of failures. Cost Effectiveness: It helps in setting up a cost-effective cloud infrastructure. Resources are only allocated when they are required. Management Simplification: A cloud infrastructure built on autoscaling principles requires minimal human intervention and is much easier to manage. Performance Improvement: Autoscaling ensures that your services and applications can handle a sudden surge in traffic without causing performance degradation. Resource Optimization: Autoscaling helps you match your resource allocation to demand. It scales up resources when demand increases and scales down when demand decreases. This eradicates the issue of over- or under-provisioning, resulting in an efficient and cost-effective infrastructure. Autoscaling vs Load Balancing    Autoscaling is a technique that adjusts the resources allocated to an application based on its current demand. In contrast, load balancing mainly focuses on distributing incoming network traffic across multiple servers. The key differences between Autoscaling and Load balancing are: Purpose: Autoscaling scales resources up or down to match demand, while load balancing distributes traffic among existing resources. Action: Autoscaling adds or removes resources while load balancing routes requests to different resources. Metrics: Autoscaling monitors CPU utilization, memory usage, and request count, while load balancing monitors response time, connection count, and traffic throughput. Scope: Autoscaling is often applied to an entire application or group of resources, while load balancing is typically applied to incoming network traffic. We can understand the differences using the following scenario: The Scenario A Fintech firm handles a substantial volume of daily transactions and experiences predictable spikes in activity during stock market opening and closing hours. It seeks to optimize its cloud infrastructure to accommodate these fluctuations cost-effectively while maintaining seamless performance even during peak periods. Solutions Implemented by the MSP Autoscaling: The Fintech’s MSP for cloud management utilizes autoscaling to dynamically adjust the number of cloud servers in real-time, responding to current transaction loads. This approach guarantees that the infrastructure is consistently optimized and appropriately sized. Load Balancing: The MSP configures load balancing to distribute incoming transactions intelligently across all available servers. This prevents any single server from becoming overwhelmed, ensuring optimal resource utilization and consistent transaction processing speeds even under heavy loads. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] How Autoscaling and Load Balancing Work Together    As autoscaling adds or removes servers, the load balancer automatically updates its configuration to include or exclude them from traffic distribution. When autoscaling and load balancing are used in tandem, they provide: Seamless Scalability: The infrastructure can smoothly handle sudden increases in transaction volume by adding servers, i.e., autoscaling, and then efficiently distributing the traffic across them using load balancing. Cost Optimization: During periods of low activity, unnecessary servers are removed using autoscaling, which minimizes cloud spending. High Availability: Load balancing ensures traffic is automatically redirected to other healthy servers if one server fails, preventing disruptions. Performance Optimization: Load balancing prevents any single server from becoming a bottleneck and gives consistent performance even under high loads. Autoscaling and Cloud Providers   Autoscaling is a fundamental feature most major cloud providers offer, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). While the core concept remains the same – automatically adjusting resources to match demand – each provider implements autoscaling with its unique tools and terminology. Autoscaling in different cloud providers: AWS: Amazon Web Services offers Auto Scaling Groups (ASGs) that allow you to define scaling policies for different resource types like EC2 instances, ECS tasks, and DynamoDB tables. AWS also provides various scaling options like target tracking, step scaling, scheduled scaling etc Azure: Microsoft Azure offers Virtual Machine Scale Sets (VMSS) for autoscaling virtual machines and Azure Container Instances (ACI) for autoscaling containers. Azure also provides autoscaling for various Azure services, such as App Service, Azure Functions, Cosmos DB, etc. GCP: Google Cloud Platform offers Managed Instance Groups (MIGs) for autoscaling Compute Engine instances and Kubernetes Engine clusters. GCP also provides autoscaling for Cloud Functions, Cloud Run, etc. Related content: Read our guide to VMware CloudHealth Challenges of Autoscaling and How Anodot Provides Assistance   While offering significant benefits, autoscaling presents complex challenges that can strain resources and expertise. Partnering with seasoned industry professionals can be a strategic move to navigate these complexities effectively. Companies like Anodot, with their deep FinOps expertise in cloud scalability solutions, can provide invaluable support in optimizing your cloud infrastructure for growth and efficiency. Complexity: As a company's cloud infrastructure grows, it becomes very complex to manage. Managing scale and configurations across many services becomes a complex and tedious task. How Anodot helps: Anodot simplifies the management of complex cloud infrastructures by providing unified visibility, AI-powered insights, automated optimization recommendations, and customizable dashboards and reports. This helps you to gain control over your cloud environment, reduce costs, and improve efficiency.   Security: As scaling increases the attack surface and introduces new security challenges, it becomes essential that scaling maintains robust security measures (like encryption and intrusion detection), especially in newly allocated instances. How Anodot helps: Anodot provides AI-driven alerting out of the box and allows users to set up custom alerts and dashboards that track key security metrics, such as network traffic, access logs, and resource utilization. These alerts can be triggered when anomalies or unusual patterns are detected, helping teams proactively respond to potential security risks.   Cost Management: Scaling can lead to unexpected and unpredictable cost increases if not managed properly. Therefore, it is important to implement cost optimization strategies and choose the appropriate scaling configurations. How Anodot helps: Anodot’s insights can help identify unutilized resources, which can prevent overspending on unnecessary resources.   Predicting Scaling Requirements: In a complex cloud environment, proactively predicting scaling needs is often very challenging. How Anodot helps: Anodot uses artificial intelligence to analyze vast amounts of metrics in real-time. It can interpret unusual usage patterns and anomalies, addressing potential scaling needs before leading to performance degradation and service disruption.   Root Cause Analytics: Operating applications at scale can introduce vulnerabilities, such as downtime, especially during sudden traffic surges or configuration errors. How Anodot helps: Anodot’s correlation engine helps quickly pinpoint the root cause by analyzing relationships between different metrics and events. This accelerates troubleshooting and minimizes downtime.   Performance: Scaling is not straightforward. Increasing the number of service servers is often not enough. Related performance bottlenecks such as network limitations and database constraints, must be identified. How Anodot helps: Anodot uses artificial intelligence to analyze vast amounts of metrics in real time. It can interpret unusual usage patterns and anomalies, addressing potential scaling needs before they lead to performance degradation and service disruption.   Data Management and Compliance: Large volumes of data need to be managed while performing scaling operations. Ensuring data consistency becomes crucial and tricky, especially in distributed systems. How Anodot helps: Anodot's leading industry experts are well-equipped to handle the challenges of data management and compliance at scale. Seeking the help of such professionals is often a wise choice as your cloud infrastructure grows.   Human Skill Limitations: Scaling large infrastructures requires specialized skills and expertise. Organizations must hire highly skilled and experienced professionals and invest in training their current personnel. How Anodot helps: Dealing with all these challenges is often unfeasible or resource-intensive. It’s often a good idea to seek the help of industry professionals with years of expertise in this field. Final Thoughts   Autoscaling has emerged as a crucial component of modern cloud computing, empowering businesses to dynamically adapt their infrastructure to fluctuating demands. By automating the resource allocation process, autoscaling ensures optimal performance, cost efficiency, and high availability. These characteristics are necessary for modern-day cloud infrastructures to keep in sync with ever-increasing user expectations. Downtime is no longer acceptable. Any service disruption often results in significant financial losses and a decline in user trust. Embracing autoscaling as a core strategy is not just an option but a necessity for the organization. FAQs   What Is Autoscaling? Autoscaling is a cloud computing feature that dynamically adjusts the computational resources allocated to an application or service based on demand. What Are the Types of Autoscaling? There are two types of autoscaling: Horizontal and Vertical. Horizontal scaling includes adding or removing servers, whereas vertical scaling refers to increasing existing servers' resources, such as CPU and memory. Which Cloud Providers Provide Autoscaling Functionality? All major cloud providers, such as AWS, Azure, and Google Cloud Platform, offer robust autoscaling capabilities. Some common autoscaling functionalities are  AWS Autoscaling, Google Cloud Autoscaler, and Azure VM Scale Sets.

Automat-it, a trusted AWS Premier consulting partner specializing in DevOps and FinOps, provides a comprehensive solution to maximize cloud investment ROI for its startup customers. It creates cloud solutions focused on practical applications, delivering efficiencies that save customers time to market while optimizing performance and costs. Short on time? Download our one-pager for a quick overview of how Automat-it leverages Anodot's CostGPT. Background:   Automat-it is leveraging Anodot's innovative CostGPT platform to deliver exceptional value to its clients. The company’s expansion revealed the need for a more efficient and scalable solution to optimize FinOps for its clients. Automat-it partnered with Anodot to integrate its AI-driven CostGPT platform into daily operations to meet this demand. Partnering with Automat-it and leveraging their POC recommendations, Anodot moved an agent to Amazon Bedrock from OpenAI and achieved a 30% classification performance improvement. The Amazon Bedrock-based classification agent, co-developed with Automat-it, addresses the complexity of cloud cost management by providing straightforward answers to complex queries, such as: Listing services launched within a specific timeframe. Calculating cost savings from optimization efforts across different regions. Determining the percentage coverage of various EBS storage types. Identifying services most impacted by anomalies and accounts with significant cost increases. Amazon Bedrock’s flexibility, scalability, and simplicity enabled Anodot to test alternatives to GPT-4. Benchmarking and evaluation led to the selection of Claude 2 for usage with one of the agents, which proved to be 30% faster while maintaining similar performance levels to GPT-4. [embed]https://youtu.be/fKwSqGqg8oQ[/embed] Solution: Anodot's CostGPT   Automat-it empowers its clients with Anodot's innovative CostGPT platform to deliver exceptional value. As the company grew its customer base rapidly, the demand for a more efficient, scalable solution for optimizing FinOps became evident. Automat-it integrated Anodot's AI-driven CostGPT platform into its clients' daily operations. Results:    With the implementation of Anodot CostGPT, Automat-it enabled their clients to achieve a 30% increase in operational efficiency thanks to several key capabilities: Efficient Onboarding of FinOps Engineers: Automat-it quickly onboarded engineers to support clients with access to CostGPT. This allowed engineers to understand client-specific FinOps practices swiftly, enabling them to provide impactful guidance and support immediately. Fast Client Insights without UI Hassle: CostGPT enabled Automat-it’s team to bypass complex interfaces, allowing instant access to real-time summaries of each client's cloud costs. This improved their ability to provide quick, accurate support and proactive cost management, directly benefiting their clients. Immediate Cost-Saving Opportunities: CostGPT helped Automat-it identify quick wins for clients by providing AI-driven insights and actionable recommendations. Automat-it could proactively advise clients on optimizing workloads and reducing cloud spending, solidifying their role as a trusted strategic partner. Rapid Pricing Queries for Optimal Decision-Making: Automat-it's ability to quickly assess pricing through CostGPT enabled it to recommend the most cost-effective configurations for clients' workloads. Leading to smarter financial decisions and strengthened client trust with immediate,data-driven consulting. Ira Cohen, Anodot Co-Founder, expresses his excitement about the solution: "I'm incredibly proud of my team for bringing CostGPT to life. Collaborating with professionals like the Automat-it team and leveraging Amazon bedrock solution has been a fantastic experience. The future of CostGPT is bright, with automation for processes, structures, and recommendations on the horizon." Ziv Kashtan, CEO of Automat-it, praised the partnership, stating: "Our partnership with Anodot is a testament to our shared commitment to innovation in FinOps. Together, we're pushing the boundaries and addressing comprehensive FinOps solutions to the market." Conclusion:   Through its partnership with Anodot, Automat-it transformed its service offerings, achieving a 30% efficiency boost for clients. By making CostGPT accessible to startups, they significantly enhanced customer satisfaction by providing real-time insights and cost-saving opportunities into cloud costs. Anodot’s AI-powered platform makes Automat-it a leading customer-focused FinOps provider by delivering faster insights and more accurate support. Unlock efficient cloud management—view our comprehensive one-pager now and see how Automat-it can transform your FinOps strategy with Anodot.

If you’re ready to leap to the government cloud, you’re likely looking back and forth between Amazon and Microsoft, wondering which is the best (and safest) bet. We’ve got you covered!  Learn all you need to know from our cloud experts about which government cloud offering will work best for you – and it may come as a surprise, but there are other options outside of AWS and Azure… get into the details below! What is AWS GovCloud? [caption id="attachment_16375" align="aligncenter" width="512"] Source: AWS[/caption]   First things first, AWS GovCloud (U.S.) is a cloud offering designed for the needs of the U.S. federal, state, or local government. AWS GovCloud enables users to adhere to conditions like ITAR (International Traffic in Arms Regulations), FedRAMP (Federal Risk and Authorization Management Program), and DoD (Departments of Defense) Cloud Computing Security Guide (SRG) Impact Levels 2, 4, and 5.  Designed to securely host data and regulate workloads by meeting the strict compliance and regulatory requirements of the U.S. government agencies, AWS GovCloud is not just an option, but a top choice in the market to keep user data 100% secure. Its robust security features will give you peace of mind about the safety of your data.  AWS GovCloud regions   AWS GovCloud offers specific regions geographically isolated from other AWS areas, ensuring all data is protected from anything ranging from natural disasters to downtime during updates.  There are two GovCloud regions, U.S.-West and U.S.-East. Each region operates independently to offer the highest levels of security, data locality, and compliance.  Though AWS GovCloud houses the data only within specific regions, it can be accessed globally so long as the user is a vetted U.S. entity.  AWS GovCloud compliance   AWS GovCloud supports FedRAMP JAB P-AT (Joint Authorization Board Provisional Authority to Operate) at a High baseline. This government-wide program ensures you get all the security you need while monitoring cloud performance.  Other compliance standards supported include:  IRS (Internal Revenue Service) Publication 1075  EAR (Export Administration Regulations)  DOJ (Department of Justice)  CJIS (Criminal Justice Information Systems) Security Policy FIPS (Federal Information Processing Standard) Publication 140-2 What is Azure Government?   [caption id="attachment_16365" align="aligncenter" width="512"] Source: Microsoft[/caption]   Azure Government is Microsoft's answer to AWS CloudGov. Similar to CloudGov, Azure Government is a cloud service designed for U.S. government agencies and their related partners. Azure Government offers a cloud entirely dedicated to government cloud to ensure maximum security and to reduce downtimes.  It also uses a data center strategy similar to AWS GovCloud, with Azure Government isolating its data centers and networks to select areas of the U.S. via regional pairing. You can choose from Regional Pair A (Arizona and Virginia) or Regional Pair B (Texas).  AWS GovCloud vs Azure Government – which is best?   AWS GovCloud and Azure Government have both been designed to do the same thing: provide cloud services made for government agencies. But not all things are created equal... and we're here to walk you through the biggest pros and cons of each service to make it easier for you to pick the best offering for your needs.  Before we get into all of the meaty details, keep this important thing in mind: if you're already using Azure or AWS, the cons of cloud migration are unlikely to outweigh the pros of starting at a new provider.  First, the main things these cloud services have in common:  Both offer physically isolated databases located in different regions of the U.S. for maximum natural disaster and redundancy protection.  Both meet compliance standards for a wide range of requirements (ex: FedRAMP, IRS 1075, etc.).  Both offer AI, IoT, analytics, and cloud security services.  Here’s how the two differ in terms of pros and cons:  AWS GovCloud and Azure Government also differ in terms of how they charge you for cloud services. Where AWS GovCloud has on-demand and reserved pricing models the same as traditional AWS cloud services, Azure pricing has four tiers to choose from for a monthly support plan (Basic, Developer, Standard, and Professional Direct). [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] AWS GovCloud pros   AWS GovCloud has been on the market longer than Azure Government. This comes with a laundry list of pros, including:  AWS offers more GovCloud services than Azure Government.  GovCloud has more customers, which means they've addressed more GovCloud-related issues.  AWS GovCloud cons   The main AWS GovCloud cons are:  Poor feature parity between commercial AWS cloud and AWS GovCloud (AWS ChatBot doesn't exist in GovCloud yet).  Additional costs and latency when transferring data between GovCloud and non-GovCloud accounts.  Access to both services is restricted to U.S. individuals who comply with U.S. export control laws.  Azure Government pros   The following are the pros you can expect from Azure Government:  Azure Government is a completely separate section of Microsoft Azure to ensure maximum security.  Easy integration with other Azure services.  As we've mentioned above, if you're already working on Microsoft, you're likely best off staying on Azure Government since you won't have to worry about migration. Azure Government cons   Azure Government’s biggest con is its lack of experience. This means that it:  Lacks the extensive cloud services that AWS offers, though.  Has a weaker market share and adoption rate, though it has been gaining traction.  Is slower to receive updates due to weaker market share and deprioritization.  While AWS GovCloud and Azure Government are the leading government cloud providers, it’s smart to consider other options before making a final decision. These providers may offer unique features or better suit your organization's specific needs.    What are other major government cloud providers?    Still unsure if you want to use either AWS or Azure services? No sweat. There are plenty of other options. Amazon launched their first government cloud option thirteen years ago and Microsoft eight years ago, so there's been time for other providers to catch up.  The best place to find other options is the FedRAMP website, which provides a list of compliant and authorized vendors and services. These vendors have been heavily vetted by technical and security reviews and audited by accredited third-party assessors before they were granded the right to operate.  We've listed the other top government cloud providers below.  Something you should know is that all of these providers are using the same data hosting system as their commercial offerings. The biggest difference between the government cloud offerings versus the commerical cloud offerings for these providers is the added level of security. Otherwise, these providers are all known for their trustworthiness and their tailor-made products. IBM SmartCloud for Government   [caption id="attachment_16376" align="aligncenter" width="540"] Source: IBM[/caption]   IBM's SmarCloud for Government allows for improved communication, encrypted mail services, and BlackBerry-specific collaborative document creation and customer support.  IBM SmartCloud also makes working in a multi-cloud environment easy, enabling you to integrate variations of other cloud-enabled IBM or Lotus products.  Salesforce Government Cloud   [caption id="attachment_16377" align="aligncenter" width="440"] Source: Finances Online[/caption]   Salesforce launched their government cloud solution back in 2014, so they've had plenty of time to perfect their service offerings. Favored by government enterprises like the Department of Defense and the Bureau of Engraving and Printing, you'd be hard-pressed to go wrong with Salesforce, as it's one of the most popular government solutions on the market.  Google Distributed Cloud Host (GDCH)     [caption id="attachment_16378" align="aligncenter" width="540"] Source: Data Centre Dynamics[/caption]   GDCH (Google Distributed Cloud Hosted) is Google's government user infrastructure offering. As opposed to AWS GovCloud or Azure Government, GDCH offers a private cloud solution that a government customer can host on their own premises.  GDCH is designed for a bit of a different purpose. It focuses on providing data residency, operational continuity, and soverentiy. Users will have access to standard Google Cloud services and scalability through the Google Anthos hybrid cloud solution.  Should you switch to a government cloud service?   Now that we’ve covered all of the different government cloud service providers and you have a better idea of which company might be best for you, it’s time for the real question: is it worth you even starting with a government cloud service in the first place?  As much as we hate to say it… it depends.  If you're a government agency, you certainly don't need to use AWS CloudGov or Azure Government or another government cloud serivce. You can stick to the standard cloud or multi-cloud experience (it's usually a little cheaper!) and not have to worry about migration. Since most government cloud services require separate account IDs and user access credentials and, in the name of security, can make it very difficult for you to add new users to the cloud platform, things are often slow-moving and inconvenient.  With that said, if you're looking for support for enterprise-sized applications (ex: Oracle, SAP) or workloads, or need help with storage, disaster recovery, or hgh performing computing, or even if you just want an additional boost for your cloud security, government cloud services might appeal. Government cloud services mean you don’t ever have to worry about data leaks, and you can manage all of your user and customer information with that peace of mind.  If you are ready to bite the bullet of transition to a government cloud service, you'll just need to be prepared for the migration, which can seriously inflate your bills.  We do have good news on that front though, because there’s an easy way you can address inflated cloud service prices. Considering AWS GovCloud? Stay 100% Secure and Gain Multicloud Visibility with Anodot   AWS GovCloud offers a dependable solution for securely storing classified US government and federal information. However, when operating in multi-cloud environments, achieving complete visibility of all your activities in a single location can significantly reduce the back-and-forth involved in calculating costs and gaining insights into your expenditures. Using Anodot, you can solve the ever-worsening mystery of why is my cloud budget so high? Our dashboard is able to integrate your cloud data  like AWS, GCP, and Azure,onto one dashboard. You can get up to a 24-month lookback with data down to the hour, making it easy to spot everything from season trends to all of your cloud spend inefficiencies.  With Anodot you’ll be able to visualize where costs are generating wether they be in the public or government controlled cloud. Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools. 

If you're a U.S. federal, state, or local government agency trying to deliver services to the public faster without sacrificing a single inch of security, GovCloud is the PaaS (Platform as a Service) solution.  But what exactly is GovCloud, and how can it ensure you deliver services more efficiently and effectively?  We'll tell you all you need to know so you can decide if you're ready to upgrade your tech stack with this tool.  What is GovCloud?   Let's define our term first.  GovCloud is a U.S.-specific AWS service designed with the extra levels of security necessary for those working in government agencies.  Not only does it check almost all of the compliance boxes for systems like CJIS (for criminal justice data), it also has a built-in compliance support system that enables you to create your documentation as needed to ensure you're always meeting industry rules.  You can receive GovCloud-like services from providers, including Azure Goverment (designed only for U.S. regions) or AWS GovCloud. Common services for government agencies include networking and database services, including heightened levels of security, encryption, and backup offerings.  Why GovCloud?   [caption id="attachment_16375" align="aligncenter" width="512"] Source: AWS[/caption] There’s a wide range of reasons GovCloud might be the best offering for you. Here are our top three reasons:  Reason #1: No need to worry about compliance needs If you're a government organization, GovCloud is a system designed to meet your compliance needs. It will pass every HIPPA, FedRAMP, FIPS 140-2, or ITAR regulatory review.  Reason #2: Complete data protection Even the American DoD (Department of Defense) can rest easy knowing that GovCloud providers are specifically designed to prevent sensitive data leaks. With this assurance, you can process and store sensitive data without worry. Reason #3: Handles massive workloads GovCloud is engineered for government work, which means you'll be working on a platform built to handle huge numbers of users and spikes in usage. You'll still be able to access popular AWS services, and since GovCloud is an open-source platform, and the portability to other cloud providers or your current on-prem tech stack.  How to Use GovCloud   You'll want to confirm your company meets a certain requirements list before settling on GovCloud. Use this checklist to make sure its the best fit for you:  Consider if your company has a legal, contractual, or customer-mandated reason to use GovCloud.  Assess how your other cloud integrations or services might blend with GovCloud.  Review your security requirements.  Evaluate operational needs.  Consider business objectives.  Ensure compliance requirements will be met.  Confirm GovCloud meets all your needs before signing the deal.  If you meet the requirements and decide that GovCloud is the best service for you, here’s what you need to do to ensure a seamless migration:  Establish GovCloud endpoints. Use a management console or API calls to establish GovCloud endpoints programmatically. Configure IAM roles. IAM (Identity and Access Management) roles decide who can access what in your GovCloud setup. Migrate your data. Now that you've established your GovCloud account, you can migrate over your workloads. You may need to make some adjustments depending on compliance requirements. Test and verify the final results. Always, always, always test and verify that your set-up has been properly established.  Managing GovCloud   Once you’ve gotten started with GovCloud, you’ll want to do the following:  Regularly monitor your workloads for compliance. Consider using an automated compliance tool to assist with this task. Consider third-party tools for additional support. The right third-party cloud management tools can optimize your spending while providing valuable insights into GovCloud. This combination allows you to enjoy top-notch security alongside AI-driven cost-saving opportunities. “While GovCloud offers unmatched security and compliance for government agencies, optimizing cloud spend remains a critical concern.  Third-party tools like Anodot can help agencies identify cost-saving opportunities, automate anomaly detection, and gain granular insights into their GovCloud usage. This empowers them to make data-driven decisions and maximize their cloud investment.” ~ Limor Tepper, VP of Product,  Anodot. Are there GovCloud drawbacks?   Like any program, GovCloud has some drawbacks.  Before you commit to GovCloud, you should know that it comes with some constraints, the largest of which are slower updates. Compared to other cloud offerings, GovCloud can be a bit slower in pushing updates live. Lack of speed is the price you must be prepared to pay if you're looking for that full compliance boost.  For example, AWS GovCloud rolled out CodeConnections in the GovCloud (U.S.-Eat) Region in September 2024, whereas the same tool was released for general AWS cloud services in March 2024. Amazon EKS Pod Identities was only released for AWS GovCloud in August 2024, while the service was available on AWS Cloud since November 2023. So, you should prepare yourself for some longer waiting times with GovCloud.  Is GovCloud the best cloud provider for US government agencies?   Now that we've covered everything you need to know about whether you should go with GovCloud let's discuss whether it's the right provider.  The Pros and Cons of AWS GovCloud   AWS GovCloud is known for its secure and compliant cloud environment offering. Its services have been specifically designed for any government service, from state to federal to local. You'll have access to all the standard features of the commercial version of AWS but with the added level of security and compliance expected from the GovCloud offering.  AWS GovCloud's key features include:  Government-specific compliance for FISMA, ITAR, FedRAMP, HIPPA, and more. Ability to manage many levels of data security. Variety of access control options. AWS GovCloud’s cons include:  Lack of feature parity between commercial AWS cloud and AWS GovCloud (ex: AWS ChatBot doesn't exist in GovCloud). Access is restricted to U.S. individuals who comply with U.S. export control laws. Additional costs and latency incurred with data transfer between AWS GovCloud and other non-GovCloud accounts.  [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Should you use GovCloud?   Federal, government, or state entities needing compliance, heightened security, and comprehensive support from government cloud services should strongly contemplate migrating to GovCloud. Despite the additional qualification steps and longer release timelines, the assurance of 100% secure user data usually outweighs the extra effort. If you’re not in a place to support migration, it’s probably better to wait. GovCloud isn’t going anywhere, and you’ll want to ensure your migration is properly executed.  No matter what platform you choose, cloud cost management is the biggest obstacle you’ll face. AWS GovCloud provides some visibility into cloud spending but lacks comprehensive insights for effectively balancing and optimizing costs. [CTA id="47462b23-d885-42f9-9a91-7644f2c84e50"][/CTA] Optimize your GovCloud spend   Let’s cut to the chase: Anodot is one of the few Finops tools that supports AWS GovCloud.  Here’s how: Multi cloud data in one place, offering a 24-month look-back period to identify changes down to the hour. This provides complete visibility into your GovCloud spending, making it easy to spot potential budget misuse. Anodot uses machine learning (ML)  to detect cost anomalies automatically in real time. This allows government agencies to identify unexpected increases in cloud spending, helping to prevent budget overruns and ensuring efficient resource allocation. Granular Cost Allocation allows users to break down cloud expenses by department, project, or service. This helps GovCloud users ensure that each segment of their organization is accountable for its cloud usage, making cost management more transparent. Predictive analytics allows agencies to estimate future cloud costs using historical data, which helps with budget planning, particularly for government organizations that must adhere to strict budget limits. Government agencies can set customized alerts for specific spending thresholds or changes in usage patterns and take immediate action when costs deviate from expected levels. Why Anodot? We’ve been demystifying cloud costs for FinOps organizations for years. We ensure that overspending is never a problem with our automated anomaly detection and customized alerts paired with AI-powered feedback. You won’t need to lift a finger. You can just start cutting costs.  Other Anodot tool features include:  Next Level Forecasting: High-powered analyses to make planning spending easy.  AI-Powered Support: AI-powered recommendations that improves resource utilization.  Multicloud Visibility: Next-gen multicloud visibility so you can see your cloud spend and activity all in one place.  Automated Anomaly Detection: Customizable alerts that improve real-time budgeting and help you react immediately to unusual data trends.  Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools. 

Every government – especially the U.S. government – needs secure cloud space. And there is no better way to get guaranteed security and compliance-ready services than through Microsoft Azure Government's world-class cloud offerings.  But what is Azure Government? How does it work? And, most importantly, will your organization qualify to reap the benefits?  Let’s take a closer look at Azure Government, the differences between this offering and regular Azure, the benefits you can expect, what regions can use this service, and how you can optimize your cloud performance.  What is Azure Government Cloud?   [caption id="attachment_16365" align="aligncenter" width="512"] Source: Microsoft[/caption]   Azure Government Cloud is a version of Microsoft Azure, a GovCloud-like offering made specifically for the U.S. government, federal, state, and local agencies and their contractors. You can store anything from cloud data for the Department of Justice to a state department.  Since Azure Government offers an IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service) cloud model system, there can be some confusion about how each type differs from the others.  For instance, Azure Government customers can expect higher security and data protection than standard Azure offerings. Azure Government also has contractual commitments regarding data storage, and access to Azure Government is restricted to screened U.S. citizens. Let’s look at the differences between Azure Government and Azure Commercial below.  Azure Government vs commercial    The most significant difference between Azure Government and a regular Azure is that it’s a sovereign cloud. This means it’s physically separated from Azure and meant for the U.S. government only.  As we mentioned above, there are many other smaller differences as well. Azure Government offers the much-needed benefit of added security. It also requires all customers to undergo an eligibility process to determine whether they meet the requirements to utilize Azure Government.  The development environment is primarily the same, but the security, compliance, and operational procedures for deployment might differ due to the unique regulatory needs of government data. Since Azure Government can be very location-specific, you’ll want to carefully review the rules for your region before starting work. The most important thing to remember is that all areas are within the US. But don’t worry! We’ll go into more detail on the different regions in a little bit. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] What are the benefits of Azure for government?   The obvious pro of Azure Government's massively superior security offerings aside, here are some other benefits to enjoy:  Disaster recovery: Azure Government offers Site Recovery in case of disaster so you can fully recover your tech stack, all without the cost of a secondary infrastructure. Secure backup and archives: Azure Government offers Azure Backup, Azure Storage, and to ensure your peace of mind in keeping your data secure. Data and analytics: Analyze real-time data changes to improve customer experience. Development and test: Azure DevTest Labs is a service that helps agencies quickly create development and testing environments, allowing for experimentation without impacting production environments. Networking: You'll have access to networking services that help you load balance traffic, autoscale, monitor back-end poorl, and more with tools like Azure Load Balancer, Azure Virtual Network, Azure ExpressRoute, Azure VPN Gateway, and Azure Application gateway.  Since Azure Government is separate from Azure, it can operate with its own network. There are eight regions in the US, two of which include U.S. Department of Defense (DoD) Impact Level 5. You can expect data replication for all regions and connection via private dark fiber.   [caption id="attachment_16366" align="aligncenter" width="540"] Source: Microsoft[/caption]   You'll also have access to networking services like VNet service endpoints for SQL, Network Watcher, and Azure storage.  Azure Government makes it easier for government agencies to bring their tech stack to the modern era. You’ll finally get cloud software you can trust to protect citizen and personnel data.  What are Azure Government regions?    Azure uses paired regions to provide geo-redundant storage.  Paired regions are typically used for disaster recovery and geo-redundancy to ensure you always have coverage. The regions are usually physically far apart to reduce the risk of both areas being impacted by a single disaster. Disasters aside, paired regions mean that you don't have to worry about downtime during an update since usually only one part of a region is updated at a time.  Here are the primary and secondary region pairings used by Azure Government:    Geography Regional Pair A Regional Pair B US Government US Gov Arizona US Gov Texas US Government US Gov Virginia US Gov Texas   In other words, if you choose Regional Pair A, your data will be stored in Arizona and Virginia in the U.S. This will ensure that if there is an outage in Virginia, you can continue to operate in Arizona. Or, if your systems require updates in Arizona, you don’t need to worry about downtime and can operate as usual in Virginia.  Should you migrate to Azure Government?   Is it worth migrating to Azure Government?  Suppose you're a government, federal, or state organization seeking enhanced cloud security and compliance support, and you're already an enthusiastic user of Azure. In that case, it may be a good fit for you. If you’re new to Microsoft Cloud, your decision will largely depend on your readiness to handle migration and whether you have a capable team to navigate the learning curve. Already a User of Azure? Anodot Can Help Manage Cloud Cost and Eliminate Cloud Waste.   What is the most significant limitation behind Azure? A limited view into how Azure pricing works. Sure, you'll have data and analytics on how your dollars are put to work, but there's always some mystery behind how exactly the money goes into the cloud and services come out.  There's an easy solution to ensure your budgets are fully optimized. Third-party tools like Anodot can provide more granular insights and proactive recommendations to really impact your Government cloud spending. How do we do this? Putting all of your cloud data in one place so you can look back up to 24 months allows FinOps teams to spot trends, seasonal usage patterns, and potential inefficiencies. This allows you to pinpoint how even the smallest changes affect your cloud budget, meaning no gaps are overlooked.  The best part?  Anodot is made for multi-cloud data management. So, if you’re juggling multiple cloud platforms and cycling between a dozen different dashboards, you can rest easy. We put all that data in one place with customizable, easy-to-understand consoles.  We’ve made it our mission to make cloud cost management tools our specialty. Anodot has been demystifying cloud costs for FinOps organizations for years. Our mission is to make your overspending our enemy. With our automated anomaly detection, AI-powered feedback, and next-level forecasting data, you can start cutting costs without ever lifting a finger.  Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.

Undoubtedly, AI is our future—which means it's past time to integrate machine learning models into your FinOps multi-cloud tech stack. AI turns simple tasks into something that can be executed at the click of a button. With well-trained models, FinOps, MSPs, and Enterprises can automate cost detection, forecasting, and anomaly identification, streamlining complex financial operations without increasing their workforce. The good news?  If you're an Azure user, you can use their Azure Machine Learning feature to stay ahead.  The bad news? The Azure ML pricing structure, like all Azure pricing, can be a bit... complicated.  But don't worry! We're something of Azure experts. Read on to avoid any and all monthly budget surprises while maintaining optimal customer experiences.  What is Azure Machine Learning?   Azure Machine Learning (ML) is an open, interoperable platform that streamlines the process of building, training, and deploying machine learning models, helping you optimize your multi cloud resources and manage costs efficiently in alignment with FinOps best practices. For teams seeking flexibility in discovering new project assets and resources while easily sharing existing files, Azure ML serves as a pivotal tool for collaboration.   [caption id="attachment_16342" align="aligncenter" width="512"] Source: Microsoft[/caption]   Azure Machine Learning (ML) is recognized for its strong security features. It works well with other Azure services to keep all ML workflows secure.  Such as: Azure Key Vault securely manages and stores critical information such as API keys and credentials.  Azure Container Registry ensures the safe management of container images, maintaining isolation and safety for machine learning environments.  Azure Virtual Networks enable you to segregate machine learning projects within your network, fostering a secure and collaborative space for your ML tasks.   Who uses Azure ML for FinOps purposes?   Azure Machine Learning is the perfect tool for FinOps groups or individuals who want to start integrating machine learning processes into their multi cloud tech stack. This tool allows you and your team to focus on what you do best while Azure ML handles menial, automatable tasks.   [caption id="attachment_16343" align="aligncenter" width="540"] Source: Microsoft[/caption]   Wait, it gets better. ML integrates well with any other tools you use in the Microsoft Azure cloud ecosystem, so optimizing security networks or role-based controls is easy.  In other words, if you want to enhance your FinOps engineer's daily work, Azure ML will appeal quite nicely!  How to set up your new Azure Machine Learning workspace   [caption id="attachment_16344" align="aligncenter" width="540"] Source: Microsoft[/caption]   Setting up your Azure ML isn't as hard as you think. Follow these steps, and you should be good to go:  Sign into your Azure Portal account. Or Create an account if you don't already have one. Search for "Machine Learning". Select it from the other services. Hit "Create" to start a new Machine Learning workspace. Select the basic settings for Subscription, Resource Group (either pick an old or pre-established one), Workspace Name, and Region (pick either your region or one close to you). Pick your resource details for Storage Account (make a new one or use a pre-existing one), Key Vaulted, Container Registry. You can also opt into Application Insights for monitoring resources. Review your choices to make sure everything is accurate. Deploy your new ML workspace!    How are Azure Machine Learning costs calculated?   Before we get into these numbers, keep in mind that these prices have been calculated assuming the user is based in North America, so it’s possible your costs might be higher or lower than the numbers below. Look to see how prices might vary for other parts of the world here.  Now that that’s been covered, let's break down how your Azure ML bill works.  There are four main factors that contribute to costs:  VNets and load balancers. The more cluster support you need, the higher your bill. Compute time. Anything from profiling a data set to deployed models or real-time endpoints on Azure Kubernetes services can contribute. Storage. Anything from storage for trained models, metrics, or logs will add to your total. Azure container registry. Yes, you'll need to pay for your registered containers.  The key to keeping your Azure ML pricing low is optimizing everything to the fullest and making sure you have the best possible tools to track any changes.  Pro tips to manage Azure Machine Learning costs   Keep your Azure ML costs low while maintaining quality customer experiences by paying close attention to the following factors:  Optimizing Compute costs As you set up your compute cluster, you must select the best compute resource for your experiments.  It may surprise you to learn that the bulk of your bill won't come from compute costs and training r models. The actual training process makes up only a small amount of the costs – though this can vary from user to user. If you're expecting heavy training runs, prepare to invest more. Here are our four tips to handle compute usage if you intend on using Azure ML for training large models:  Don't pick a super low compute tier. If you pick something too low, it will likely save you more money in the short run, but because you're stuck with slower processing time, it'll cost more in the long run in resources and time. Specify 0 as the minimum number of nodes for your compute cluster. This means your compute resources can shut off when you no longer have any active work scheduled, letting you dodge additional resource charges. Use low-priority compute resources during training tasks. If you don't mind training tasks taking a bit longer or having to be restarted if there's limited capacity, your experiments are a great place to save money. Enable an idle shutdown timer. Set a stop compute instance schedule for off-hours. This means you don't have to worry about hidden-away compute instances in notebooks leading to surprise charges.  The key here is to maintain a quality offering while eliminating waste. We’ll explain how to do that below.  Monitoring Storage prices Azure Storage is the most common budget-killer when it comes to ML pricing. Make sure to delete any trained models you no longer  use . It's best to regularly audit any stored data on Azure so you're not paying for something that isn't useful.  The following are the biggest contributors to increased storage: Log Model metrics  Data profiles  Training data  Trained models For instance, when automated ML trains your models to identify the most effective hyperparameters, you'll achieve a highly efficient model. However, this process may also leave you with numerous underperforming models stored away, increasing your storage costs over time. Managing Endpoints Endpoints are another pain point for Azure ML pricing. Deploying real-time models to live endpoints is a powerful feature... which means it's also very expensive. You'll have to pay for Azure Kubernetes Services resources or Azure Container Instances and associated container registries, storage, and load balances. You’re also on the hook for all sorts of real-time costs 24/7 – always on cost, autoscaling costs, and idle costs, so plan carefully. Here's how you can optimize:  Azure Container Instances (ACI) are usually less expensive than Azure Kubernetes Services (AKS) since AKS clusters are made for product-level tasks, while ACIs are more for developing and testing. So, if you're using Azure ML for testing and development, it's a good idea to use ACI to save on costs! Use batch endpoints instead of real-time endpoints to lower compute costs when you're able.  Remove endpoints you aren't regularly using to help save costs while maintaining UI.  Azure Container Registry Azure Container Registry (also known as ACR) is where you store, build, and manage your container images. It enables you to replicate images across multiple locations and provides added security by offering image signing through Docker Content Trust.  You must create various resources in your Azure Machine Learning Workspace, but the container registry is optional. Since it comes with an associated fixed cost, opt out of it for now or use a pre-existing container registry even if you're not actively using it.  If you ever deploy a container, you won't need to worry about anything going wrong because Azure ML Workspace will automatically create a container registry. So don't make one unless you need it! How to track Azure ML pricing   Microsoft does provide some tools to help you monitor changes in Azure pricing. You can use Azure Cost Management to monitor cost alerts and changes to spend. There's also their Pricing Calculator that you can use to project how much service add-ons might cost.    However, these tools have limitations. Though you can pull your Azure AI and ML services into the same dashboards to project costs with Azure's tools, you often won't get a full view of your multi cloud experience or an in-depth analysis of how to address pricing issues. You won’t get the best view into how your resources might be going to waste, or how to optimize your customer’s user experience best while maintaining profitable margins.     [caption id="attachment_16345" align="aligncenter" width="540"] Source: Microsoft[/caption]   Top solution to track Azure ML spend What is the best solution for keeping track of your Azure ML spend?  A third-party tool that works alongside you to help reduce Azure ML pricing without any ulterior motives to increase your Azure costs. And we’re the cloud cost optimization tool. Anodot can help you save up to 40% on annual spending. Anodot lets you get all multi cloud data in one place. Picture this: a UI-friendly dashboard that shows where all of your spend is going and fluctuations captured down to the hour with retention periods up to 18 to 24 months. Finally, you can finally have that 100% visibility into your cloud performance that you’ve always dreamed of.  Why Anodot? We’ve been working to demystify cloud costs for FinOps organizations for 10 years.  Other Anodot features include:   Real-time anomaly detection: Automated alerts that improve response time to cloud spend spikes and allow you to track VM, GPU, cluster, and other training and deployment resource-associated costs.  Customizable alerts: Anodot allows you to set up custom daily, weekly, or monthly alerts based on spending thresholds, which means you will be notified when your Azure ML costs get out of hand.  AI-powered feedback: Budgeting has never been easier with our CostGPT, which informs your decisions with rapid, AI-powered recommendations. Reveal immediate insights into hidden expenses, pricing inefficiencies, unused resources, and more.  Comprehensive multi cloud visibility: Full support and visibility across all cloud platforms so you can see your cloud spend and activity all in one place. Cost-saving Recommendations: Anodot's recommendations cover a variety of Azure services, including Disk, VM, MySQL, SQL Data Warehouse, PostgreSQL, Cosmos DB, Maria DB, Load Balancer, Snapshots, Data Explorer, Redis, Kusto, RI Commitments, and App-Service. Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools. 

Autoscaling in cloud computing is the ability of a system to adjust its resources in response to changes in demand automatically. This guarantees that applications always have the resources they need to perform optimally, even during periods of high traffic. Autoscaling eliminates manual intervention, allowing your dev team time to focus on your product. All major cloud providers like AWS, Azure, and Google Cloud Platform offer robust autoscaling solutions with many features and capabilities. This is part of a series of articles about Cloud Management. What is Autoscaling?   Autoscaling in cloud computing is a feature that automatically adjusts the number of computing resources allocated to an application or service based on its current demand. This dynamic allocation ensures applications maintain optimal performance during traffic spikes while reducing costs during low-traffic periods. Adding or removing resources as per requirements improves overall system reliability and user experience and allows businesses to manage their cloud infrastructure efficiently, paying only for the resources they actually use. What Are the Types of Autoscaling?   There are two types of autoscaling: Horizontal Scaling: This type of autoscaling, also called Scaling Out, involves adding or removing instances as needed. It is ideal for applications designed for distributed environments. Vertical Scaling: This type of autoscaling is also called Scaling Up. It involves increasing resources such as CPU, memory, etc. of existing server instances. It is suitable for applications running on single large servers rather than multiple distributed servers. How Does Autoscaling Operate on the Cloud?   Autoscaling works as follows: Monitoring: Autoscaling systems continuously monitor various metrics of your application or server, such as CPU utilization, memory usage, network traffic, response times etc. Scaling Policies: Scaling policies are the conditions under which the autoscaling should occur. This is dependent on monitoring metrics. When a specific criteria is met, instances are scaled up or down. Scaling Action: When the monitoring metrics reach a certain threshold set up in the Scaling Policies, the system automatically adds or removes instances to curate to the demand. Load Balancing: Autoscaling systems work in sync with load balancers, distributing traffic to the resources the autoscaling system provides. Load Balancing: Autoscaling systems work in sync with load balancers, distributing traffic to the resources the autoscaling system provides.   Source: TechTarget  What Are the Benefits of Autoscaling?   The benefits of autoscaling in the cloud are: High Availability and Reliability: Autoscaling helps maintain your services’ availability by automatically adding resources in case of failures. Cost Effectiveness: It helps in setting up a cost-effective cloud infrastructure. Resources are only allocated when they are required. Management Simplification: A cloud infrastructure built on autoscaling principles requires minimal human intervention and is much easier to manage. Performance Improvement: Autoscaling ensures that your services and applications can handle a sudden surge in traffic without causing performance degradation. Resource Optimization: Autoscaling helps you match your resource allocation to demand. It scales up resources when demand increases and scales down when demand decreases. This eradicates the issue of over- or under-provisioning, resulting in an efficient and cost-effective infrastructure. Autoscaling vs Load Balancing    Autoscaling is a technique that adjusts the resources allocated to an application based on its current demand. In contrast, load balancing mainly focuses on distributing incoming network traffic across multiple servers. The key differences between Autoscaling and Load balancing are: Purpose: Autoscaling scales resources up or down to match demand, while load balancing distributes traffic among existing resources. Action: Autoscaling adds or removes resources while load balancing routes requests to different resources. Metrics: Autoscaling monitors CPU utilization, memory usage, and request count, while load balancing monitors response time, connection count, and traffic throughput. Scope: Autoscaling is often applied to an entire application or group of resources, while load balancing is typically applied to incoming network traffic. We can understand the differences using the following scenario: The Scenario A Fintech firm handles a substantial volume of daily transactions and experiences predictable spikes in activity during stock market opening and closing hours. It seeks to optimize its cloud infrastructure to accommodate these fluctuations cost-effectively while maintaining seamless performance even during peak periods. Solutions Implemented by the MSP Autoscaling: The Fintech’s MSP for cloud management utilizes autoscaling to dynamically adjust the number of cloud servers in real-time, responding to current transaction loads. This approach guarantees that the infrastructure is consistently optimized and appropriately sized. Load Balancing: The MSP configures load balancing to distribute incoming transactions intelligently across all available servers. This prevents any single server from becoming overwhelmed, ensuring optimal resource utilization and consistent transaction processing speeds even under heavy loads. Billing [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] How Autoscaling and Load Balancing Work Together    As autoscaling adds or removes servers, the load balancer automatically updates its configuration to include or exclude them from traffic distribution. When autoscaling and load balancing are used in tandem, they provide: Seamless Scalability: The infrastructure can smoothly handle sudden increases in transaction volume by adding servers, i.e., autoscaling, and then efficiently distributing the traffic across them using load balancing. Cost Optimization: During periods of low activity, unnecessary servers are removed using autoscaling, which minimizes cloud spending. High Availability: Load balancing ensures traffic is automatically redirected to other healthy servers if one server fails, preventing disruptions. Performance Optimization: Load balancing prevents any single server from becoming a bottleneck and gives consistent performance even under high loads. Autoscaling and Cloud Providers   Autoscaling is a fundamental feature most major cloud providers offer, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). While the core concept remains the same – automatically adjusting resources to match demand – each provider implements autoscaling with its unique tools and terminology. Autoscaling in different cloud providers: AWS: Amazon Web Services offers Auto Scaling Groups (ASGs) that allow you to define scaling policies for different resource types like EC2 instances, ECS tasks, and DynamoDB tables. AWS also provides various scaling options like target tracking, step scaling, scheduled scaling etc Azure: Microsoft Azure offers Virtual Machine Scale Sets (VMSS) for autoscaling virtual machines and Azure Container Instances (ACI) for autoscaling containers. Azure also provides autoscaling for various Azure services, such as App Service, Azure Functions, Cosmos DB, etc. GCP: Google Cloud Platform offers Managed Instance Groups (MIGs) for autoscaling Compute Engine instances and Kubernetes Engine clusters. GCP also provides autoscaling for Cloud Functions, Cloud Run, etc. Related content: Read our guide to VMware CloudHealth Challenges of Autoscaling and How Anodot Provides Assistance   While offering significant benefits, autoscaling presents complex challenges that can strain resources and expertise. Partnering with seasoned industry professionals can be a strategic move to navigate these complexities effectively. Companies like Anodot, with their deep FinOps expertise in cloud scalability solutions, can provide invaluable support in optimizing your cloud infrastructure for growth and efficiency. Complexity: As a company's cloud infrastructure grows, it becomes very complex to manage. Managing scale and configurations across many services becomes a complex and tedious task. How Anodot helps: Anodot has cloud management tools that simplify  complex cloud infrastructures by providing unified visibility, AI-powered insights, automated optimization recommendations, and customizable dashboards and reports. This helps you to gain control over your cloud environment, reduce costs, and improve efficiency.   Security: As scaling increases the attack surface and introduces new security challenges, it becomes essential that scaling maintains robust security measures (like encryption and intrusion detection), especially in newly allocated instances. How Anodot helps: Anodot provides AI-driven alerting out of the box and allows users to set up custom alerts and dashboards that track key security metrics, such as network traffic, access logs, and resource utilization. These alerts can be triggered when anomalies or unusual patterns are detected, helping teams proactively respond to potential security risks.   Cost Management: Scaling can lead to unexpected and unpredictable cost increases if not managed properly. Therefore, it is important to implement cost optimization strategies and choose the appropriate scaling configurations. How Anodot helps: Anodot’s insights can help identify unutilized resources, which can prevent overspending on unnecessary resources.   Predicting Scaling Requirements: In a complex cloud environment, proactively predicting scaling needs is often very challenging. How Anodot helps: Anodot uses artificial intelligence to analyze vast amounts of metrics in real-time. It can interpret unusual usage patterns and anomalies, addressing potential scaling needs before leading to performance degradation and service disruption.   Root Cause Analytics: Operating applications at scale can introduce vulnerabilities, such as downtime, especially during sudden traffic surges or configuration errors. How Anodot helps: Anodot’s correlation engine helps quickly pinpoint the root cause by analyzing relationships between different metrics and events. This accelerates troubleshooting and minimizes downtime.   Performance: Scaling is not straightforward. Increasing the number of service servers is often not enough. Related performance bottlenecks such as network limitations and database constraints, must be identified. How Anodot helps: Anodot uses artificial intelligence to analyze vast amounts of metrics in real time. It can interpret unusual usage patterns and anomalies, addressing potential scaling needs before they lead to performance degradation and service disruption.   Data Management and Compliance: Large volumes of data need to be managed while performing scaling operations. Ensuring data consistency becomes crucial and tricky, especially in distributed systems and if you're a government organization and must be held to even stricter standards. How Anodot helps: Anodot's leading industry experts are well-equipped to handle the challenges of data management and compliance at scale. Seeking the help of such professionals is often a wise choice as your cloud infrastructure grows.   Human Skill Limitations: Scaling large infrastructures requires specialized skills and expertise. Organizations must hire highly skilled and experienced professionals and invest in training their current personnel. How Anodot helps: Dealing with all these challenges is often unfeasible or resource-intensive. It’s often a good idea to seek the help of industry professionals with years of expertise in this field. Final Thoughts   Autoscaling has emerged as a crucial component of modern cloud computing, empowering businesses to dynamically adapt their infrastructure to fluctuating demands. By automating the resource allocation process, autoscaling ensures optimal performance, cost efficiency, and high availability. These characteristics are necessary for modern-day cloud infrastructures to keep in sync with ever-increasing user expectations. Downtime is no longer acceptable. Any service disruption often results in significant financial losses and a decline in user trust. Embracing autoscaling as a core strategy is not just an option but a necessity for the organization. FAQs   What Is Autoscaling? Autoscaling is a cloud computing feature that dynamically adjusts the computational resources allocated to an application or service based on demand. What Are the Types of Autoscaling? There are two types of autoscaling: Horizontal and Vertical. Horizontal scaling includes adding or removing servers, whereas vertical scaling refers to increasing existing servers' resources, such as CPU and memory. Which Cloud Providers Provide Autoscaling Functionality? All major cloud providers, such as AWS, Azure, and Google Cloud Platform, offer robust autoscaling capabilities. Some common autoscaling functionalities are  AWS Autoscaling, Google Cloud Autoscaler, and Azure VM Scale Sets.