We’ve all been hearing about Amazon Bedrock – and the exclusive few who could access the full scope of AWS’ new product. But what exactly is AWS Bedrock? What can it help you accomplish? And, most importantly, when can you get full access to it?  Learn all you need to know about AWS’ new tool from our cloud experts.  In this article: What is AWS Bedrock? What can AWS Bedrock do? How does Amazon Bedrock work? How much does AWS Bedrock cost? Who can use Amazon Bedrock? How to manage your AWS Bedrock spend What is AWS Bedrock?   Amazon Bedrock will be your next go-to tool for building generative AI applications in AWS. Developers can use the tools to make applications like ChatGPT and other gen AI programs. In other words, it enables you to create a tool that can generate anything from copy to images to even music.  We’ll explain how AWS Bedrock does this below, but for now, know that this tool gives developers full access to customizable foundational models.  The best part? You don't need to worry about building independent infrastructures or training the AI. You can use your pre-existing AWS cloud environment for that.  [caption id="attachment_16656" align="aligncenter" width="512"] Source: AWS[/caption] AWS Bedrock vs AWS Sagemaker Let’s clarify one thing: though AWS Bedrock and AWS Sagemaker might both handle AI, they are two very different tools.  Amazon Bedrock is ideal for creating and generating AI applications that help with everything from content creation to security and privacy. Amazon SageMaker is designed to develop, train, and deploy machine learning models, such as debuggers, profilers, and notebooks.  These two AWS products can be used in tandem to develop machine learning (ML) and generative AI applications. However, Amazon Bedrock was made with developers in mind, with pay-as-you-go pricing and pre-trained models to help you get started. SageMaker was made for data scientists and ML engineers, allowing full creative access to customize models as needed.  Amazon Bedrock vs Microsoft vs Google Amazon Bedrock might be the newest developer tool on the market, but how does it compare to the other generative AI tool powerhouses? AWS isn't the only company developing generative AI tools like this. Microsoft has Azure OpenAI, and though Google doesn't have an exact 1:1 competitor, it is working on a similar option called Google Vertex AI.   But what are the differences between these tools?  Azure OpenAI is more accessible than Bedrock. It supports more regions and languages but is limited in creation compared to Bedrock. Google Vertex AI lets you build custom models, providing the most freedom with features like deploying from an on-premise or hybrid environment, not just from the cloud. However, the learning curve might be higher than that of another alternative because of that added freedom and lack of managed service. Amazon Bedrock offers managed services, so you’ll have much more support to build and grow. Plus, you'll get access to Amazon's still-growing AI model, Titan. The tool also offers features that can help you save money on computing and reduce overall workloads, making AWS more cost-effective in the long run. What can AWS Bedrock do?   Amazon Bedrock allows developers to create a wide range of generative AI tools. Here are a few common examples:  Chatbots. Like ChatGPT and Bard, developers can create a conversational bot or your next virtual assistant. Content simplification. Like Google's Search Generative Experience, you can also create an AI that condenses data sets and essays into easy-to-understand paragraphs. Text generation. As mentioned above, Amazon Bedrock can be your go-to for copy and content generation, helping you get started with anything from blogs to emails. Image generation. You can also use AWS Bedrock to create images for said blogs or even for marketing campaigns or branding. Personalization recommendations. Improve your user recommendations by feeding customer personas into Amazon Bedrock to ensure your offer aligns with your customers' needs.  How does Amazon Bedrock work?    AWS Bedrock enables your developers to create powerful generative AI tools by allowing your team access to already-established large language models (LLMs) from companies like Anthropic, Stability AI, and more. Developers can add their flare by attaching custom code to an established foundation model. Deployment will always take place in AWS’ cloud environment.  But what exactly is a foundation model? A foundation model is a tool that can understand natural language requests for generating new text or images. Still, a developer shouldn’t replace it, as it cannot complete complex tasks without human guidance. Amazon Bedrock provides foundational models developers can use to build applications such as chatbots or content generators. To leverage Bedrock effectively, developers can connect these foundation models with specific data sets or fine-tune them for tasks relevant to your organization.  How much does AWS Bedrock cost?   Amazon Bedrock charges you for model interference and customization, which makes projecting your AWS budget a bit tricky.  You can either pay for these services with: On-demand plan, which is a pay-as-you-go plan. This means you can avoid long-term commitments, though you’ll be eating steeper costs. Provisioned Throughput plan that ensures you'll have the same monthly bill but will want to meet specific performance thresholds to ensure you aren't wasting money. AWS Bedrock pricing varies depending on:  The foundation you use to build your generative AI. The assets you choose to generate. Images will cost more than copy, for example. Model customization can also inflate your price depending on how many tokens you use and how much model storage you need.  Who can use Amazon Bedrock?   AWS Bedrock was released for the general public in September 2023, so anyone can use it now! Companies like have already started migrating from OpenAI and have seen a 30% classification improvement.  In other words, you don’t even necessarily need to make room in your budget for AWS Bedrock. If you’re investing in a similar tool and want to see how much you can save, you should take the plunge.  How to manage your AWS Bedrock spend   Amazon Bedrock sounds like an obvious addition to your AWS tool suite, but are you ready for its cost? Sure, you might be able to save big by transitioning from Azure OpenAI to AWS Bedrock, but such migrations are always a risk – and always cost a little more than you might expect.  The good news is that you don’t have to commit to AWS Bedrock’s full cost… you might only have to commit to 70% of that cloud cost. And those hidden migration prices? They don’t need to creep up on you—not if you have a cloud cost management tool.  Tools like Anodot have been designed to help you save big on your annual cloud spend—all by giving you 100% visibility into your cloud spend. And by cloud spend, we mean all of your cloud spend, as in full views and dashboards of your entire multi-cloud environment, with graphs capturing spend down to the hour up to a two-year period.  Why use Anodot? Demystifying cloud costs is our area of expertise, and we’ve created the AI tools you need to address cloud price fluctuations. By partnering with Automat-it, our CostGPT is now enhanced with Amazon Bedrock, proving the perfect aid to identify hidden prices, poor cost monitoring and reporting, all with you asking a simple search about your cloud spend.  Want a proof of concept?  Talk to us to learn how much you can save with Anodot’s tools.  

With Anodot's partnership, monday.com's vision for FinOps innovation has become a reality.

Microsoft offers several cost-saving opportunities with Azure pricing. Azure Reserved Instances, Azure Savings Plan, Azure Hybrid Benefit... all can help you save a pretty penny if you know what you're doing. If you're looking to cut costs with your Azure VMs, Spot VMs can be the way to go – but the risks can be just as high as the rewards.  How do you help your FinOps organization save big – and improve your chances of avoiding a complete resource shutdown? Learn all the ins and outs of Azure Spot VMs below.  In this article: What are Azure Spot VMs Why use Spot VMs? How much do Spot VMs cost Who should use Spot VMs? How do Azure Spot VMs work How to deploy Azure Spot VMs Azure Spot VMs vs. Reserved Instances Get complete visibility into your Azure Spot VM spend What are Azure Spot VMs   [caption id="attachment_16623" align="aligncenter" width="512"] Source: Microsoft[/caption] A Spot VM is a cloud server instance available for up to a 90% discount. Of course, with big discounts come big drawbacks. In this case, Azure has the right to evict – basically, it can make the VM unavailable and disrupt your Spot VMs with little to no notice.  So, knowing that Spot VMs can be interrupted anytime, why would you ever want to use them?  Applications that best run Spot VMs   Spot VMs can appeal if you're running certain types of jobs that can stand to be interrupted. If you're doing any of the following tasks, you might want to look into this opportunity:  Test and development environments Already using Azure DevOps? Then your test and dev enivronments are a great place to start. These are places where software developers can code and test new applications in a product environment that won't impact the current status quo. Since these environments aren't cheap, Spot VMs can be highly appealing.  For example, let's consider a hypothetical FinOps organization. Say this company wants to test new security measures that should offer extra comfort to clients, but this might introduce a horde of new bugs. The best way to ensure a smooth, glitch-free experience is through rigorous testing in a dev environment. Since the testing isn't customer-facing, it can be done in a Spot VM space.  Batch jobs Batch jobs are automatic tasks typically processed in large groups – a.k.a. "batches". Since these tasks usually have flexible start and end times, it doesn't hurt the workload to be randomly paused or started.  Retake our FinOps organization example. Say you’re running transactions toward the close of business. You might load up thousands to millions of transactions to validate and update an account. Spot VM instances can be a great place to do this.  Fault-tolerant applications Fault-tolerant applications can continue even after certain components fail due to special redundancy measures and failover mechanisms.  Let’s say our FinOps company has multiple Azure SQL servers established through Azure Functions that focus on handling customer support. This means that requests will be automatically routed through the next available server, even if one server fails.  [CTA id="cad4d1a1-3990-4d6b-bb21-ccdcbb6949db"][/CTA] Why use Spot VMs?   Spot VMs can be wildly unpredictable. We’re talking like a 30-second eviction notice.  Why does Azure do this?  Like most major cloud providers, Azure has a lot of unused compute capacity on its platform. During low demand, it wants to sell that space to make a little extra cash. However, when the demand for compute resources increases, the first thing Azure is looking to cut is Spot VM instances.  So, why risk it?  The most apparent advantage of Spot VMs is the cost savings they offer. A 90% discount means you can put those dollars elsewhere. You can scale up workloads on your new Spot VMs. You can invest more elsewhere in your cloud environment, like beefing up your cloud forecasting tools or transitioning into GreenOps to save the real-world environment, not just the cloud environment.  When your VM inevitably evicts you, you can handle that easily enough by having a metaphorical eviction policy that quickly and easily handles the lost VM, restarting your systems once you have available bandwidth again while maintaining your data and workloads.   How much do Spot VMs cost   Spot VM prices vary depending on the following factors:  OS/software type  Region  VM series type  You can also sort Spot VM options by a minimum number of vCPUs/cores and RAM.  Below is a sample table to give you an idea of the costs. The price breakdown shows a possible scenario for purchasing a Red Hat Enterprise Linux OS in the Eastern US region. Instance vCPU(s)/Core(s) RAM Temporary storage Pay as you go Spot A1 v2 1 2 GB 10 GB $41.90/month $14.31/month 66% savings F1 1 2 GB 16 GB $46.79/month $14.90/month 68% savings F1s $1 2 GB 4 GB $46.79/month $14.90/month 68% savings DS1 v2 $1 3 GB 7 GB $63.80/month $16.96/month 73% savings D1 v2 $1 3 GiB 50 GB $63.80/month $16.96/month 73% savings DC1s v2 $1 4 GB 50 GB $80.59/month $18.99/month 76% savings DC1s v3 1 8 GB N/A $80.59/month $18.99/month 76% savings DC1ds v3 1 8 GiB 75 GiB $93.00/month $20.49/month 78% savings B2pts v2 2 1 GiB N/A $27.16/month $22.88/month 16% savings B2ats v2 2 1 GiB N/A $27.89/month $23.10/month 17% savings Azure also has a Spot VM-specific pricing calculator that can help you weigh risk vs reward scenarios.  Who should use Spot VMs? Spot VMs are usually a solid choice if you're an enterprise organization. You'll typically have big testing environments or large workloads that can withstand the sudden start/stop pattern of Azure Spot VMs.  But you can still use and benefit from Spot VMs even if you're not handling enterprise-sized workloads. Organizations that manage experimental or testing environments are also ideal for Spot VMs since they have a high tolerance for random interruptions.  Who shouldn’t use Spot VMs? The real question is, who shouldn’t use Spot VMs?  If your company purely runs monitoring software or web applications, Spot VMs aren't the best choice since the risk of a sudden downtime ruining everything is too high.  How do Azure Spot VMs work   As mentioned above, Spot VMs occur when Azure needs extra compute space to sell, so they’ll offer it for a steep discount. One key thing to remember (besides that Spot VMs can stop working anytime someone wants to pay full price) is that not all Spot VMs come with those 90% discounts. Remember: Spot VMs have such steep discounts because Azure is trying to sell that space quickly. That means the more spare compute capacity Azure has for a certain VM, the bigger the savings (since they'll want to sell that unused space faster). Savings can also vary depending on the VM configuration, compute capacity, or cloud region you're shopping in. Different regions might have steeper discounts.  Typical rates range between 75% to 90%, though sometimes savings can be as low as 30% or 40%. At that point, you're better off using Reserved Instance types like we mentioned above. How to deploy Azure Spot VMs   You have two options for deploying Spot VMS: a single Spot VM or a Spot VM Scale Set, also known as a group of Spot VMs. And don’t worry—Azure Spot VMs are as easy to deploy as Azure Machine Learning.  Our guide below details how to do this from the Azure portal, but you can also use Azure CLI or Azure PowerShell or another method to start your Spot VM. You can refer to the Azure guidelines to learn more.  Deploying Single Spot VMs Start with creating VMs from the Azure portal since that's the easiest way. You can do this by:  Go to "Virtual Machines" listed under "Services." Select "Create".  Fill out all necessary details in the "Create a virtual machine page". Don't forget to check the "Run the Azure Spot discount" box.  Select the "Eviction type" and "Eviction Policy" that works best for you.  For "Eviction type," you can pick either "Capacity only" or "Pricing or capacity". The former means your VM will only get evicted when Azure VMs are in high demand, forcing you to set a maximum price to pay for the Spot VM since that number can fluctuate. The latter lets you reduce that maximum price, and you'll then get evicted either when Azure runs out of room, or the cost exceeds your set price point.  For "Eviction Policy," you can either select "Deallocate" or "Delete." Deleted means everything is permanently lost. Deallocated means the VM pauses but can be resumed. You'll typically want to pick deallocated so you can pick up where you've left off, but if you're worried about spiking Azure storage costs, deletion might be best.  Deploying Virtual Machine Scale Sets (VMSS) Creating Spot Virtual Machine Scale Sets (VMSS) is very similar to creating a single Spot VM.  Find Virtual Machine Scale Sets.  Follow the same steps as the Spot VM creation process. Make sure to know if what kind of Eviction type and Eviction Policy you want to go with!  With VMSS, you can also automate the kind of deployment and management types you want, so it's easier to scale. For example, you can pick CPU usage or network traffic to help cap or bolster scaling or stick with the same maximum price and availability requirements offered by the single Spot VM setup.  Azure Spot VMs vs. Reserved Instances   Spot VMs aren’t the only way you can spend on Azure spend. As mentioned above, Reserved Instances can sometimes be more appealing despite lower deals. Let's explain why you might want to choose one deal over another.  Spot VMs You already know by this point that Spot VMs can help you save up to 90%. But Spot VMs also come with the steep drawback of a 30 second warning before eviction, and a fluctuating savings offering depending on availability.  If you're dealing with a flexible workload, Spot VMs will usually offer the better savings deal—so long as you're prepared for unpredictability.  Reserved instances On the other hand, Reserved Instaces require a one—or three-year commitment. You can get a VM for a much lower cost than the on-demand Azure pricing, but you'll have to be ready to use it for either one or three years. You don't need to worry about eviction, but you're offered no flexibility.  If you know that you'll have a steady amount of work for the next one or three years—and you don't want to deal with the unpredictability of Spot VMS—Reserved Instances are probably the better option for you.  How to optimize Azure Spot VMs   Here are our best practices to reduce the risk and increase the rewards of using Spot VMs:  Set maximum VM prices [caption id="attachment_16624" align="aligncenter" width="426"] Source: Microsoft[/caption] As we mentioned above, setting a maximum price can prevent Azure from pulling a fast one on you by changing the Spot VM price when availability becomes less limited. You'll get evicted from your VM, yes, but you'll have much better control over your budget.  Confirm available capacity Since capacity can change depending on Azure customer demand, you'll want to make sure the region and VM size you need are available for purchase.  Get an interruption strategy  Spot VMs can be interrupted at ainy time, so make sure you're implementing strategies to address this, not just using workloads that can withstand frequent pauses. Consider using checkpointing to save work often done so you can pick up where you left off if abruptly stopped.  Review previous evictions Make sure to review historical eviction rates. While there is not guarantee of eviction rates matching past trends, this still gives you can idea of the interruptions you'll have to work around so you know if you have workloads that are a good fit or not.  Consider using Spot Priority Mix Spot Priority Mix is a unique Azure feature that lets you combine standard and Spot VMs. This set up will allow you to automatically move Spot VM workloads when you get evicted to a standard VM. If you're looking to run hundreds of VMs, you can use Spot Priority Mix, so you don't have to worry about the risks of relying heavily on Spot VMs.  Use Azure Backup You can also keep your Azure Spot VM data secure by using Azure Backup. This means your data will remain 100% secure and recoverable no matter the eviction.  Monitor, monitor, monitor Our final pro tip for savings with Azure Spot VMs is that Spot pricing can change even after you've deployed a server. So even if you've captured a 90% discount rate, you'll want to monitor accordingly because Azure can and will pull the rug out from under you and change that fee.  Azure provides some tools to help you keep a pulse point on your fluctuating costs, but they can be limited in the amount of information provided as far as cost optimizations. If you're really looking for the best way to save on Spot VMs, you'll want to consider going third-party.  Get complete visibility into your Azure Spot VM spend   To truly master the savings Azure Spot VMs offer, you’ll need far more than the monitoring tools Azure provides you. Going third-party is the only way to ensure your cloud resources are performing at maximum capacity at all times and not a penny has gone to waste.  And the best news? You can stack up to 90% savings from Azure Spot VMs with up to 40% annual cloud spend savings offered by a cloud optimization tool like Anodot.  Why go with Anodot? We’ll make sure a Spot VM eviction never catches you by surprise. Our AI-powered anomaly detection means you’ll be ready for anything.  Anodot was made for demystifying cloud costs for FinOps organizations. Our real-time dashboards and customized alerts mean you can keep a 24/7 pulse on your spend not only with Azure and Spot VMs but also your entire multi-cloud experience. Our tools offer AI-powered feedback, which means you can start savings without ever having to dig into the data.  Anodot’s dashboards capture all of your multicloud spend, with a specific focus on opportunities where you can save. So if you want to be thrifty with Azure Spot VMs, we’ve got your back. We’re here to ensure that you get the most lift for each dollar spent and get some of those dollars back to reconfigure your monthly budget.  Want a proof of concept? Talk to us to learn how much you can save with Anodot’s tools.  

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.  In this article: What is AKS (Azure Kubernetes Service)? How does AKS pricing work? How to optimize your Azure Kubernetes Service costs Get complete visibility into your AKS spend 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! In this article: What is AWS GovCloud? What is Azure Government? AWS GovCloud vs Azure Government – which is best? What are other major government cloud providers? Should you switch to a government cloud service? Considering AWS GovCloud? Stay 100% Secure and Gain Multicloud Visibility with Anodot 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 managed service provider (MSP).  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 cloud 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.  In this article: What is GovCloud? Why GovCloud? How to Use GovCloud Are there GovCloud drawbacks? Is GovCloud the best cloud provider for US government agencies? Should you use GovCloud? Optimize your GovCloud spend 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 U.S. managed service provider.    Outside of the U.S.? Don't sweat it. Check out our list of the top managed services providers in the U.K. and Israel.  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 via autoscaling. 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.