Page 13 of Umbrella Resources

What is a Reserved Instance?   An Amazon EC2 Reserved Instance (RI) is one of the most powerful cost savings tools available on AWS. It’s officially described as a billing discount applied to the use of an on-demand instance in your account. To truly understand what RI is, we need to take a step back and look at the different payment options for AWS. On-Demand – pay as needed. No commitments. Today you can use 1,000 servers and tomorrow it can only be 10 servers. You are charged for what you actually use. Spot - Amazon sells its server Spot. This means Amazon sells its leftover server space that it has not been able to sell without the use of a data center. The server is the same server that they provide with the on-demand option. The significant difference is that Amazon can request the server back at 2 minutes notice (this can cause your services to have an interruption). On the other side, the price can reach a discount of up to 90%. In most cases, the chances of them asking for the servers back is very low (around 5%). Reserved Instances - Simply put, you are committing to Amazon that you are going to use a particular server for a set period of time and in return for a commitment, Amazon will give you a discount that can reach as high as 75%. One of the most confusing things about RI (as opposed to On-Demand and Spot) is that with RI you don’t buy a specific server but your on-demand servers still get the RI discounted rate. What is being committed?   Let’s look at the parameters that affect the height of the RI premise: The period: 1 year 3 year The Payment option: Full up-front Partial up-front No up-front (will charge 1st of each month) Offering Class: Standard Convertible Of course, the longer the commitment, and the upfront payment is higher, the assumption that Amazon offers is more significant. The above graph illustrates different RI options with respect to on-demand and recommending a specific RI that is tailored to each customer’s specific needs. In addition, when you purchase a RI, you are also committing to the following parameters: Platform (Operation system) Instance Type Region The RI is purchased for a specific region and at no point can the region be modified. To be clear, when we commit to Amazon on a particular server, we also have to commit to the operating system, region and, in some cases, instance size. Usually, after a few months the RI usage has improved its on-demand price and after the break-even point, every minute of running is considered “free” in relation to on-demand.   Related content: Read our guide to AWS Pricing Load Balancer Standard or Convertible offering   With RI, you can choose if we want the Standard or Convertible offering class. This decision is based on how much flexibility we need. We can decide how long we are willing to commit to using the RI and we can choose both our form of payment and if we prefer to pay in advance. Obviously, the more committed you can be to Amazon (longer period, prepay, with less change options etc.) the greater the discount you will get. We still need to clarify the differences between Standard and Convertible. In the Offering Class Standard, you commit to specific servers while Convertible is a financial commitment. This means, you commit to spend X money during this time period and are more open to flexibility in terms of the type of server. Below is a comparison from the AWS website about the differences between Convertible and Standard. Now that we have a better understanding of what RI is, we need to understand how to know how much you should commit to Amazon and what kind of commitment meets your needs. As we know, we cannot predict the future, but we can make educated conclusions on the future based on our past activity. It is also important to note that when you commit to RI, you must run the particular server 744 hours a month (assuming there are 31 days). The discount only applies per hour so if you were to run 744 servers in one hour, only one server will get the discount. In addition, it can be difficult to understand how Amazon figures out the charge. For example, if at some point there are 6 servers running together, Amazon can decide to give each server 10 minutes of the RI rate and 50 minutes of standard on-demand rate. The decision which server gets the discounted rate is Amazon’s alone. If a particular account has multiple linked accounts, and the linked account that bought the RI did not utilize the RI at a given time, the RI discount can be applied to another linked account that is under the same payer account. RI Normalization factor   Recently Amazon introduced a special deal for RI running on the Linux operating system. The benefit is that you do not have to commit to the size of the server but rather only to the server type. So assuming I bought m5.large but actually used m5.xlarge, 50% of my server cost would be discounted. The reverse is also true if I bought m5.xlarge but in practice, I ran m5.large it will get the discount (both servers will get the discount). Amazon has created a table, which normalizes server sizes, and it allows you to commit to a number of server-type units rather than size. In order to intelligently analyze which RI is best for you, it is necessary to take all the resources used, convert the sizes to a normalization factor and check how many servers were used every hour, keeping in mind that you will only get the discount for one hour of usage at a time. You also need to deduct RI that you have already purchased to avoid unnecessary additional RI purchases. Additionally, there will be some instances where servers may not run in succession and there is a need to unite between different resources. Lastly, it is also possible that certain servers may run for hours but do not complete a full month. Despite the above complexity and the need to analyze all of these factors, the high discount obtained through RI, may still result in a significant reduction in costs. Umbrella’s algorithm takes all the above factors and data into account, converts the Normalization factor wherever possible, tracks 30 days of history, and uses its expertise to provide the optimal mix for each customer. Undoubtedly, RI is one of the most significant tools for reducing your cloud costs. By building the proper mix of services combined with an understanding of the level of commitment you can safely reduce your cloud costs by tens of percent. Optimizing AWS EC2 with Umbrella   Umbrella’s Cloud Cost Management solution makes optimization EC2 compute services easy. Even with multi-cloud environments, Umbrella seamlessly combines all cloud spending into a single platform allowing for a holistic approach to optimization measures. Umbrella offers built in, easy-to-action cost-saving recommendations specifically for EC2, including: Amazon EC2 rightsizing recommendations EC2 rightsizing EC2 operating system optimization EC2 generation upgrade Amazon EC2 purchasing recommendations EC2 Savings Plans EC2 Reserved Instances Amazon EC2 management recommendations EC2 instance unnecessary data transfer EC2 instance idle EC2 instance stopped EC2 IP unattached Umbrella helps FinOps teams prioritize recommendations by justifying their impact with a projected  performance and savings impact. Umbrella learns each service usage pattern, considering essential factors like seasonality to establish a baseline of expected behavior. That allows it to identify irregular cloud spend and usage anomalies in real-time, providing contextualized alerts to relevant teams so they can resolve issues immediately. Proprietary ML-based algorithms offer deep root cause analysis and clear guidance on steps for remediation.

Organizations using Amazon Web Services (AWS) cloud traditionally leveraged Reserved Instances (RI) to realize cost savings by committing to the use of a specific instance type and operating system within the AWS region. Nearly 2 years ago, AWS rolled out a new program called Savings Plans, which give companies a new way to reduce costs by making an advanced commitment of a one-year or three-year fixed term. Based on first impressions the immediate understanding was that saving money on your AWS would be significantly simpler and easier, due to the lowering of the customer’s required commitment. The reality is the complete opposite. With Amazon’s Saving plans, it is significantly harder to manage your spending and lower your costs on AWS Plans, especially if you only rely on Amazon’s tools. 1. What are Savings Plans? To understand why the new Saving Plans significantly complicate cloud cost management, it is necessary to briefly review the two savings plan options. EC2 Compute Saving Plan The EC2 Savings plan is just a Standard Reserved Instance without the requirement of having to commit to an operating system up front. Since changing an operating system is not routine, this has very little added value. Compute Saving Plan With this product Amazon has clearly introduced a new line. The customer no longer has to commit to the type of Compute he is going to use. You no longer have to commit to the type of machine, its size or even the region where the machine would run, these are all significant advantages. In addition, Amazon no longer requires a commitment to the service that will use Compute. It does not have to be EC2, which means that when purchasing Compute Saving Plans, using Compute in EMR, ECS EKS clusters or Fargate can also be considered a guarantee and you will receive a discount. In RI Convertible, to get a discount on a different server type, rather than the original server for which we purchased the RI an RI change operation was required. With the new Compute Plan, it is not necessary to make the change and the discount is automatically applied to the different types of servers. The bottom line is that you commit to the hourly cost of computing time, however, you choose whether the commitment is for one or three years and how you want to pay i.e. prepayment, partial payment, or daily payment. At this stage, it sounds like Compute Saving Plans would simplify and lower your costs, as the commitment is more flexible. However, as we stated above, the reality is much more complex. 2. Are Amazon’s Saving Plan Recommendations Right for Me? Let’s start with the most trivial yet critical question, how do I know the optimal computing time for me? Amazon offers you recommendations of what your computing time costs should be and what they feel you should commit to buying from them. It’s interesting that Amazon offers these recommendations considering they don’t share usage data with their users. So what is this recommendation based on? Amazon is recommending to their users to commit to spend hundreds of thousands of dollars a month without any real data or usage information to help users make an educated investment decision. Usually when people commit to future usage they do so based on past usage data. The one thing that Amazon does allow you to do is choose a time period on which their recommendation will be based on. For example, based on usage over the last 30 days of a sample account, Amazon recommended a spend of $ 0.39 per computing hour. The IT manager can simply accept Amazon’s recommendation, but with no ability to check the data the resulting purchase could cost the company a significant amount of additional and unnecessary money. In the example above, there was significant usage over the last 30 days, however a couple of weeks prior to this, there may have been a significant change, such as a reduction in server volume and/or a RI acquisition and therefore the recommendation here should have been particularly lower. This is even truer if Saving Plans had already been purchased and had earned an actual discount. 3. How do I know which savings plan is best for my company? On this large and significant vacuum Umbrella for Cloud Cost can provide a lot of value. Using Umbrella, you can see your average hourly cost per day for the last 30 days. Since the Saving Plan estimate does not include the Compute hours already receiving an RI discount, Umbrella only displays the cost of Compute on-demand. It is also critical for a user who has already purchased and is utilizing Saving Plans to know how this impacts his costs before making any additional commitments. Umbrella shows the actual cost of each individual computing hour over the last 30 days to enable educated decisions that can impact significant multi-year financial commitments. Umbrella utilizes its unique algorithm and analyses all your data to deliver customized recommendations on what will be the optimal computing time cost that you should actually commit to. It is important to note that when purchasing a Compute Saving Plans, it is not possible to know at the time of purchase what your exact discount will be. The actual amount of the discount can be only estimated in all cases other than RI. This uncertainty is due to an additional complexity that exists in Compute Saving Plans. Each type of server receives a different discount, so in practice the discounts that you receive depends on the type of server you actually run and if Amazon’s algorithm chooses to provide that type of server with the Saving Plan discounts offered.

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Many companies have tried to feed business data, such as business activity, into IT or APM monitoring solutions, only to discover the data is too dynamic for static thresholds. Some companies choose to depend on analyze BI dashboards to find issues, but that leaves anomaly detection to chance. As companies have tried to solve these challenges, AI is driving a future where monitoring business data is monitored autonomously.

Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cloud cost monitoring. There are countless services to keep track of—including storage, databases, and cloud computing—each with its own complex pricing structure. Cloud cost monitoring is essential for both cloud cost management and optimization. But monitoring cloud spend is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.  Many cloud providers such as AWS, Google Cloud, and Azure provide you with a daily cost report, but in most cases, this is not enough. For example, if someone is incorrectly querying a database for a few hours this can cause costs to skyrocket—and with a daily report, you wouldn’t be able to detect the spike until it’s too late.  While there are cloud cost management tools that allow you to interpret costs, again these technologies often fall short as they don’t provide the granularity that’s required in real-time monitoring. Similarly, without a real-time alert to detect and resolve the anomaly, the potential to negatively impact the bottom line is significant.  As we’ll see from the examples below, only an AI-based monitoring solution can effectively monitor cloud costs. In particular, there are three layers to Umbrella’s holistic cloud monitoring solution, these include: Cost monitoring: Instead of just providing generic cloud costs, one of the main advantages of AI-based monitoring is that costs are specific to the service, region, team, and instance type. When anomalies do occur, this level of granularity allows for a much faster time-to-resolution. Usage monitoring: The next layer consists of monitoring usage on an hourly basis. This means that if usage spikes, you don’t need to wait a full day to resolve the issue and can actively prevent cost increases. Cost forecasting: Finally, the AI-based solution can take in every single cloud-based metric - even in multi-cloud environments - learn its normal behavior on its own, and create cost forecasts which allow for more effective budget planning and resource allocation. Now that we’ve discussed the three layers of AI-based cloud cost monitoring, let’s review several real-world use cases. Network Traffic Spikes In the example below, we can see that the service is an AWS EC2 instance, which is being monitored on an hourly basis. As you can see, the service experienced a 1000+ percent increase in network traffic, from 292.5M to 5.73B over the course of three hours. In this case, if the company was simply using a daily cloud cost report this spike would have been missed and costs would have also skyrocketed as it’s likely that the network traffic would have stayed at this heightened level at least until the end of the day. With the real-time alert sent to the appropriate team, which was paired with root-cause analysis, you can see the anomaly was resolved promptly, ultimately resulting in cost savings for the company. Spike in Average Daily Bucket Size The next use case is from an AWS S3 service on an hourly time frame. In this case, the first alert was sent regarding a spike in head request by bucket. As you may know, bucket sizes can go up and down frequently, but if you’re looking at the current bucket you often don’t actually know how much you’re using relative to normal levels. The key difference in the example below is that, instead of simply looking at absolute values, Umbrella’s anomaly detection was looking at the average daily bucket size. You can see that the spike in the bucket size is not larger than the typical spikes, but what is anomalous is the time of day of the spike. In this case, by looking at the average daily bucket size and monitoring on a shorter time frame, the company received a real-time alert and was able to resolve it before it incurred a significant cost. [CTA id="dcd803e2-efe9-4b57-92d5-1fca2e47b892"][/CTA] Spike in Download Rates A final example of cloud cost monitoring is monitoring the AWS CloudFront service, which was again being monitored on an hourly timescale.  In this case, there was an irregular spike in the rate of CloudFront bytes downloaded. Similar to other examples, if the company was only monitoring costs reactively at the end of the day, this could have severely impacted the bottom line. By taking a proactive approach to cloud-cost management with the use of AI and machine learning, the anomaly was quickly resolved and the company was able to save a significant amount of otherwise wasted costs. Summary: As we’ve seen from these three examples, managing a cloud-based infrastructure requires a highly granular solution that can monitor 100 percent of the data in real-time. If this unexpected cloud activity isn’t tracked in real-time, it opens the door to runaway costs, which in most cases is entirely preventable. In addition, it is critical that cloud teams understand the business context of their cloud performance and utilization. An increase in cloud costs might be a result of business growth - but not always. Understanding whether a cost increase is proportionately tied to revenue growth requires context that can be derived only through AI monitoring and cloud cost management. AI models allow companies to become proactive - rather than reactive - in their cloud financial management by catching and alerting anomalies as they occur. Each alert is paired with a deep root-cause analysis so that incidents can be remediated as fast as possible. By distilling billions of events into a single scored metric, IT teams are able to focus on what matters leave alert storms, false positives, and false negatives behind, gain control over their cloud spend, and proactively work towards cloud costs optimization.

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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 Umbrella's CostGPT. Background:   Automat-it is leveraging Umbrella'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 Umbrella to integrate its AI-driven CostGPT platform into daily operations to meet this demand. Partnering with Automat-it and leveraging their POC recommendations, Umbrella 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 Umbrella 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: Umbrella's CostGPT   Automat-it empowers its clients with Umbrella'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 Umbrella's AI-driven CostGPT platform into its clients' daily operations. Results:    With the implementation of Umbrella 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, Umbrella 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 Umbrella 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 Umbrella, 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. Umbrella’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 Umbrella.

Download this report to learn about the time of cost anomaly detection, realized cost savings and more. Learn what the top industry players and over 1000 Umbrella customers are challenged by and how they optimize their cloud costs.