In some cases, we receive a commission from our partners; however, our opinions are our own. Spot pet insurance provides comprehensive pet insurance, including an allowance for end-of-life memorials.

Spot Pet Insurance Review 2023

Compounding those shallow depth charts are prickly stops in the schedule — also known as situational angles or spot bets. These are quirks in the calendar that could have an underlying role in ...

NFL Week 7 Odds: Best Spot Bets Include Potential Browns Letdown

In practice, the best free cloud storage services aren’t that easy to pin down. Though an appealing part of being able to start storing photos and other media without using local hard disk drive ...

Best free cloud storage of 2023

England have booked their spot at Euro 2024 after a 3-1 win against Italy at Wembley on Tuesday night. Atalanta striker Gianluca Scamacca, a summer signing from West Ham, opened the scoring but ...

Harry Kane double sees England defeat Italy to book spot at Euro 2024

This is the bizarre moment stunned locals spot an unidentified flying object ... The second object also disappeared in the cloud." It is not the first time mysterious objects have been spotted ...

Surprised locals spot weird bouncing 'UFO' darting around the sky at night

Spot the difference puzzles are excellent ways to sharpen your visual recall and mental agility. They challenge your observation skills and attention to detail, as telling the differences between ...

Use your sharp eyes and spot 3 differences in the dog at the park picture in 7 seconds.

The Boston Celtics have at least one roster spot open. While they could go into the season with 14 full-time players, Bleacher Report’s Adam Fromal explained why the Celtics are the best spot ...

Celtics Called ‘Better Landing Spot’ for 6-Time All-Star

A crypto news website has apologised after its incorrect social media post about the approval of a spot Bitcoin exchange-traded fund (ETF) was debunked and Bitcoin (BTC) prices spiked early in the ...

Bitcoin price jumped after false news about spot ETF approval

All things being equal for Miami, based on team need, familiarity and the satisfaction of righting a wrong, signing free-agent guard Kendrick Nunn to fill the Miami Heat’s 14 th roster spot is ...

Miami Heat 14th Roster Spot: Erik Spoelstra Favorite Still a FA

As a result, Huawei overtook the iPhone in the No. 1 spot for market share. Apple's iPhone has officially been dethroned from its position as the smartphone market share leader in China ...

Apple's iPhone loses top spot in China to Huawei: Analysts

Get the most recent info and news about Cloud Spot on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

CloudSpot is a software company based in Irvine, CA that makes photographer's lives easier. Our CloudSpot app allows photographers to deliver images digitally to clients in beautiful galleries through a seamless system that is fast and easy. We're the only company in the segment that was founded by professional photographers, so we know exactly what photographers needs are. Our goal is to eliminate the hurdles of image delivery and to build a community around a product that can support and build our community's businesses and lives.

Cloud Spot

Software engineers are builders - so it’s only natural for them to want to build in-house proprietary software tools, customization and integrations. But that instinct can often be problematic for businesses. Here are some reasons you should think twice before letting your engineers build their own internal software: Tech savvy investors are wary of custom internal software. On the noted it’s one of the primary goals for the businesses they acquire: get rid of bespoke software. Why? The engineers or consultants you hire can’t be expected to know how to use your custom, internal tools. AWS Podcast, MacLaurin Group partner Alan Williamson Because it’s hard to scale if your development process doesn’t rely on industry standards. The technical debt of non-consumer facing software will almost always increase over time. While it might be initially exciting for your team to develop its own tools and integrations, few like to properly fix the flaws are discovered later . Developing internal, custom tools may make your team feel in control, but it’s an illusion. Your custom software will still run on ever-changing technology platforms and interact with external software in ways that will require refactoring or patching anyways. Is a short-term fix now worth being stuck with a problematic legacy application later? While the dynamic pace of software development will always present challenges, there’s growing evidence that the best performing DevOps teams avoid using proprietary tools when possible. Dr. Nicole Forsgren released an extensive study with Google Cloud on DevOps practices compiled from responses of 31,000 IT professionals. You can read the entire . 2019 Accelerate State of DevOps Report here On pages 59 and 60, Dr. Forsgren notes that among the . The better performing teams only did so 5% to 6% of the time. Respondents in the lowest performing group were also more likely to rely primarily on a mix of customized open source and commercial off-the-shelf products. The better performing DevOps respondents were also 3 to 5 times more likely to primarily use open source tools with little customization - and planned to use more. lowest performing DevOps teams, 20% relied primarily on in-house and proprietary tools heavily In another recent study by , They noted this practice isn’t only time-consuming, but error-prone and insecure. The complexity of the number of tools and toolchains is already making it impossible for any one engineer to actually know how they all work together. Using bespoke tools and integrations only makes it even harder. Forrester Research on tools and toolchains they found 20% of their respondents also relied on manually hard-coding custom integrations between tools. Of course, there are times to build custom internal software and there are challenges to managing open source and off-the-shelf tools as well. In fact, Dr. Forsgren also found that contributing and being involved in a vibrant open source community helped engineers feel productive and reduced burnout. But open source shouldn’t be dismissed as insecure any more than tools developed internally should be considered “free”. The Forrester report also came to the conclusion that it would be better for teams to adopt out-of-the-box toolchain management solutions. 47% of respondents that had reported an increase in security, 46% an increase in revenue, 45% an improvement in quality and 42% an increase in developer productivity. Buying a product to make your toolchain more manageable and secure can be well worth the price of resisting your engineer’s instincts to code all the integrations themselves. Again, there is no denying that with the ever increasing complexity of tools and toolchains that custom, in-house solutions are going to be of your development environment. But there’s lots of evidence to suggest you should try and make it as small a part as possible. After all, would you rather your highly-skilled by retooling your architecture to use - or constantly paying off the technical debt of ‘fixing’ your legacy, internal applications? part AWS DevOps engineers spend their time cutting cloud costs Spot instances

Avoiding In-House Development Tools Now May Pay Off Later

To pique your curiosity about robotics, we bring you the product review you were waiting for, a comparison between real sense cameras, one of the main hardware pieces used in all types of robots. Cameras are as necessary for the development of autonomy as eyes are for humans. These assets help autonomous robots look like they can go from one point to another on their own. Many engineers find themselves looking for different camera comparisons as they are essential for autonomous technology to run. In this blog, Rafael Rincón, Computer Vision and Integration Engineer at Kiwibot, wanted to show the comparison of the stereo cameras that estimate the depth of the robot's immediate environment: D435i, D435f, and D455 and some considerations about the tests made with the Luxonis' OAK-D Pro camera. Stereo cameras use two or more lenses with separate image sensors for each lens to simulate human binocular vision. The distance between the lenses often mimics the distance between our eyes. The cameras can capture three-dimensional images, for 3D pictures for movies, or to avoid drone collisions, among many other uses. For most applications, stereo cameras with a three-meter range for depth data are more than sufficient, particularly if they will be used indoors. However, when exposed to sunlight and outdoor environments, the data from these cameras can be highly affected, and the precision of depth maps and point clouds is considerably reduced. With a background in the sidewalk navigation of robots, Rafael wanted to test three top-of-the-range cameras in outdoor environments to find the most suitable camera for outdoor usage, like field robotics. The Cameras' Backstory First up, we have the Intel Realsense D435i (with an IMU) and D435f (with a polarizing filter). Both variations of the D435 camera should have the same depth range and limitations: an ideal range of up to three meters and an expected error of <2% at two meters. The D435i camera is very reliable in indoor situations, but in outdoor environments, its data can be significantly affected after two meters – and the Realsense team is aware of this. Next up is the Intel Realsense D455, with a depth range of six meters. in both indoor and outdoor environments. The expected error is <2% at four meters. The product can be used Despite stating these depth ranges, all the Intel models can be used up to 10 meters by modifying the threshold filter of the stereo module using the . We used this mode in all the tests. Realsense Viewer tool Last but not least, we tested the Luxonis OAK-D Pro camera. It is a depth perception of up to 35 meters. However, data located at such a distance range could be overkill for most applications. Applications include making measurements, calculating where a point is, getting its coordinates, and estimating the depth of nearby obstacles. capable of having The Moment of Truth The setup and test were simple: Rafael used Kiwibot's test track, with an already defined measurement template and significant sunlight. He placed all cameras six meters from a reference object: a chair, in this case. Any error that occurred would be considered over the camera's Z-axis (in most stereo cameras, the Z-axis is perpendicular to the camera's lenses), the most straightforward and stable measurement over the chair. As expected, the D435i camera experienced an error of around 38%. The measurements were near 8.3 meters at the back of the chair (the real distance was 6 meters). The measurement range was way over the expected camera range. The D435f camera showed outstanding performance, with an error of around 8% and measurements near 6.5 meters at the back of the chair. What makes these results an astonishing find is the fact that this is the same D435i camera – just with a polarizing filter placed on the camera's main lens, with the objective to eliminate all the interference of brightness from solar noise on reflective surfaces, like glass and tiles. A 6-meter range is nearly twice its intended measurement reach. The D455 camera had remarkable results, with an error near 2.2%, with measurements around 6.1 meters at the back of the chair, making the D455 camera first place for performance. The OAK-D Pro camera experienced an error of 25%, and the measurements were close to 7.5 meters at the chair. Point Cloud Outcomes: Indoors vs. Outdoors A point cloud is a set of data points representing a 3D shape produced from stereo images. The visualizations were plotted in RViz2 using the ROS2 wrapper. While the D455 camera outperformed when measuring depth values at longer distances – showing that it is much more robust in measurements due to a more modern depth module – its point cloud deteriorated and was not drastically different from the D435i camera. But the D435f had remarkable point cloud quality compared to the D435i. The point clouds of the different cameras indoors were as expected. https://www.youtube.com/watch?time_continue=2&v=gSfVPv7_D5I&feature=emb_title However, the outdoor operation with a higher presence of sunlight showed the degradation of the point clouds. The OAK-D Pro camera generated a point cloud with good depth perception, but it was very noisy in some sections, regardless of distance. However, the point cloud quickly spotted approaching obstacles, which is observed in the last seconds of the clip. It is important to note that, during the test, the point cloud stopped publishing several times. https://www.youtube.com/watch?v=P2MOFBGC5r4 The D435i had a small point cloud with noticeable effects from the sunlight, but the filter present in the D435f camera is an absolute game-changer. The visualization of the D435f point cloud was smoother than the OAK-D Pro model, and it was easier to track the displacement of people up to 4 meters of distance. The quality and integrity of its point cloud are way better than the D435i camera, and only just below the results for the D455 camera, which had a wide point cloud outcome. https://www.youtube.com/watch?v=qfSWwd4q2wM&feature=emb_title Can't Forget Detail Quality The following clip was recorded with the OAK-D Pro and the D435i camera to compare the quality of the texture. The OAK-D Pro point cloud was very detailed. However, with a closer look, you can see that Rafael's face and the front of his body look totally flat. Contrastingly, the Realsense camera displays a very detailed 3D model of his face, chest, and even his headphones. https://www.youtube.com/watch?v=Br0jH3rqqVA&feature=emb_title Overall, the Luxonis OAK-D Pro camera is a solid product, but there's always room for improvement. Its main advantage is that it has a small computer in the camera which allows for running AI models in the camera instead of a computer, relieving the load on the main system. However, that was not tested during Rafael's experiment with the stereo cameras, as he was testing the precision of the data, the reading of the point cloud, and which camera could block out the noise in outdoor environments. Although this is just a first look, Luxonis could be a strong competitor of Intel in the near future and is a brand to keep your eye on for the next few years. Disclaimer: We are sharing this article because having all four cameras in your possession is highly unique. Kiwibot wanted to share Rafael's experience as Computer Vision and Integration Engineer at Kiwibot and doesn't intend to generate any profit or benefit any brand. Rafael is not recommending which camera to buy; that is dependent on the reader's application and needs.

Thinking of Buying Stereo Cameras? Read This Product Comparison.

Which is the one for you — Spot, on-demand, or reserved instances? Which EC2 instance type do you prefer? 😉 Tweet your thoughts or give us a shout out @TotalCloudio Originally published at blog.totalcloud.io on March 6, 2018.

Spot Vs. On-Demand Vs. Reserved Instances

According to a recent , the worldwide public cloud computing market has been projected to reach $495 billion by end of 2022. survey The present-day cloud technologies turn out to be a vital factor in driving innovation and business growth. However, jumping onto the bandwagon of cloud users without planning for cloud cost optimization makes it an expensive affair. Proceeding further, we will be discussing one such method of cloud cost reduction. That is the use of spot instances. The major public cloud service providers (AWS), , and all offer spot instances. But let’s understand “what are spot instances?” Amazon Web Services Microsoft Azure Google Cloud What are Spot instances? Spot instances are the unused cloud computing capacity that is available at steep discounts compared to the on-demand instances and reserved instances. The cloud service pricing model is divided into the following three categories: – This is the pay-as-you-go model, where you pay in terms of computing power used per hour or second. Here you get the flexibility of increasing or decreasing the resources as per your operational requirements. There are no long-term commitments or upfront charges. On-demand Instances – These costs are slightly lesser than on-demand instances. They are what you consider for a longer period. Say 3 years or 5 years, this is when your entire cloud infrastructure runs on a daily basis. These are the long-term commitments where you get to pay a bigger sum upfront. They cost 40-50% lesser than on-demand instances, the pricing also depends on the cloud service provider. Reserved Instances – As discussed earlier these are offered at steep discounts, i.e., even lesser than reserved instances. But there’s a catch, these do not have any fixed prices, and customers/users have to bid on the unused capacity of the cloud provider’s data center. Now, these prices vary as they are adjusted across the availability zones based on market demand and the biggest consideration is that – this spare capacity can be taken away almost instantaneously! Spot Instances Spot Instances on AWS The Spot instances in AWS use the unutilized EC2 (Elastic Compute Cloud) instance facilitating them in optimizing their cloud costs. These unused EC2 instances have a variable price in the AWS Spot Market depending on the supply and demand of these instances. The pricing of spot instances on AWS is done on an hourly basis and is known as the . spot price Considering the demand and availability of spot instances, AWS determines the auction price (spot price) for each instance type in their respective availability zone. The Working of AWS Spot Instances To begin with, the user needs to create a spot instance request. The request includes the following data – quantity of instances, the type of spot instances, the availability zone, and the maximum bid. When the maximum bid exceeds the auction price, with all conditions of the spot instance request met, Amazon launches the unused EC2 instances. However, if the bid doesn’t exceed, Amazon waits till all the conditions are met to process the request, or the user may cancel the request. But the work doesn’t end here, the spot instances launched would stay only up to the time the maximum bid of the user exceeds the spot price. The very moment when the prices change, the allotted instances get terminated. Or it can even get terminated if the requested capacity is unavailable. A point to be noted is that the user can start or stop an instance manually. Furthermore, if an instance gets terminated the user can open a new request, and again if all conditions are met the EC2 instances are launched. [ A Good Read: Guide to Creating Spot Instance Request on AWS ] How do AWS Spot Instances help Businesses? Cost Saving The biggest benefit of using spot instances is quite obvious, the cost-benefit. It costs less than the on-demand and reserved instances. In this particular , an effective 80% reduction in overall cloud costs is achieved by leveraging Spot Instances. case study on Lenskart Flexibility for Experimentation and Growth ( ) Voost specializes in developing a digital asset management platform for professional cryptocurrency traders and businesses. Their creation ‘Pastel’ ( SaaS Platform ) required connecting with global exchanges to collect large volumes of data. This enables users to view their distributed crypto assets in one place without visiting the exchange. However, being a startup with limited resources, building this infrastructure on on-demand instances made it an expensive affair. This is where they thought of leveraging spot instances, which saved them around 90% in cost over on-demand instances. reference Summing up the benefits from the above case: No more waiting for approvals or incurring any higher costs while giving shape to your ideas. We can act quicker on our ideas, and test them directly with customers without any delays. Spot instances are an effective solution, when not sure whether you need extra resources for certain experimental projects. It gives a choice to businesses for investing in what is critical, and what is required, preventing wastage. Improved DevOps Productivity The principles of DevOps promise faster deliverability of products and services. This holds true when the right amount of automation is implemented in the CI/CD pipeline. It helps in streamlining the DevOps lifecycle by reducing the friction surrounding the testing and release processes, enabling a smoother assembly line. Challenges of Spot Instances Transient Nature The spot instances in AWS are with us till the time – The current price exceeds our maximum bid of the spot instances Or the requested capacity becomes unavailable The moment either of the conditions becomes true, the spot instances are terminated immediately. Any applications working on them get interrupted, and we possibly lose a lot of data in the process. The possible solution is to automate these processes through a platform, whereby all the operations continuing on the AWS spot instances are saved and the very moment spot instances are withdrawn, the platform automatically switches the work to on-demand instances. Thereby balancing the costs and business processes seamlessly. For example: Source Multi-Cloud Adoption Enterprises today are adopting multi-cloud services. It is obvious that different cloud services have different methods of allotting spot instances. Although we are diversifying our resources, at times it becomes a cumbersome process to shuffle the workload among all these variations. Bringing automation to this is going to solve half of our woes. A hybrid cloud deployment platform that creates a secure virtual environment, and helps us in building a safe passage for our product following the compliances simplifies the product journey. Choosing the right combination of Instances The right combination of instances is the blend of spot instances, on-demand instances, and reserved instances for optimal application performance. To be able to know, what part of an application’s service can be compromised (that can be made to work in spot instances), and which parts are critical. For this information, we need to have a good understanding of what our customers want, and this can be achieved by storing data in dashboards format. A practical solution will be an intuitive platform that stores all metrics in a proper dashboard fashion, and accordingly ensures and allots different instances for optimal resource utilization. Now, businesses are no more busy looking after infrastructure requirements, they can direct their focus toward the core business operations. Conclusion The market is very competitive. Businesses are facing steep competition in the form of innovation, agility, and better customer service. This is where spot instances help in filling these competitive gaps. Choosing the right jobs like data analysis, background processing, batch jobs, and other optional tasks can not only bring down our costs but successfully supplement the overall performance of an application. The foundational pillars of a successful business are proper cost optimization, faster deliverability, and a better customer experience. Bringing in this modularity requires a lot of human hours but when considered through an intuitive platform, complexities are reduced to a great extent. Businesses are in for a long marathon and if the right decisions are taken in terms of implementing automation, we are enhancing its lifespan by manifolds. Also Published Here

How to Manage Spot Instances

Amidst the ever-changing tech landscape, stands tall, empowering enterprises to thrive. Multi-Cloud DevOps confirm its impact on business success. Seamlessly deploying applications across diverse clouds is now paramount, and this guide unveils the path to a triumphant Multi-Cloud journey, unlocking the full potential of multiple clouds with cohesive cross-platform excellence. A resounding 90% of organizations DevOps Selecting the Right Cloud Providers Companies must diligently assess their unique objectives, covering aspects like scalability, performance, data security, compliance, and geographical reach, to make informed cloud decisions. A thorough understanding of these requirements sets the stage for aligning with a cloud provider that perfectly echoes the business vision. With a multitude of cloud providers available, a comprehensive evaluation of their offerings is paramount. Beyond basic capabilities, delve into advanced features like AI tools, machine learning, and IoT integration. Consider diverse pricing models such as pay-as-you-go, reserved instances, and contract options to find the best value. Adopting a multi-cloud approach is gaining traction as businesses seek robustness and resilience. Embracing a complementary multi-cloud ecosystem unlocks diverse provider strengths while mitigating vendor lock-in risks. Seamlessly integrating cloud services across multiple providers enhances flexibility and ensures adaptability to ever-changing business dynamics. Designing a Multi-Cloud DevOps Strategy Embark on a journey of clear objectives, shaping your Multi-Cloud DevOps vision. Define your purpose, be it efficiency, uptime, or scalability. Connect your goals to the broader mission for ultimate impact. Decide your destiny with the right deployment model. Tailor your strategy, weighing cloud providers' strengths. Hybrid or Multi-Cloud, build a robust ecosystem, combining the best of both worlds. Lead the charge with CI/CD practices, across clouds. Drive innovation, save time, and reduce errors. embracing automation for continuous integration and deployment Thrive in the ever-changing multi-cloud landscape. Design a scalable infrastructure, flexing with fluctuating workloads. Embrace the power to dynamically scale, optimizing costs and performance. Embracing multi-cloud has , and fortunately, it's not a mere coincidence but a deliberate driver of positive outcomes. become the new norm Automating Cross-Platform Deployment Gone are the days of manual server setups and time-consuming configurations. IaC brings order to chaos by treating infrastructure as software. By defining your infrastructure in code, you gain version control, repeatability, and consistency across platforms. Changes become as simple as modifying a few lines of code, ensuring deployment uniformity, and reducing human errors. Orchestration and configuration management tools orchestrate the entire deployment process, making it a breeze to manage complex multi-cloud setups. These tools automate the deployment, scaling, and management of applications, allowing developers to focus on the code rather than the intricacies of each cloud platform. With centralized control, you can effortlessly manage and update multiple environments at once. One size does not fit all, especially in the multi-cloud landscape. Embrace the power of deployment pipelines customized for each cloud platform. These pipelines automate the entire process, from code commit to production deployment. With continuous integration and continuous deployment (CI/CD) practices, your team can push updates rapidly and confidently, knowing that automated testing and monitoring are in place to safeguard the process. Ensuring Security and Compliance Embracing Multi-Cloud DevOps demands a . Every cloud harbors unique strengths and weaknesses. Conducting in-depth risk assessments and adopting a robust, all-encompassing security framework is vital. Strengthen defenses with encrypted channels, ironclad authentication, and regular audits. security-first approach Centralizing Identity and Access Management (IAM) grants control and proper access privileges. Unified IAM simplifies provisioning and minimizes unauthorized entry with strong passwords and multi-factor authentication. Amidst strict regulations, data protection reigns supreme in multi-cloud landscapes. Complying with GDPR and CCPA requires understanding collected data and employing anonymization, encryption, and retention policies. Disaster Recovery and Business Continuity Diversifying cloud providers brings a smart edge, enhancing redundancy and fortitude. Distributing data and apps across multiple clouds safeguards against total system failure if one provider faces issues. Match unique cloud capabilities to your precise needs for optimal results. This fosters business resilience, safeguarding against unforeseen challenges. Disaster recovery is paramount; cross-cloud replication spreads data and apps across platforms. If one cloud falters, swift switching to an alternate ensures minimal downtime and data loss. Mitigate reliance on a single provider, keeping business smooth during emergencies. Effectiveness lies in testing; regularly evaluating recovery procedures through simulated scenarios. Identify weaknesses and refine plans. Embrace technological evolution, updating recovery strategies to stay prepared and resilient. Cost Optimization and Governance Achieving a triumphant multi-cloud deployment hinges on astute cost management. Launch your journey by analyzing cloud services, and spotting top-value providers for specific tasks. The right blend of cloud partners guarantees cost optimization without compromising on performance and reliability. Forge Financial Clarity: Implement cost allocation and budget controls in the multi-cloud realm. Assign project-specific budgets, monitor spending in real-time, and leverage cloud tools to unearth cost-saving opportunities swiftly. Mastery through Governance: Vital governance policies propel efficient multi-cloud management. From resource provisioning to access controls, establish clear and enforceable guidelines, fostering consistency across platforms. Review and adapt these policies to meet ever-changing business needs and industry standards. Conclusion Enterprises unlock boundless possibilities with Multi-Cloud DevOps—a gateway to agility, scale, and competitiveness. By mastering intricacies, provider selection, automation, and security, they thrive in multi-cloud complexity. Fueled by a robust strategy and relentless growth, innovation soars, creating unparalleled customer experiences in a dynamic tech realm.

Multi-Cloud DevOps: Strategies for Seamless Cross-Platform Deployment

Automation is the best answer to controlling cloud costs, especially if you work with modern solutions like containers and Kubernetes. But automated optimization solutions come in different forms: they focus on automating different cost-saving tactics and give you various levels of visibility and control. Here’s a comparison of two automation solutions available to teams that work with Kubernetes: CAST AI and Spot.io. Keep on reading to find out which one supports your use cases better. – Full-Service Cloud-Native Platform CAST AI – Automated Spot Instance Use Spot.io Created by industry veterans, CAST AI is a comprehensive cloud automation platform for environments. Companies across e-commerce and ad tech are using CAST AI to . Automation features include spot instances, autoscaling up & down, and AI VM type selection. optimizing Kubernetes save from 50% to even 90% on their cloud bills Developed by NetApp, is a cloud cost management solution that focuses on optimizing cloud infrastructure costs with spot instance automation. Using machine learning and analytics algorithms, helps teams to get spot capacity for their workloads automatically, cutting costs and ensuring high availability. Spot.io Spot.io CAST AI vs. – Quick Feature Comparison Spot.io Feature CAST AI 🥇 Spot.io Supported platforms AWS ✅ ✅ Google Cloud ✅ ✅ Microsoft Azure ✅ ✅ Cost visibility Detailed cluster cost breakdown ✅ ✅ Automated cost forecasting ✅ ✖ Cost view across multi-cloud ✅ ✖ Real-time alerting functionality ✖ (coming soon) ✅ Kubernetes cost optimization and automation Rightsizing Recommendations, for instance, types and sizes ✅ ✅ Automated rightsizing via AI-driven instance selection ✅ ✖ Multi-shape cluster construction ✅ ✖ Autoscaling ✅ ✅ Automated pod scaling parameters ✅ ✅ Horizontal pod autoscaling ✅ ✅ Node autoscaling ✅ ✅ Cluster scheduling ✅ ✅ Automatic bin packing ✅ ✅ Spot Instance automation ✅ ✅ Full multi-cloud optimization ✅ ✖ Detailed Feature Comparison of and CAST AI Spot.io Cost visibility and monitoring Cost optimization and automation Spot instance automation Full multi-cloud optimization Security Pricing 1. Cost visibility and monitoring Cluster cost breakdown & forecasting In , you get a cost breakdown at the project, cluster, namespace, and deployment level. Teams can analyze costs down to individual microservices and then generate a detailed forecast of their cluster costs for better planning. CAST AI uses universal metrics that work with all supported cloud service providers. CAST AI The platform also comes with a cost allocation feature that works per cluster and per node. A feature for ongoing cloud cost reporting is also in the near-future pipeline since the CAST AI team plans to expand cost dimensions beyond compute – such as control plane, network, egress, storage, and others. breaks down the infrastructure costs of clusters and offers insights into each of the layers. Costs are broken down into namespaces and individual workloads within every namespace, with the option to filter them according to your container labels and annotations. For each workload, Spot.io displays compute and storage costs. Spot.io Users can use this data to analyze the application costs, perform chargebacks without extensive resource tagging, and estimate future cloud spend. Cost View Across Multi-cloud Many companies use more than one cloud service today. Allocating costs in a multi-cloud context is challenging, but equips teams with insights for addressing that. Its full multi-cloud functionality comes with visibility via universal metrics from Grafana and Kibana that can be used for any supported cloud service providers. CAST AI currently doesn’t support multi-cloud functionality or visibility across its solutions for containerized applications. Spot.io 2. Cost Optimization and Automation Rightsizing uses AI to choose the best instance types and sizes that meet an application’s requirements but still help teams save cloud costs. Whenever a cluster needs extra nodes, the AI-driven instance selection algorithm selects instances that achieve maximum performance at a minimum cost. Your team doesn’t have to lift a finger because it all happens automatically. CAST AI The platform also comes with multi-shape cluster construction, since choosing the same instance shape for every node in a cluster may easily lead to overprovisioning. That’s why CAST AI delivers an optimized mix of different instance types adapted to an application’s requirements. offers a handy Right-Sizing recommendation mechanism. The tool monitors workload utilization in real-time to provide you with recommendations for adjusting the resource requirements of workloads when they start consuming significantly more or fewer resources than requested. Spot.io The solution provides recommendations per container and summarizes them for the entire workload to enable easier visualization at a high level and faster implementation. Autoscaling Horizontal Pod Autoscaling and Node Autoscaling automates pod scaling parameters to help teams avoid overprovisioning their infrastructures. The platform also includes a Horizontal Pod Autoscaler, which uses business metrics to generate the optimal number of required pod instances. The feature then scales the replica count of your pods up and down, scaling to zero and removing all pods if there’s no work to be done. CAST AI CAST AI also ensures that the number of nodes in use matches the application’s requirements at all times, scaling nodes up and down automatically. continuously checks for unschedulable pods, and if it finds one, it scales up to make sure that all the pods have a place to run. The solution also removes nodes automatically when all pods running on the node can be moved to other nodes in the cluster. To optimize costs, Spot.io prioritizes downscaling the least utilized nodes. Spot.io Cluster Scheduling and Termination automatically pauses and resumes clusters created within the platform to help teams avoid paying for resources they’re not using. CAST AI makes sure that all that pods and nodes are terminated in a case of scale-down or instance replacement. Spot.io Automatic Bin Packing Kubernetes presents a cost challenge to teams because it distributes applications within a cluster evenly, with no attention paid to how cost-effective this setup is. changes the default pod scheduling behavior and uses automated bin-packing to achieve maximum savings in line with your settings. Fewer nodes translate to lower costs. CAST AI uses bin-packing algorithms as well. When the tool identifies instances where workloads may be distributed across the cluster, it triggers a scale-down to drain and terminate the instance. Spot.io 3. Spot Instance Automation Spot Instances can result in dramatic savings of up to 90% off the On-Demand pricing. But there’s a catch – the cloud provider can pull the plug anytime. That’s why automation is so critical for effective spot instance use. makes sure that the replacement of spot instances that were interrupted is fully automated. This means that teams don’t have to worry about their workloads not finding a place to run. The platform is always on the lookout for better instance alternatives and provisions in a split-second to guarantee high availability. CAST AI allows users to run their clusters on spot instances without the need to provision or scale instances. The tool also takes care of bin packing containers on spot instances to optimize their use even further. Spot.io 4. Full Multi-Cloud Optimization As more and more companies use multiple cloud services to access best-in-class services and prevent disasters, the need to monitor, manage, and optimize costs across providers is greater than ever. CAST AI meets this need by offering you the option to set up multi-cloud clusters using a number of : multi-cloud features – CAST AI replicates applications and data across multiple cloud services. If one fails, others keep your workloads afloat, guaranteeing business continuity. Active-Active Multi-Cloud – the platform distributes traffic evenly across all the participating clouds, always choosing the endpoints that are up and healthy. Global Server Load Balancing – CAST AI makes cost allocation across cloud services easier thanks to universal metrics from Grafana and Kibana. Multi-cloud visibility doesn’t support the multi-cloud functionality at the moment. Spot.io 5. Security Since both automation solutions work directly with the cloud infrastructure, their security is essential. Created by cybersecurity experts, offers a number of security features such as encryption at rest/in transit, secrets management, network security, logging, visibility, and more. The platform also comes with automatic patching and upgrades to VMs and Kubernetes to eliminate the chance of errors in your clusters. Additionally, CAST AI doesn’t access any environment variables that are considered sensitive such as secrets, passwords, tokens, etc. CAST AI is ISO-certified and well underway to obtaining SOC 2 certification. CAST AI encrypts account data within the browser and re-encrypts it with a secure algorithm when it reaches its servers. The tool doesn’t store any private keys, passwords, or authentication tokens – authentication is based on the IAM Cross-Account Role & External ID. Spot.io 6. Pricing offers a free Savings report you can run to check whether you could save up on your cloud bill. The analyzes your setup and generates actionable recommendations. You can implement them manually or turn automated cost optimization features – in that case; you can choose between two plans: Growth and Enterprise. CAST AI read-only agent CAST AI offers guaranteed savings of at least 50%. offers a free trial, after which users can choose from two different pricing models: Pay-as-you-Go without the annual commitment and Subscription that comes with a commitment but opens the doors to more discounts and priority support. Spot.io Summary Both Spot.io and CAST AI are great automation solutions for reducing cloud costs and facilitating processes like cost monitoring, management, and optimization. Using spot instances is a smart method for reducing the cloud bill, but it’s just part of the picture. By – for example, ones purchased within an AWS Savings Plan – you can drive your cloud bill down even more. rightsizing other instances While Spot.io offers recommendations, CAST AI does the job for you via automated instance selection. The broad range of optimization features combined with automated cost forecasting and unique multi-cloud functionality brings CAST AI to the top of cloud cost optimization platforms. To accurately estimate the savings that you would be able to get – start with the free AI Cluster Analyzer.

CAST AI vs. Spot.io: Two Approaches to Automated Cloud Cost Optimization

Bonus: Does your workload qualify for AWS spot instances? [CHEAT SHEET] You may already know what the catch is. The cloud provider can pull the plug at any time with as little as 30-second notice. We’re not saying that you should opt to reserve VM instances instead. Far from it. Reserved capacity is a path to vendor lock-in and paying more in the long term. There is a way how you can use spot instances effectively. Even for production workloads. Read this guide and learn and make financial team pleasantly surprised when they see the bill. how to handle spot instances This is part 3 of our cost optimization series. The rest of the series include: Surprised by your cloud bill? 5 common issues & how to deal with them How to choose the best VM type for the job and save on your cloud bill Why are spot instances so tricky? Interruptions are inevitable CSPs offer their unused capacity at prices that offer savings up to 90%. The only catch is that they can pull the plug with short notice, from 2 minutes to as little as 30 seconds. This is why spot instances are more difficult to manage for production workloads. Since you bid on spare computing resources, you have no guarantee on how long these capacities will stay available. Interruptions are bound to happen. That’s why you shouldn’t be using them for workloads that can’t tolerate them and are critical. Pulling the plug happens fast CSPs offer short interrupt notice. Amazon gives you 2 minutes, Azure and Google only 30 seconds. Is that enough time to drop everything and find a replacement for your instance? Not for a human. Let’s say that you already set your eyes on an on-demand instance. Creating a new VM takes around 5 minutes on AWS (and even longer if you use Kubernetes), so you’re looking at a few minutes of potential downtime. Another method is having some paused machines that can step in whenever you lose an instance. But then your savings aren’t going to be so spectacular. The best way to handle spot instance interruptions is through automation. Note: Rebalance recommendation In November 2020, AWS introduced a new feature that you can use to proactively rebalance workloads running on EC2 Spot instances without having to wait until your instance receives the interruption notice. It’s a signal that notifies you when the risk of interruption increases for a spot instance that you’re using. It arrives sooner than the interruption notice, giving you time to rebalance your workload to new or existing instances. Limited capacity The amount of available capacity sold as spot instances can vary a lot based on size, region, time of day, and many other factors. And all of them are subject to frequent changes. The availability of a spot instance is based on supply & demand. This might lead to unexpected behavior if you happen to pick the most popular instance types and a market surge like Black Friday occurs. So, why use spot instances at all? Some of your workloads probably don’t need on-demand machines at all times. Tech companies like Salesforce, Lyft, or AutoDesk use spot instances. If you’re still harboring some doubts, consider this scenario: Let’s say that you have 10 pods running for your application — a product catalog service. Half of the pods are running on a spot instance. At some point, the instance gives you a preemption notice — it’s about to be taken away from you. If that happens, you’ll lose half of your capacity. You’re not going to . Instead, the pods will be redistributed to other machines that are still available after the interruption. experience downtime immediately But what if you want to handle the interruption gracefully and replace that capacity before it becomes an issue? You can quickly order a new instance within the allotted time — for example, a different type of spot instance. Or go with an on-demand instance if there’s no spot instance capacity on the marketplace. And you can replace that on-demand instance with a spot instance a couple of hours later when the market pressures are alleviated. By not locking yourself up in a reserved plan, you get a lot of flexibility and avoid getting locked in with your vendor (or even a specific instance type). That’s why using spot instances is a good idea. When to use spot instances? If a service is stateless and can be scaled out — that is, have more than one replica — is a . good candidate for spot instances The good news is that most services are stateless in modern architectures. K8s was designed for stateless architectures. Here are some examples of workloads that work well in spot instances: — they’re fault-tolerant and instance-flexible. Batch processing jobs — they’re typically self-contained, highly available, fault-tolerant, and capable of handling interruptions. Containers and microservices — these apps usually need very high compute capabilities, massive amounts of memory, fast storage, and high network performance. Spot instances can support them via bursting or even serve as primary compute infrastructure. High Performance Computing (HPC) — it doesn’t matter what tools you use; these instances can come in handy in your deployment process. CI/CD operations — Elasticsearch or MongoDB can handle an interruption without losing any data or affecting the service. Distributed databases Any app on an orchestrated environment Which CSP to choose for spot instances? Do this before getting a spot instance 1. Know your workload How aggressive are you going to be about implementing spot instances? Before getting into the spot instance business, you need to know how much time your application needs to finish a job. Can it handle interruptions well? Will you have an automation tool in place to move your workload to another instance before your time runs out? 2. Cherry-pick your instances Next, it’s time to examine what the CSP has to offer. Take a look around and consider going for slightly less popular instances. They might come with a lower chance of interruptions and run stable for a longer time. When looking through the available instances, be sure to check the frequency of interruption. Frequency of interruption is the rate at which the instance reclaimed capacity during the trailing month. AWS displays it in the Spot Instance Advisor in ranges of <5%, 5–10%,10–15%,15–20% and >20%: 3. You can still use spot instances for more important workloads For example, AWS offers a type of spot instance where you get uninterrupted time guarantee for up to 6 hours (in hourly increments) and pay just a little more. A spot instance running for a predefined duration can achieve a discount of up to 30–50% compared to on-demand pricing. 4. Bid your price Now it’s time to set the maximum price you’re willing to pay for the spot instance. Your spot instance will run only when the marketplace price matches your bid or is lower. The rule of thumb here is using maximum price that equals on-demand price. If you set a custom amount and the price goes up, you risk getting interrupted. 5. Manage spot instances in groups When using groups of spot instances, you can request multiple instance types at the same time. As a result, you increase your chances of getting filled. Another perk is that you can set a maximum price per hour for the entire fleet rather than a given spot pool (a group of instances with the same type, OS, availability zone and network platform. — you can manage a large fleet of spot instances with different allocation strategies (for example, considering the lowest price or only capacity optimized types). AWS Spot Fleet — use this feature to create and manage a group of load-balanced VMs, increasing or decreasing their number automatically. Azure VM scale set — you can bring preemptible instances together in a group after specifying the preemptible option in the instance template. Google managed instance group But to make it all work, prepare for a massive number of manual configuration, setup, and maintenance tasks. 6. Turn to automation You can avoid downtime from lost instances by implementing automation tools for managing your cloud infrastructure via autoscaling methods. By using an automation tool, you can pick how much of your workload will be running on a spot instance, and then automatically fall back to on-demand instances in case of interruptions. Automation is here to make sure that your workload has a place to run. And thanks to features like AWS Rebalance events, you can mitigate the risk even before receiving the interrupt notice. You can get away with adding some basic levels of automation to how you manage these instances. But to achieve the best results, you need a solution that carries out automated actions based on predictive analytics. Here’s what automating spot instances can do for you Remember the case study we shared with you in the article about ? choosing the best VM for the job To test our cost savings approach at CAST AI, we opted for an open-source from Google. e-commerce demo app that we adapted We first prepared the app: We load-tested our application with ~1k concurrent users. We scaled the pods for every microservice accordingly (using AWS EKS with a statically-scaled deployment). To capture the metrics, we ran the test for 30–60 minutes. We then extrapolated the costs over a 30-day period (we took assumed traffic seasonality into account). To make it happen, we generated a likely 30-day usage pattern using a Python script. The example usage experienced spikes every day at around the same time and had several days of a week with heavier traffic. This is how we calculated on the AWS test instance. the monthly costs of running the demo app Our initial monthly cost of running the app was . $691.20 We applied ensuring that our application saved on costs relative to the on-demand pricing. the CAST AI spot instance policy We used the most aggressive policy settings where all the instances in use are spot instances. This brought the total compute costs down to $65.01 — saving 90% over the original costs of an unoptimized deployment. Wrap up Spot instances are a beast that can be tamed. But to reap the pricing benefits and use them safely in production, you will need to use an automation solution like CAST AI . Previously published at https://cast.ai/blog/how-to-reduce-cloud-costs-by-90-spot-instances-and-how-to-use-them/

All You Need to Know About Spot Instances to Cut Down Your Cloud Service Expenses

One of the primary motivations for using the cloud are flexibility and cost saving. The problem is that without ongoing management and oversight, the cloud budget can grow to dimensions that eliminate profitability. A recent study on the cost of using the cloud has shown that companies pay an average of on cloud services than they really should. A total of is being wasted on cloud services that are not in use. 35% more over $60 billion In this article, I will share my conclusions from recent cloud cost optimization processes and review a number of ways and tools to significantly reduce cloud costs 30–50%, without sacrificing performance. The recommendations in this article are valid for all cloud vendors unless otherwise specified. Tools for managing cloud costs Before you start saving it is important to understand the cost composition. In each cloud, there are cost management tools that show different segmentation of costs by service type. These tools can automatically detect available servers and recommend that they are reduced or turned off. Multi-cloud: VMware CloudHealh Azure: , Cost advisor Cloudyn AWS: , Trusted advisor Cost management GCP: Cost management Part 1 — Server costs Usually, servers are the largest component of the overall cost, so we will start with a number of server cost-saving strategies, then look at how you can lower your storage and network expenses and finally review some business options to obtain significant discounts on cloud spending. Turn off servers Estimated discount — 15% of total server cost Turn off servers that are not being used. Sizing — Adjusts the size of the server to the amount and usage load. Restrict user permissions to create new servers. Tools for managing cloud costs will help to find which servers should be minimized. Reserved Instances Estimated discount — 50% of the cost of long-term servers Reserved Instances (RI) can significantly reduce server costs, provided you commit to a long term contract (1–3 years). This program is not suitable for temporary servers that we may not need in the future. Usually, a commitment for a year will give a 40% discount, while a commitment for three years will provide a 60% discount. If you still want to stop using a server you have committed to, there are several ways to get out of the RI commitment: Replace the commitment with a different type of server Cancel the RI and pay an exit fine From a calculation I made for Azure, it is more profitable to commit to 3 years with 60% discount and cancel after a year with a 12% penalty than to commit to 40% per year (per server size). https://azure.microsoft.com/en-us/pricing/details/virtual-machine-scale-sets https://aws.amazon.com/ec2/pricing/reserved-instances/pricing/ https://cloud.google.com/compute/pricing Spot Instances Estimated discount — 70% of server costs that are not “mission critical” Spot servers or low priority servers are 70–90% cheaper than regular servers of the same power! Furthermore, today the discount is fixed (and not by the auction process as practiced in the past). The catch is that these servers are low priority so you run the risk that they will suddenly close without notice. Fortunately, there are several ways to deal with this unfortunate state of affairs: Running stateless services that are not critical; if they close, you can always turn them on again (the disc is not deleted). Working with queues; if a server closes, the task will remain in a queue and wait for another server. Use auto scaling rules to automatically handle a particular instance count if a spot server is closed. A company called helps reduce costs by effectively using Spot. They are able to identify spot servers that are about to close and replace them with other spot servers with virtually no downtime. From an inquiry I made, it seems that their Azure support is still incomplete (Azure managed k8s service is not fully supported), AWS has better support. Spotinst Serverless \ Autoscaling Architectures Estimated discount — 90% of the cost of servers that are constantly open to load times Our systems have to withstand heavy loads, but there is no reason to keep all the servers working during periods of low demand. Autoscaling ensures that the number of servers will automatically scale according to the measured load. Serverless functions run only as needed without the need for any servers. Proper architecture is one of the essential elements of an efficient and effective cloud but is beyond the scope of this article. For server architecture and cloud design patterns, in general, see . awesome-design-patterns In systems managed by Kubernetes, the allocation of resources is usually more efficient and cost-effective. The project can connect Kubernetes to serverless containers platforms such as AWS Fargate and Azure Container Instances. virtual-kubelet Dev \ Test Estimated discount — 50% of the cost of low-altitude servers A large portion of our servers belong to dev, test and pre-prod environments. In Azure, there are (I do not know of a parallel program in other clouds). discounts on dev/test environments In many cases we work on dev/test environment during the day only, so servers can be shut down at night and weekends, saving over 50% of the cost. There are tools to shut servers down automatically according to schedule: Skeddly Parkmycloud Parlmycloud — Servers turn on only at the green slots. Part 2 — Storage and Network Storage Estimated discount — 20% of storage costs Here are the top 5 cloud storage categories ordered from low cost to expensive (from slow to fast): Archive storage-eg AWS Glacier Object storage-eg S3 \ Blob File storage — Network libraries that can be mapped to multiple servers Block storage-discs SSD Database storage-eg SQL, MongoDB … Within each storage category, there are several price levels depending on speed and redundancy. Raw data can be stored in a cheap storage category but metadata, used for queries, is best kept in an expensive storage category. Archive storage is the cheapest form of storage but is not practical for ongoing work due to slow retrieval times. Object storage is the next cheapest storage category. Therefore this is the preferred storage choice for the majority of the data. You should set up a “Storage Lifecycle” policy that allows you to set rules for automatically moving old files to cheaper storage categories. (Available in AWS and found in preview in Azure) Network Estimated discount — 20% of the cost of network traffic The network traffic consists of internal traffic (within your network) and external traffic between the server and the customers. For internal traffic I recommend: Allowing processes to operate within the same geographical area if possible. Using only internal addresses. For understanding ways to reduce external traffic I talked with John Graham-Cumming , Cloudflare’s CTO, who pointed out Cloudflare’s networking costs benefits * : DDOS protection — DDOS can incur networking costs. CDN — files are downloaded from Cloudflare servers closest to the client location (with no additional network costs). —promising cheaper cloud networking costs when transferring data between members of the alliance. Bandwidth alliance — runs javascript request logic at a Cloudflare’s endpoint before the request reaches my network. Cloudflare workers compression- better than gzip. Brotli *This does not imply Cloudflare is the only vendor in this domain. Part 3 — Business discounts Cloud providers and their partners have many programs that can offer significant discounts. Large customers can also bargain directly with cloud providers for discounts. Even if the whole system is on one cloud, sometimes, in specific areas it is useful to use external providers for services like CDN, DNS or even another cloud in order to lower costs. Cloud partners Estimated discount — 5–10% off the total cost + consultation There is no substitute for consultation from an experienced cloud architect. All the major cloud providers have partners that provide consulting services. Working with them can provide a number of benefits. For example: Discounts on the total cost of the cloud Support for malfunctions Architectural consulting Tools for cost management Flexible payment terms https://partner.microsoft.com/ https://aws.amazon.com/partners/ https://cloud.google.com/partners/ Startup programs Estimated discount — credit that can reach tens of thousands of dollars or more All cloud companies have plans that can benefit start-ups. The benefits can include: credit for cloud usage consulting services business promotion/accelerator https://startups.microsoft.com/en-us/ https://aws.amazon.com/startups/ https://cloud.google.com/developers/startups/ Free tier programs Estimated discount — first year free for small servers All cloud providers have free tier plans. These programs allow free use of the lowest cost services for a year or for a fixed number of uses. There is usually no limit to the number of free tier accounts that can be created. https://azure.microsoft.com/en-us/free https://aws.amazon.com/free/ https://cloud.google.com/free/ * The topics mentioned above reflect my professional opinion only. I have no affiliation with any of the services mentioned in the article.

CLOUD SPOT

cloudspot.io

1-10 employees

Since 2015

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CLOUD SPOT

cloudspot.io

1-10 employees

Since 2015

COMPANY RANKING

CLOUD SPOT

EVERGREEN INDEX #2782

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