Usage-based Pricing Research Articles

Leveraging AI to Revolutionize Subscription Business Models

This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.

Should the Internet of Things platform enter the smart device market?

PurposeThe Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms.Design/methodology/approachIn this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements.FindingsOur outcome shows that introducing an IoT platform’s smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platform’s own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platform’s resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platform’s entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases.Originality/valueThese findings hold significant practical implications for firms operating in an IoT-based supply chain.

Networking in Cloud Computing: Embracing Contests and Seizing Prospects

Cloud computing presents the concept of utility computing, which allows users to access computing, storage, and networking resources as needed, with a usage-based pricing model. However, consumers have limited control over network resources, and cloud-computing providers confront a number of issues when operating infrastructure as a service (IaaS) environments. This research investigates the networking difficulties and federation challenges inherent in IaaS, as well as unique software-defined networking (SDN) concepts that could provide efficient solutions for future deployments.

SaaSC: Toward Pay-as-You-Go Mode for Software Service Transactions Based on Blockchain's Smart Legal Contracts

Usage-based pricing or Pay-as-You-Go is a relatively new SaaS business model that may provide customers the option to pay for only what they use. Yet, it is more challenging to implement than traditional Pay-before-Use subscriptions considering that it need not only realize financial payment on consumption-based behaviors, but also regulate the rights and obligations among the providers, consumers, and platforms in a legal form. To address these challenges, in this paper Smart Legal Contract (SLC) is integrated into a service computing platform by introducing a new architecture, called Service as a Smart Contract (SaaSC). On the aspect of service legalization, through combining SaaS and SaaSC, we establish three kinds of terms in SLC-based software subscription contract to support service's registration, discovery and customization, so that a complete transaction procedure can be regulated in terms of service states, transaction process, and interactions among contracting parties. On the aspect of service financialization, we propose a new scheme of service interface declaration in the SLC-based SPESC contract. By automatically executing smart contracts and checking the terms, the pay-as-you-go billing form can be made fine-grained payment after using service interface calls. Moreover, we take weather forecast service as a case to implement and analyze the acquisition, delivery, and contractual payment of software service on Blockchain smart contract. The experimental results demonstrate the feasibility and effectiveness of the proposed SaaS+SaaSC architecture so that it provides a practicable approach for contractual software service.

A Game-Theoretic Analysis of Joint Mobile Edge Caching and Peer Content Sharing

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mobile edge caching</i> allows edge devices to cache popular contents and deliver them to end-users directly, and hence can effectively alleviate the increasingly heavy backbone loads and improve the quality-of-service of end-users. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Peer content sharing</i> enables edge devices to share the cached contents with each other, and can further increase the caching capability and efficiency. While lots of research efforts have been made to edge caching or content sharing, it remains largely open to devise a joint caching and sharing framework and study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint framework for mobile edge caching and peer content sharing, and focus on studying the strategic behaviors and interactions of edge devices in the joint framework. Specifically, we model their interactions as a non-cooperative game, where each edge device ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">player</i> ) can choose to be an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agent</i> (who caches and shares contents with others) or a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">requester</i> (who doesn't cache but requests contents from other devices) of each content. We characterize the game equilibrium under a generic usage-based pricing scheme (for content sharing) and analyze its existence and uniqueness systematically. We further design a best response based iterative learning algorithm for players to update their behaviors and reach the equilibrium in a self-enforcing manner. Moreover, we analyze the Price of Anarchy (PoA) of the game equilibrium to measure the system performance degradation due to the selfish strategic behaviors of game players, based on which we further design a pricing scheme (for content sharing) to reduce such performance degradation. Simulations show that our framework can reduce the total system cost by up to 60%, comparing with the benchmark pure caching system without peer content sharing.

RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network.

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.

Delay-Aware Content Delivery With Deep Reinforcement Learning in Internet of Vehicles

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

Usage-based road pricing and potential equity issues: A study of commuters in South East Queensland, Australia

Usage-based pricing has gained significant traction in recent times with respect to addressing transport challenges. Since such pricing is still in its infancy, at least in the context of urban transport, it is important to understand its implications and, to be specific, any potential equity issues. Based on a hypothetical usage-based pricing scheme together with 2009–2012 Household Travel Survey data of commuters residing in South East Queensland (SEQ), Australia, the results indicate that usage-based road pricing will lead to both horizontal and vertical inequity for some commuters. Most importantly, a greater percentage of these commuters are already transport and socio-economically disadvantaged. They will have no choice but to compromise their quality of life and bear addition financial burdens in the absence of feasible travel alternatives. To ensure that these social costs do not outweigh the benefits, improving the quality of urban transport systems and its integration with urban form and land use, will be required. Until this is achieved, policy makers should incorporate some form of spatial and/or temporal variability within usage-based road pricing in the studied, and similar, urban contexts.

Analysis of Information Service Pricing Scheme Model Based on Customer Self-Selection

This study attempts to analyze pricing schemes with monitoring cost and marginal cost for perfect substitute and quasi-linear utility functions for achieving Internet service Provider (ISP) in gaining benefit. Two types of customers analyzed, namely customers who are heterogeneous (both high-end and low-end) as well as heterogeneous customers (high-demand and low-demand) based on Flat-fee, usage-based, and two-part tariff are the three types of pricing methods employed. The results show that usage-based pricing schemes gain maximum profit optimal for heterogeneous customers (high-end and low-end), while for heterogeneous customers (high-demand and low-demand) type of pricing scheme two-part tariff obtains maximum profit optimal. The results of this study are more directed to the lemma of the perfect substitute utility function which compares the lemma of heterogeneous customers. This model was solved using LINGO 13.0 software and ISP to get maximum profit.

A Comprehensive Overview of Privacy and Data Security for Cloud Storage

People used to carry their documents about on CDs only a few years ago. Many people have recently turned to memory sticks. Cloud computing, in this case, refers to the capacity to access and edit data stored on remote servers from any Internet-connected platform. Cloud computing is a self-service Internet infrastructure that allows people to access computing resources at any location worldwide. The world has altered as a result of cloud computing. Cloud computing can be thought of as a new computing typology that can provide on-demand services at a low cost. By increasing the capacity and flexibility of data storage and providing scalable compute and processing power that fits the dynamic data requirements, cloud computing has aided the advancement of IT to higher heights. In the field of information technology, privacy and data security have long been a serious concern. It becomes more severe in the cloud computing environment because data is stored in multiple locations, often across the globe. Users' primary challenges regarding the cloud technology revolve around data security and privacy. We conduct a thorough assessment of the literature on data security and privacy issues, data encryption technologies, and related countermeasures in cloud storage systems in this study. Ubiquitous network connectivity, location-independent resource pooling, quick resource flexibility, usage-based pricing, and risk transference are all features of cloud computing.

Determinants of Commute Distance in South East Queensland, Australia: Implications for Usage-based Pricing in Lower-density Urban Settings

ABSTRACT Usage-based road charges are expected to reduce the use of private vehicles. To assess this assumption, this study investigates the factors currently influencing commute distance in South East Queensland (SEQ), Australia. A multiple regression model was estimated to identify the significant determinants of commute distance, followed by a hierarchical regression analysis to ascertain the contribution of personal characteristics, household characteristics, work-related characteristics and accessibility. We conclude that many commuters would remain captive private vehicle usersuntil accessibility improves, and other transport modes offer viable commuting options. Alternatively, the pricing scheme should be adapted to suit local contexts.

Transformative Tourism Breakthrough in Post Pandemic: An Enigma or Eclipse

The development of tourism in Indonesia had entered a critical period, especially during the uncertain COVID-19 pandemic, which affected the tourism industry both economically and socio-culturally. Workers and stakeholders suffered as the multiplier effect of the COVID-19 outbreak. As preparation for dealing with these conditions, the tourism industry strove to adapt with a transformative business model for its sustainability. In this context, the research proposed a transformative business model for the tourism industry so that stakeholders had an adaptive and innovative frame of mind post-pandemic. The model pinpointed on personalization, closed loop, asset sharing, usage-based pricing, collaborative ecosystem, and agility as the key features. This qualitative research used case study that aimed to explore major aspects of an inspirational business model from various industries that have survived over the decades. The results describe applicable business patterns as a contribution to the tourism industry. It is suggested that tourism stakeholders focus more on developing innovative business strategies post-pandemic.

Exploring business model innovation for competitive advantage: a lesson from an emerging market

Purpose Emerging markets are witnessing rapid changes in their economy owing to the ongoing liberalization and globalization. India, as one of the emerging markets in south Asia, is also experiencing a dramatic change in its business ecosystem. This poses huge opportunities to the companies, both start-ups and established ones. In this direction, the business model innovation offers a strategic renewal mechanism. The study aims to explore the practices of an online transport network company (OLA) creating a distinctive place for itself in Indian taxi service sector. Design/methodology/approach Methodologically, an exploratory case study of an India-based online transport company (OLA) business model innovation is reported. Findings This paper reveals that OLA has been able to gain competitive advantage in the Indian emerging market by developing an innovative business model with its distinctive features of personalised customer service, asset sharing, usage-based pricing, collaborative ecosystem, agile and adaptive organising and successful expansion strategies. Research limitations/implications This study adds to current knowledge concerning the theoretical foundations and antecedents of business model innovation as a competitive advantage. The paper is explorative in nature because the analysis is mostly based on literature review. Furthermore, in consideration of the analysis of business model of a single company, further research is required to generalize the results. Practical implications The understanding of the intricacies of business model innovation can be of great concern to existing and prospective managers and entrepreneurs of emerging markets. Originality/value The paper discusses the features of innovative business models and how firms can make their business models more relevant to the competitive markets. As such, the study is hopeful to aid practitioners engaged in the pursuit of beating the competition with innovation driven business models.

Practical estimation of cloud storage costs for clinical genomic data

BackgroundLaboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. MethodsWe developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. ResultsTwo factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. ConclusionsSteady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.

Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach.

Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits.

Mobile Internet Usage and Usage Based Pricing

Using data on mobile Internet usage of thousands of individuals, we provide some of the first analyses linking mobile usage to key demographics such as income. We find a reverse-U relationship between mobile Internet usage and income – notably different than the monotonically declining relationship found on home devices. This pattern suggests that data caps are particularly binding on low-income users. We then construct a simple model of mobile Internet usage that incorporates demand features suggested by our empirical finding and prior empirical findings on device adoption and usage. After abstracting away from cost and two-sided market considerations, we solve the model, and through comparative statics, identify demand conditions for which usage-based pricing (via a data cap) is, or is not, revenue enhancing. Key insights from this analysis are: 1) the tendency toward demand-driven price discrimination is hill-shaped (increasing then decreasing) in the number of low-income users and eventually declining in high-income users, and 2) a relatively high (low) proportion of total value attained from relatively low usage levels by low-income (high-income) users increases the tendency toward demand-driven price discrimination. Hence, to the extent that a market has a large number of low-income users and/or high-income users attain a higher proportion of their total value at the cap than low-income users, the use of caps by providers is more likely driven by cost (or other non-demand-side) considerations than by revenue enhancement. Lastly, additional analysis shows a largely monotonically increasing relationship between income and usage intensity (measured as page views in a session), suggesting that, ceteris paribus, price discrimination strategies may be more effective if tied to usage intensity rather than duration.

Steering Incentives of Platforms: Evidence from the Telecommunications Industry

We study the trade-offs faced by Internet Service Providers (ISPs) that serve as platforms through which consumers access both television and internet services. As online streaming video improves, these providers may respond by attempting to steer consumers away from streaming video toward their own TV services, or by attempting to capture surplus from this improved internet content. We augment the standard mixed bundling model to demonstrate the trade-offs the ISP faces when dealing with streaming video, and we show how these trade-offs change with the pricing options available to the ISP. Next, we use unique household-level panel data and the introduction of usage-based pricing (UBP) in a subset of markets to measure consumers' responses and to evaluate quantitatively the ISP's trade-offs. We find that the introduction of UBP led consumers to upgrade their internet service plans and lower overall internet usage. Our findings suggest that while steering consumers towards TV services is possible, it is likely costly for the ISP and therefore unlikely to be profitable. This is especially true if the ISP can offer rich pricing menus that allow it to capture some of the surplus generated by a better internet service. The results suggest that policies like UBP can increase ISPs' incentive to maintain open access to new internet content. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Subscription-Based Data-Sharing Model Using Blockchain and Data as a Service

In modern times, many individuals, businesses and the Internet of Things (IoT) integrated industries collect huge amounts of meaningful data daily, which may be beneficial for other individuals and businesses as well. By utilizing this data, future trends to make the right decisions on the bases of facts and figures are analyzed efficiently. In addition to that, many new ways are paved for researchers to utilize this data in their upcoming research. However, due to some major issues like security, privacy and access control of data, data owners avoid sharing data among themselves. Another main problem is the selfish behavior of data owners. Businesses also act selfishly and invest huge amounts of money to collect and maintain the data for their benefits. Therefore, data owners are hesitant to share their data with others without the availability of a fair profit and secure data-sharing platform. Moreover, consumers are not much motivated to buy data from Data Providers (DPs) due to its bad quality and inconsistency. The data provided by data owners is mostly incomplete, outdated, heterogeneous and costly. In this paper, a subscription-based data-sharing model is proposed by leveraging the blockchain technology and Data as a Service (DaaS) concept. In this model, users subscribe to a DP for a specific period to get access to the data and pay according to the subscription plan. The DP keeps receiving revenue recurrently for a long-time, which has a huge profit margin in comparison with selling data at once. Furthermore, two major pricing models, Flat Rate Pricing (FRP) and Usage-Based Pricing (UBP), are discussed to set standards for data owners to monetize their data, and a new hybrid pricing model is also proposed. Blockchain technology is utilized in the proposed model to make it secure, transparent and immutable. To investigate the performance of the proposed model, a private blockchain network is deployed using a web interface provided by MultiChain blockchain. The simulation results demonstrate that the proposed model is feasible and efficient. The theoretical discussion proves that the proposed model is beneficial for both data owners and data consumers and has a good scope in the future for data management and trading processes.

A measure based pricing framework for data products

It has been widely recognized that data can be viewed as a kind of assets. But accounting for data assets and pricing data transactions are still difficult due to the lack of reasonable measurements of datasets or data products. Literatures of data pricing mainly focus on traditional pricing models including models basing on contents of data, demand of market, data quality, etc.. However, due to the particularity of data, the above models may not coincide with the measure theory and thus suffer from some problems. For example, they do not consider how to price datasets sharing common contents; whether we should pay for a repeat purchase; and how to define peak-valley tariff formally for usage-based pricing. To tackle the above problems, in this paper, we formally define measure spaces for datasets and data products. Specifically, we introduce the measures on discrete, continuous and product data spaces respectivaly. Further we introduce the integral and propose a measure based pricing framework for data products. Our work is parallel to existing pricing models. We fouce on how to measure data, and pricing data is a natural extension by integrating the unit price function under the measure. In contrast, existing models focus on determining total prices directly by considering lots of factors like contents of data, demand of markets, etc. By doing analyses on several real-world applications and cases, we prove the effectiveness and generality of our proposal.

Practical Cost Analysis of Genomic Data in the Cloud

Abstract Background The growth of clinical NGS testing, together with new CAP checklist items stating that read-level (raw) NGS data must be retained for a minimum of 2 years, has prompted laboratories to reevaluate their NGS data storage solutions. In this context, cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; nonetheless, several perceived disadvantages, including increased complexity and security/regulatory considerations, have hindered adoption. Furthermore, despite the ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. Methods We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers and provides complex cost and usage forecasts over 10- to 20-year timeframes. Parameters include current test volumes (and customizable file sizes), expected test volume growth rate, data compression techniques, data retention times across storage tiers, and case reaccess rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Results The per-GB storage prices among major cloud providers have dropped ~6 to 7× over the past 10 years; in addition, there currently exists an up to 20× price difference between storage tiers. Lower prices are typically offset by either increased access/transfer costs or slower retrieval rates. Two factors were found to markedly affect the average cost per test: (1) total file size, including the use of NGS-specific file compression techniques, and (2) storage tiers and rapid transfer to “cold” or archival storage. In contrast, some factors were not found to affect total costs, including costs associated with reaccess from archival storage tiers, and discounts associated with increased volumes. When data from NGS testing were stored in “hot” storage for 1 year, then archived for 9 years, typical costs per exome were ~$3/test and genomes ranged from $40/test to $66/test, depending on compression algorithms used. Overall, reaccess of data from cold storage added 2% or less to the total cost per test (with an estimated reaccess rate of 10% of cases per year). Conclusions Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Taken together, laboratories should not be concerned about reaccess costs but rather should focus on data compression techniques and archival storage tiers for long-term data. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.