--- title: Confidence by Spotify Reviews meta\_title: 'Confidence by Spotify Reviews 2026: Details, Pricing, & Features | G2' meta\_description: Filter 55 reviews by the users' company size, role or industry to find out how Confidence by Spotify works for a business like yours. aggregate\_rating: rating\_value: 4.6 review\_count: 55 scale: '5' date\_modified: '2026-08-07' parent\_category: name: Conversion Rate Optimization Tools url: https://www.g2.com/categories/conversion-rate-optimization-tools --- # Confidence by Spotify Reviews & Product Details Visit Website Confidence is an experimentation and feature flagging platform developed by Spotify, designed to assist software and product teams in conducting experiments and making data-informed decisions at scale. Leveraging over a decade of experience in A/B testing infrastructure, Confidence provides a robust framework for teams looking to optimize their product features and user experiences through systematic experimentation. The platform is particularly beneficial for development teams, product managers, and data analysts who are keen on enhancing their decision-making processes through empirical evidence. By facilitating A/B testing and gradual rollouts, Confidence allows teams to test new features and modifications in a controlled manner, ensuring that changes are based on real user feedback rather than assumptions. This approach not only minimizes risks associated with feature launches but also fosters a culture of continuous improvement within organizations. Key features of Confidence include its advanced feature flagging capabilities, which enable teams to control user experiences remotely without the need for code deployments. This flexibility allows for rapid iterations and adjustments based on user interactions and feedback. Additionally, the platform's data warehouse-native architecture ensures seamless integration with existing data systems, providing teams with comprehensive insights into user behavior and engagement metrics. Built on OpenFeature, Confidence offers a vendor-agnostic solution for feature flagging, eliminating concerns of vendor lock-in and allowing teams to maintain control over their experimentation processes. The platform's feature flags are available at no cost, making it accessible for any team interested in adopting a data-driven approach to experimentation. By utilizing Confidence, teams can enhance their ability to innovate and respond to user needs effectively, ultimately leading to improved product outcomes and user satisfaction. The combination of powerful experimentation tools and a commitment to data integrity positions Confidence as a valuable resource for organizations aiming to elevate their product development strategies. * * * Product Website Confidence by Spotify Seller [Spotify](https://www.g2.com/sellers/spotify) Discussions [Confidence by Spotify Community](https://www.g2.com/products/confidence-by-spotify/discuss) Languages Supported German, English, French, Swedish Solution Type All-in-One Overview by Donna Hanafi Show More ## Pricing Pricing provided by Confidence by Spotify. ### Free Free Per Month [ View More Pricing Information ](https://www.g2.com/products/confidence-by-spotify/pricing) ## Confidence by Spotify Integrations (7) What do users say about integrations? 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KB Kishore B. Product Manager Mid-Market (51-1000 emp.) 7/21/2026 Business partner of the seller or seller's competitor, not included in G2 scores. "Confident A/B Test Decisions with Guardrails and Exposure Metrics" 4.5/5 What do you like best about Confidence by Spotify? 1. We can be sure that if there is significant result, then we can release the ab test variant winner without any worry 2. Like the usage of having guardrail metrics to be safe. 3. Ability to have exposure metrics helps me track only the result you need. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? 1. It is very difficult to get significant result with less number of trigger such as premium purchases. I think it would be good to modify algorithm a bit to avoid requiring too many users to be part of ab test to get significant results. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? 1. Helps us analyse which variant to roll out with significancy 2. Gives us flexibility to have different variants and also keep the variant for users even in case of pausing ab test with help of sticky assignments. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Anup M.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Anup M.") AM Anup M. Engineering Manager Mid-Market (51-1000 emp.) 7/24/2026 "First-Class, Server-Side Experiment Assignment with Deterministic Bucketing" 4/5 What do you like best about Confidence by Spotify? Honestly, the thing I'd rank first is that it treats experiment assignment as a first-class, server-side concern rather than a client-side afterthought. The resolver/flag model means you can do assignment in backend services with deterministic bucketing, which matters a lot for the kind of experiments you run — payment retry schedules aren't something you can toggle in a frontend SDK. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? Debuggability of the exposure pipeline is the big one. When assignments and exposures disagree — exactly your ABExp03 situation — the platform tells you that there's a sample ratio mismatch but gives you very little tooling to diagnose why. You end up manually reconciling backend assignment logs against exposure events in the warehouse, which is tedious and error-prone. A built-in assignment-vs-exposure reconciliation view would save days of investigation. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Trustworthy server-side experimentation. We run experiments on backend billing logic — currently a payment retry schedule test — where assignment has to happen in services, not the client. Confidence gives us deterministic server-side assignment with proper flag management, so treatment logic lives where the billing decisions actually happen. Review collected by and hosted on G2.com. Show More Validated ReviewerIncentivizedSource: Seller invite ![Verified User in Financial Services](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Financial Services") CF Verified User in Financial Services Mid-Market (51-1000 emp.) 7/23/2026 "Standardizes A/B test process, good tool. Biggest opportunity: easier to make mental model" 4/5 What do you like best about Confidence by Spotify? The platform builds on our own data warehouse, which pushes us to build proper data products and manage the data upstream, rather than sending separate datasets into the platform. Statistics are handled consistently across tests, instead of relying on individual Python notebooks and hands-on analysis by a data scientist, which can easily create bottlenecks and lead to low reproducibility. Overall, the platform encourages our data organization and teams to formalize A/B testing processes. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? The abstractions and entities in the ecosystem make it challenging to build a personal mental model of the whole system. One of the selling points is that it makes A/B testing easier by removing some details and adding fail-safes to the process, but in doing so it introduces a new layer of abstraction. If the platform is meant to be easy to learn and to onboard new teams onto, why is the bootcamp so long? The UI/UX is pretty good in terms of visual design and UI components, but it’s lacking when it comes to giving users a strong overview. It suffers from some similar challenges to Google Tag Manager, for example: it’s easy to inspect individual Metrics, Tests, and Fact Tables, but hard to get the big picture. It’s also difficult to see connections at a glance, or to edit, rename, or archive things in bulk. On top of that, it’s not always clear what metadata exists around what’s being used by which team, and it’s hard to establish and maintain consistent naming syntax across teams. Finally, the mix of very clear SQL tables that you own yourself with harder-to-grasp calculations layered on top—plus Confidence entities created in the GUI such as Fact Tables, Dimension Tables, Metrics, etc.—can sometimes lead to a semi-black-box feeling about what’s really going on. This can be improved through clearer mental models and some trial-and-error, but there’s probably a lot of potential to make it more transparent directly in the GUI, and through more visual, example-driven documentation. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? A platform for A/B testing that uses feature flags and data products from many sources that we control. The alternative would be to build something similar ourselves or use a similar tool. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Verified User in Computer Software](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Computer Software") UC Verified User in Computer Software Mid-Market (51-1000 emp.) 7/22/2026 "Statistically Sophisticated Testing with Seamless BigQuery Metrics" 4.5/5 What do you like best about Confidence by Spotify? The tool is more statistically sophisticated than the previous tool we were using (firebase). It can calculate the sample sizes required to detect metric movements, and it also lets us add multiple metrics, either as success metrics or as guardrails. Because the metrics are derived from our bigquery events, it’s seamless to do follow-up investigations. After designing the test, we can largely rely on the red/green status and don’t have to manually check the results each time. The UI/UX does a good job of capturing the hypothesis and the UI variants, and the dashboards load fast. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? For someone without a statistical background, understanding some of the technical terms can be difficult. More in-context explanations of NIM, metric variance, and similar concepts would be helpful. In some places there are multiple legend labels- like “Has deteriorated, has not deteriorated, powered, significant or not” - which can be confusing for non-technical product folks. We also tend to run the same test across different geos. Right now we have to duplicate tests for each geo and then check the results individually. On top of that, the metrics in these tests aren’t arranged consistently, which makes comparisons harder. It would be much better if related tests could be grouped together. The UI has a few small issues as well. For example, adding an owner filter doesn’t show results, but adding owner plus status does show results. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? It has made testing much easier and has also helped evangelise a stronger testing culture across the company. In our team it has helped iterate with multiple ideas to improve a metric and get confident results to take decisions. By keeping both success and guardrail metrics we can move the discussion to consider trade-offs instead of arguing about a single metric. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Verified User in Banking](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Banking") UB Verified User in Banking Mid-Market (51-1000 emp.) 7/21/2026 "Easy, Statistically Sound A/B Testing—But Click-Ops Slows Bulk Changes" 3.5/5 What do you like best about Confidence by Spotify? Confidence makes it quite easy to set up A/B tests and analyse them properly from an statistical standpoint. It makes sure that I don't accidentally make bad statistical choices. This is done by design of the system. It is easy for non-analytical teams to setup an A/B test. They need to create a flag in their customer facing interface, then almost everything can be handled from Confidence afterwards. It handles the randomness and analysis. This alleviates quite a bit of effort from me as a data scientist. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? Confidence is built as a click-ops tool. This makes it difficult to perform actions in bulk For example when one needs to mass change metric names or source, one has to click their way through all metrics that use the source. This click-ops interface also makes it difficult to perform meta-analyses of AB-tests. Say I want to estimate how much we can affect a certain metric in experiments, then I need to manually find all uses of the metric and then manually extract the results to perform the meta analysis. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? We use Confidence for AB-tests and rollouts. Each team should be able to autonomously launch experiments and rollouts with as little support from a central team as possible. Confidence helps the autonomous teams to do experiments themselves and still draw correct statistical conclusions without having to be data analysts. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Verified User in Telecommunications](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Telecommunications") UT Verified User in Telecommunications Mid-Market (51-1000 emp.) 7/21/2026 "Easy Experimentation with speed and rigor" 4.5/5 What do you like best about Confidence by Spotify? What stands out is how easy it is to run experiments while retaining full user-level traceability for deeper investigation when needed. The segmentation capabilities are powerful, the UI/UX is clean and intuitive, and the platform integrates well with a range of systems. Performance is top-notch, and the team behind it is genuinely engaged — responsive to requests, with a smooth onboarding experience and great support throughout. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? Honestly, nothing to dislike. Anything we've flagged has been more in the spirit of product feedback — we share it continuously, and the team genuinely works on it. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Confidence solves the core challenge of running trustworthy A/B experiments at scale. Before, setting up experiments, tracking exposures, and interpreting results reliably required significant manual effort and statistical expertise. Confidence streamlines the entire workflow — from flag-based rollouts and exposure tracking to automated statistical analysis of success and guardrail metrics — so we can make ship/no-ship decisions with confidence rather than guesswork. The biggest benefit is speed and rigor at the same time: teams can launch experiments quickly, monitor guardrails (like performance or stability regressions) in real time, and get statistically sound readouts without needing a statistician in the loop for every test. The full traceability down to user-level exposures also means we can always dig deeper when a result looks surprising. Overall, it has raised the quality of our decision-making while lowering the barrier to experimentation. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Verified User in Automotive](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Automotive") AA Verified User in Automotive Mid-Market (51-1000 emp.) 7/21/2026 "Intuitive, Warehouse-Native Experimentation That Speeds Decisions" 4/5 What do you like best about Confidence by Spotify? What I like best is how intuitive the platform is to work with day to day — setting up and reading experiments doesn't require wrestling with the tooling, so the barrier to launching a test is low across the team. The biggest win for us is that Confidence connects directly to our data warehouse, which means results land fast and we can make decisions without waiting on a separate data pull or manual pipeline. On top of that, the sequential testing approach is a real step up from traditional fixed-horizon A/B testing: we can monitor experiments as data accumulates and call them as soon as there's a clear signal, rather than committing to a fixed sample size upfront. That combination — intuitive UX, warehouse-native data, and sequential methodology — has meaningfully shortened our time from hypothesis to decision. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? The main friction points for us are documentation and metric flexibility. The docs are thin in places, so figuring out how to do something less common often means trial and error or reaching out rather than just looking it up. The bigger limitation is that we can't fully customise how metrics are calculated — things like percentiles or retention rate aren't well supported natively, so we end up computing those outside Confidence and bringing them back in. That extra step adds work and makes some analyses clunkier than they should be. Better documentation and more flexible custom metric definitions would close most of the gap for us. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Before Confidence, we essentially weren't running experiments — shipping decisions were made on intuition and whatever analysis someone could pull together ad hoc. Confidence solved the tooling and process gap that was keeping us from testing in the first place. We've gone from effectively zero A/B tests to running experiments as a standard part of shipping, and the bigger shift is cultural: it's helped us build an experiment-first mindset, where the default question before we ship something is "how do we test this?" rather than "does this feel right?" Having warehouse-connected results and a low-friction setup is what made that scale possible — testing stopped being a special effort and became the normal way we work. The benefit isn't just more tests; it's that more of our decisions are now grounded in evidence rather than assumption. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Verified User in Online Media](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Online Media") UO Verified User in Online Media Enterprise (\> 1000 emp.) 7/20/2026 "An intuitive experimentation platform that makes data-driven decisions easier" 4/5 What do you like best about Confidence by Spotify? What I like most about Confidence is how intuitive and easy it is to use. It gives product teams a high level of autonomy by centralizing all the key KPIs and metrics in a single place, making them accessible and understandable for everyone involved. The platform also provides a very clear view of experiments, their associated metrics, and, most importantly, the results. It makes interpreting A/B test outcomes much easier, allowing teams to quickly understand whether a change has had a meaningful impact. Over the past year, the product has evolved significantly with many valuable new features. Each improvement has made it easier to launch, monitor, and analyze experiments, helping teams make faster and more informed decisions based on data. Ultimately, Confidence has become an essential tool for building better products through a strong experimentation culture. Finally, I'd like to highlight the incredible support from the Spotify team. Whenever we've encountered an issue or had questions, they've always been quick to respond and incredibly helpful. Their responsiveness and commitment to improving the product have made a real difference in our experience using Confidence. A big thank you to the team for all their support!. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? One area that could be improved is metric organization. As the number of metrics grows, it becomes increasingly difficult to navigate and find the right one. Having better ways to organize, group, or archive metrics—and reducing the need to duplicate them for simple changes such as category updates—would make the platform much easier to maintain. As a Data Analyst, I would also appreciate more flexibility when creating and exploring metrics. Today, the metric creation process is very product-oriented, which is great for governance, but there are cases where being able to write a simple SQL query or define an ad hoc metric would be much faster and more efficient for analysis. Providing advanced users with this kind of flexibility, while keeping the existing workflow for standard metrics, would make the platform even more powerful. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Confidence has helped us build a much stronger experimentation culture by making A/B testing accessible to both Product Managers and Data Analysts. It centralizes our KPIs, metrics, and experiment results in a single platform, ensuring everyone works from the same source of truth. Before using Confidence, analyzing experiments required much more manual work and it was often difficult for non-analytical stakeholders to interpret the results. Confidence simplifies this process by presenting clear experiment outputs and statistical insights, making it much easier for teams to understand the impact of a feature and make informed decisions. The platform has also significantly improved the way we launch and monitor experiments. With its continuous improvements and new features, we can iterate faster, spend less time on operational tasks, and focus more on generating insights. Ultimately, this allows us to make better product decisions based on reliable data and build better experiences for our users. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: In-app ![Verified User in Information Technology and Services](/assets/icons/anonymous-avatar-purple-4ae1032bdb50ee5682003170c8184aee790d25958bd397abbd384ba52c596a7b.svg "Verified User in Information Technology and Services") UI Verified User in Information Technology and Services Mid-Market (51-1000 emp.) 7/20/2026 "Statistically Rigorous A/B Testing with Strong SDKs and BigQuery Integration" 4/5 What do you like best about Confidence by Spotify? - The statistical rigor is the biggest win; specifically built in sample sizing, sequential analysis & health checks like SRM detection make our tests far more trustworthy than our previous AB testing tools. - SDK coverage is equally strong: client side and server side SDKs let us run FE and BE experiments through one platform. - The UI guides teams through a coherent flag → metrics → results workflow, which makes the right experimental setup the easy path even for non-experts. - Integration with our data warehouse is a standout; metrics are defined on fact tables built from our own BigQuery data, so results are computed on data we already trust rather than a vendor's black-box tracking. - Really appreciate the updates/ iterations in documentation and support the team offers. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? - Some UI workflows fight you: the sample-size calculator requires selecting a flag filter, which creates a chicken & egg for brand new flags with no assignment history (you end up borrowing a comparable flag's traffic as a workaround). - And because a power calculation isn't mandatory to launch, it's easy for teams to skip it entirely. - The fact table model is powerful but rigid: once metrics are built on a table, you can't edit its columns from the UI, so a wrong entity/grain choice early on means rebuilding tables and re-reviewing PRs. Changing the randomisation entity after the fact is similarly painful. - There's little built in guidance or automation to catch design flaws (wrong exposure filter, underpowered primary) before launch; that burden still falls on experimentation experts. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Before Confidence, experimentation at Truecaller was quite fragmented-feature flagging, exposure tracking, and results analysis lived in separate places, and statistical quality depended heavily on individual analysts. Many tests launched without proper power calculations, and ambiguous or underpowered results led to weeks of wasted runtime and debates over whether a flat result meant no effect or no sensitivity. Confidence solves this by unifying flag delivery, exposure tracking, and analysis in one platform, with the statistics (sequential analysis, sample sizing, SRM checks) built in rather than bolted on. Metrics defined on fact tables from our own warehouse mean results are computed on data we already trust. The benefit is faster, more defensible ship/no-ship decisions across a 450M+ user base, a shared experimentation workflow that scales beyond a handful of experts & fewer uninterpretable tests which directly translates to less wasted engineering time and safer rollouts of sensitive features. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ![Mikael A.](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Mikael A.") MA Mikael A. Senior Product Manager Mid-Market (51-1000 emp.) 7/22/2026 "Simple, Clean UI That Makes High-Volume Testing Easy" 5/5 What do you like best about Confidence by Spotify? Simplicity and clean ui makes it easy to work with tests in volume. It was easy to integrate with our existing data warehouse and any client integrations was also easy. Pricing was superior. Great onboarding. Review collected by and hosted on G2.com. What do you dislike about Confidence by Spotify? The product is opinionated on how you should be running good tests which is good but sometimes these opinions aren't always clear and you realize to late like for example why success metrics will have an alpha penalty when you have multiple. It makes sense but it is not very clear. Review collected by and hosted on G2.com. What problems is Confidence by Spotify solving and how is that benefiting you? Confidence has solved doing experiments at scale and fast pace using datawarehouse metrics for us. We are doing more and better experiments. Review collected by and hosted on G2.com. Show More Current UserValidated ReviewerIncentivizedSource: Seller invite ## Pricing Options Pricing provided by Confidence by Spotify. ### Free Free Per Month ### Growth Starting at $449.00 Per Month ### Pay-as-you-go credit packages Free Trial With One-Time Purchase [ View More Pricing Information ](https://www.g2.com/products/confidence-by-spotify/pricing) ##### ##### Confidence by Spotify Features Functionality Feature Testing Analytics Reporting and Analytics [ View More Features ](https://www.g2.com/products/confidence-by-spotify/features) ## Top-Rated Alternatives [ ![PostHog](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "PostHog") PostHog 4.5/5(1,054) ](https://www.g2.com/products/posthog/reviews) [ ![VWO Testing](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "VWO Testing") VWO Testing 4.4/5(929) ](https://www.g2.com/products/wingify-vwo-testing/reviews) [ ![LaunchDarkly](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "LaunchDarkly") LaunchDarkly 4.5/5(812) ](https://www.g2.com/products/launchdarkly/reviews) [ View All Alternatives ](https://www.g2.com/products/confidence-by-spotify/competitors/alternatives) ##### Categories on G2 [A/B Testing](https://www.g2.com/categories/a-b-testing-tools)[Feature Management](https://www.g2.com/categories/feature-management) ##### Explore More [What's the best marketing analytics software for executives who need fast-loading dashboards without waiting on a data team?](https://www.g2.com/discussions/what-s-the-best-marketing-analytics-software-for-executives-who-need-fast-loading-dashboards-without-waiting-on-a-data-team)[PuTTY vs MobaXTerm vs SecureCRT for IT teams comparing terminal emulators across Windows, Linux, and cross-platform deployment scenarios: which handles the widest range without switching tools?](https://www.g2.com/discussions/putty-vs-mobaxterm-vs-securecrt-for-it-teams-comparing-terminal-emulators-across-windows-linux-and-cross-platform-deployment-scenarios-which-handles-the-widest-range-without-switching-tools)[What is the top-rated translation management system for enterprises?](https://www.g2.com/discussions/what-is-the-top-rated-translation-management-system-for-enterprises) [Which healthcare integration engines offer native FHIR support and automated data translation for legacy EHRs?](https://www.g2.com/discussions/which-healthcare-integration-engines-offer-native-fhir-support-and-automated-data-translation-for-legacy-ehrs)[What decision making software works best for a 51-200 person company that needs to reduce manual overhead without buying something built for a 5,000-person enterprise?](https://www.g2.com/discussions/what-decision-making-software-works-best-for-a-51-200-person-company-that-needs-to-reduce-manual-overhead-without-buying-something-built-for-a-5-000-person-enterprise)[What is the top-rated IT alerting software for enterprises?](https://www.g2.com/discussions/what-is-the-top-rated-it-alerting-software-for-enterprises) [Show MoreShow Less](javascript:void(0);)