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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM Cloudant

    Score7.4 out of 10
    N/ACloudant is an open source non-relational, distributed database service that requires zero-configuration. It's based on the Apache-backed CouchDB project and the creator of the open source BigCouch project. Cloudant's service provides integrated data management, search, and analytics engine designed for web applications. Cloudant scales your database on the CouchDB framework and provides hosting, administrative tools, analytics and commercial support for CouchDB and BigCouch. Cloudant is often…

    $1

    per month per GB of storage above the included 20 GB

    ScienceLogic AI Platform

    Score8.2 out of 10
    Enterprise companies (1,001+ employees)
    ScienceLogic provides a unified IT Operations platform designed to manage operational workflows using high-fidelity Telemetry Data and explainable automation. The ScienceLogic AI Platform is deployable across On-Premises, Cloud, and Hybrid Environments. The platform consolidates monitoring tools to provide Observability and enables engineers to automate manual processes using Machine Learning capabilities. The platform utilizes automation for Closed-Loop Remediation and provides insights…N/A
    Pricing
    IBM CloudantScienceLogic AI Platform
    Editions & Modules
    Standard
    $1
    per month per GB of storage above the included 20 GB
    Standard
    $75
    per month 100 reads/second ; 50 writes/second ; 5 global queries/second
    Lite
    Free
    20 reads/second ; 10 writes/second ; 5 global queries / second ; 1 GB of storage capacity
    Standard
    Included
    per month 20 GB of storage
    No answers on this topic
    Offerings
    Pricing Offerings
    IBM CloudantScienceLogic AI Platform
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    YesYes
    Entry-level Setup FeeNo setup feeRequired
    Additional Details—ScienceLogic SL1 offers four tiers: SL1 Advanced – Application Health, Automated Troubleshooting and Remediation Workflows SL1 Base – Infrastructure Monitoring, Topology & Event Correlation SL1 Premium – AI/ML-driven Analytics, Low-Code Automated Workflow Authoring SL1 Standard – Infrastructure Monitoring – with Agents, Business Services, Incident Automation, CMDB Synchronization, Behavioral Correlation
    More Pricing Information
    Community Pulse
    IBM CloudantScienceLogic AI Platform
    Considered Both Products
    IBM
    No answer on this topic
    ScienceLogic
    No answer on this topic
    Key User Insights
    Would buy again
    86%
    Would buy again
    6 Answers
    87%
    Would buy again
    81 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    5 Answers
    91%
    Delivers good value for the price
    71 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    7 Answers
    92%
    Happy with the feature set
    86 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    7 Answers
    85%
    Lived up to sales and marketing promises
    44 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    7 Answers
    81%
    Implementation went as expected
    58 Answers
    Features
    IBM CloudantScienceLogic AI Platform
    NoSQL Databases
    Comparison of NoSQL Databases features of IBM Cloudant and ScienceLogic AI Platform
    Feature
    IBM Cloudant
    9.1
    21 Ratings
    6% above category average
    ScienceLogic AI Platform
    -
    Ratings
    Performance9.721 Ratings00 Ratings
    Availability8.321 Ratings00 Ratings
    Concurrency9.821 Ratings00 Ratings
    Security8.221 Ratings00 Ratings
    Scalability9.021 Ratings00 Ratings
    Data model flexibility9.821 Ratings00 Ratings
    Deployment model flexibility9.021 Ratings00 Ratings
    AIOps Features
    Comparison of AIOps Features features of IBM Cloudant and ScienceLogic AI Platform
    Feature
    IBM Cloudant
    -
    Ratings
    ScienceLogic AI Platform
    7.6
    26 Ratings
    0% below category average
    Monitoring and Alerting00 Ratings8.225 Ratings
    Performance Analytics00 Ratings7.826 Ratings
    Incident Management00 Ratings6.626 Ratings
    Service Desk Integration00 Ratings7.525 Ratings
    Root Cause Analysis00 Ratings7.721 Ratings
    Capacity Planning Tool00 Ratings7.222 Ratings
    Configuration and Change Management00 Ratings7.723 Ratings
    Automated Remediation00 Ratings7.620 Ratings
    Collaboration and Communication00 Ratings7.920 Ratings
    Threat Intelligence00 Ratings7.319 Ratings
    Best Alternatives
    IBM CloudantScienceLogic AI Platform
    Small Businesses
    RavenDB
    Score8.1 out of 10
    No answers on this topic
    Medium-sized Companies
    Amazon DynamoDB
    Score7.9 out of 10
    No answers on this topic
    Enterprises
    Amazon DynamoDB
    Score7.9 out of 10
    ignio AIOps
    Score8.1 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM CloudantScienceLogic AI Platform
    Likelihood to Recommend
    7.0
    (45 ratings)
    8.0
    (228 ratings)
    Likelihood to Renew
    7.3
    (1 ratings)
    8.5
    (24 ratings)
    Usability
    7.7
    (5 ratings)
    9.6
    (15 ratings)
    Availability
    8.2
    (1 ratings)
    9.2
    (14 ratings)
    Performance
    8.2
    (1 ratings)
    8.2
    (14 ratings)
    Support Rating
    8.6
    (4 ratings)
    6.6
    (20 ratings)
    In-Person Training
    -
    (0 ratings)
    8.8
    (6 ratings)
    Online Training
    7.3
    (2 ratings)
    7.7
    (8 ratings)
    Implementation Rating
    8.2
    (4 ratings)
    6.4
    (99 ratings)
    Configurability
    8.5
    (3 ratings)
    10.0
    (7 ratings)
    Ease of integration
    -
    (0 ratings)
    8.0
    (15 ratings)
    Product Scalability
    9.6
    (23 ratings)
    8.0
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    9.1
    (7 ratings)
    Vendor pre-sale
    9.1
    (1 ratings)
    8.3
    (7 ratings)
    User Testimonials
    IBM CloudantScienceLogic AI Platform
    Likelihood to Recommend
    IBM
    Our organization found Cloudant most suitable if One, a fixed pricing structure would make the most sense, for example in a situation where the project Cloudant is being used in makes its revenue in procurement or fixed retainer — thus the predictability of costs is paramount; Two, where you need to frequently edit the data and/or share access to the query engine to non-engineers — this is where the GUI shines.
    Incentivized
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    ScienceLogic
    For Windows, the issue is in higher resource consumption related to WinRM monitoring, which provides better options then the SNMP monitoring, which on the other hand is less resource intensive. The problem is also with support for OS with other than English language.
    Incentivized
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    Pros
    IBM
    • For us, performance and scalability is the key, and Cloudant DB backed by CouchDB is scalable and performant.
    • IBM Cloudant dB is very easy to provision for sandbox, development, QA as well as production.
    • Support for Java for CouchDB app server analytics enables a greater control for over developers.
    • Schema free oriented very easy to program and build applications on it.
    • We love it!!
    Incentivized
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    ScienceLogic
    • Best overall coverage of montioring different technologies.
    • Easy to use in any environment
    • Customizable being able to generate your own reports, dashboards, DA's, RBA's, etc.
    • Have very good out of the box integrations with other monitoring solutions such as ServiceNow
    • Always improving and regularly releasing new versions and upgrades to the system/DA's.
    • Interactive community
    Incentivized
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    Cons
    IBM
    • It was only after we went with the cloud-based solution that IBM rolled out an on-premise version.
    • We found that a 3rd-party ODBC driver was required for a few applications that needed to pull data out of Cloudant.
    • The sales process was difficult because the salesperson we used was not as versed on Cloudant as I had hoped.
    Incentivized
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    ScienceLogic
    • Dashboards are quite old and are of Iron age. Need to have AP2 dashboards only instead of AP1 and consistent new design across all functionalities.
    • Reporting is not improved since Y2020 and need to revamp completely. Need to integrate Dashboards and Reporting. PowerBI Like functionality to be given OOTB. Reports should be extracted in Excel, PDF, HTML and should be heavily automated.
    • Create and Open APIs for basic and advanced monitoring data extraction.
    • Topology based Event Correlation and Suppression should be improved drastically. Need to identify critical network interfaces based on Topology and monitor them. Basic customization of Dynamic App and/or Powerpack to exclude/include certain metrics/events to be permitted OOTB instead of customizations.
    • Integration with ServiceNow to be improved and to be taken to next level. Automation Powerpack should be made available OOTB as part of base product and to be priced attractively.
    • Take product to next level where we can monitor actual impacted IT or Business Service instead of metrics and events BSM and Topology map to be auto discovered and identify the network dependencies and alternate paths automatically instead of manual creation of BSM.
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    Likelihood to Renew
    IBM
    the flexibility of NoSQL allow us to modify and upgrade our apps very fast and in a convenient way. Having the solution hosted by IBM is also giving us the chance to focus on features and the improvement of our apps. It's one thing less to be worried about
    Incentivized
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    ScienceLogic
    It is simply because of all the best possible autonomy solutions it is providing and getting better day by day. Using AI and Devops along with handy automation, The monitoring and Management of devices becomes much easier and the way it is growing in all the aspects is one the best reasons too. Evolution of the SL1 platform in the autonomy monitoring and management is quite appreciable.
    Incentivized
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    Usability
    IBM
    It's mostly just a straight forward API to a data store. I knock one off for the full text search thing, but I don't need it much anyways. Also, the dashboard UI they give is pretty nice to use. It provides syntax-highlighting for writing views and queries are easy to test. I wish other DBs had a UI like this.
    Incentivized
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    ScienceLogic
    The core functions are there.
    The complexity is due to the complexity of the space.
    The score is based on comfort (I no longer notice the legacy UI) and the promise that I see in the 8.12 Unified UI (a vast improvement).
    It is also based on the fact that with 8.12, you can now do everything in the new UI but you still have the legacy UI as a fallback (which should now be unnecessary for new installations)
    Incentivized
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    Reliability and Availability
    IBM
    it is a highly available solution in the IBM cloud portfolio and hence we have never had any issues with the data base being available - we also do continuous replication to be on the safer side just in case some thing goes awry. We also perform twice a year disaster recovery tests.
    Incentivized
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    ScienceLogic
    SL is always there and online when you need to get info from it. The only times when SL was not available in our own data center, was when network links from out side of the data center was down and those links were not in our controll. Having a central database and people accessing it all over the world, may put a bit of constarin on the performance of the dashboards when reports gets generated, but that is far and few n between.
    Incentivized
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    Performance
    IBM
    very easy to get started and is very developer friendly given that it uses couchDB analytics. It is a cloud based solution and hence there is no hardware investment in a server and staging the server to get started and the associated delays/bureaucracy involved to get started. Good documentation is also available.
    Incentivized
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    ScienceLogic
    SceinceLogic SL1 architecture helps the platform to give a top-notch performance in every respect, Data collection to reporting happens very smoothly. With the new user interface pages load much faster. Individual appliances carrying the individual task ensure things are working without lag. Integration with ticketing tool(SNOW) is well managed by the ScienceLogic, no issue or much delay has been observed while interacting with an external tool.
    Incentivized
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    Support Rating
    IBM
    Very happy by the commitment given by the team which has been really good over the last 7 years of usage.
    Incentivized
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    ScienceLogic
    So far, it's good as part of my overall experience, except for a couple of use cases. The support team is well knowledgeable, has technical sound, and is efficient. When support escalates to engineering, the issue gets stuck and takes months to resolve.
    Incentivized
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    In-Person Training
    IBM
    No answers on this topic
    ScienceLogic
    It was good, Do the online training first and understand it and you will get the most out of the in-person training that way. This also takes you to an advanced level which is very good and the training as been overhauled once again along with new product coming in such as Zebruim / Skylar, worth going through again if it a while back that you first did this.
    Incentivized
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    Online Training
    IBM
    online resources are good enough to understand but there is nothing like testing. In our case, we discovered some not documented behavior that we take in count now. Also, the experience in NodeJs is critical. Also, take in count that most of the "good practices" with cloudant are not in online courses but in blogs and pages from independent developers
    Incentivized
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    ScienceLogic
    There are a lot of educational materials and courses on the SL1 training site (Litmos university). However the recording quality is sometimes not very good - screen resolution is low. There is a lack of professional rather than user-oriented documents and there are mistakes in documentation and education is not well structured.
    Incentivized
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    Implementation Rating
    IBM
    • Test the architecture on CouchDB helped us to address initial design flaws.
    • The migration to Cloudant as such was very painless.
    • We have migrate our replication system to Cloudant Android Sync for mobile devices.
    • We have regular informal contact with the Cloudant leadership to discuss our use cases and implementation strategies.
    Incentivized
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    ScienceLogic
    Implementation is smooth if we are to just support the out-of-the-box features available in ScienceLogic. For any custom requirement, having to go to SL1 Professional Services is the worst part of procuring this suite. And more often than not, SL1 Professional Services also ask to raise feature request. So, you subscribe to Professional Services to only hear back from them that "This feature is not supported and needs to have a separate feature request". At times frustrating.
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    Alternatives Considered
    IBM
    The feature-set, including security, is very comparable. Overall, IBM's services added to the product are mature and stable, although product support and engineers could be a little better. Global availability is improving, and Disaster Recover Capabilities are great. Overall, it's very comparable to MongoDB as a DBaaS offer, available globally and with great documentation.
    Incentivized
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    ScienceLogic
    Science logic SL1 is so user friendly and it's really easy to navigate between function. I would recommend Sciene logic SL1 to all of them who are looking for really useful monitoring tool and expecting easy way of managing it.
    Incentivized
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    Scalability
    IBM
    The service scales incredibly well. As you would expect from CloudDB and IBM combination. The only reason I wouldn't score it a 10 is the fact that document trees can get nested and nested very quickly if you are attempting to do very complex datasets. Which makes your code that much more complex to deal. Its very possible we could find a solution to this problem with better database planning to begin with, but one of the reasons we chose a service over a self-hosted solution was so we could set it up quick and forget about it. So we weren't going to dedicate a team to architecture optimization.
    Incentivized
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    ScienceLogic
    Our deployment model is vastly different from product expectations. Our global / internal monitoring foot print is 8 production stacks in dual data centers with 50% collection capacity allocated to each data center with minimal numbers of collection groups. General Collection is our default collection group. Special Collection is for monitoring our ASA and other hardware that cannot be polled by a large number of IP addresses, so this collection group is usually 2 collectors). Because most of our stacks are in different physical data centers, we cannot use the provided HA solution. We have to use the DR solution (DRBD + CNAMEs). We routinely test power in our data centers (yearly). Because we have to use DR, we have a hand-touch to flip nodes and change the DNS CNAME half of the times when there is an outage (by design). When the outage is planned, we do this ahead of the outage so that we don't care that the Secondary has dropped away from the Primary. Hopefully, we'll be able to find a way to meet our constraints and improve our resiliency and reduce our hand-touch in future releases. For now, this works for us and our complexity. (I hear that the HA option is sweet. I just can't consume that.)
    Incentivized
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    Return on Investment
    IBM
    • IBM Cloudant is very secure and we never have to worry about losing data/unauthorized access
    • It is one of the best data backup system and works well
    • Global availability means it is easy to connect to the nearest data center and this reduces load time which is great.
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    ScienceLogic
    • Once a powerpack is developed and configured for a device for one customer, it is easy to deploy the same powerpack on a second customer estate and configure specifically for that customer without having to reinvent the powerpack. This saves time and therefore money.
    • Once the customer estate tuning is complete, the Operations team have come trust the alerts. This is especially true when transient or self-correcting alerts are automatically cleared without ops team involvement, but a record is still available for audit and debugging purposes. This saves time and therefore money.
    • When setup correctly, it provides good visibility into applications, devices and whole customer estates. This saves time and therefore money when issues arise.
    Incentivized
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    ScreenShots

    ScienceLogic AI Platform Screenshots

    Screenshot of Unified Observability and Contextual IntelligenceScreenshot of Relationship Mapping & Service VisibilityScreenshot of ITSM and Collaboration IntegrationsScreenshot of Low-Code Workflow BuilderScreenshot of Dynamic Application DevelopmentScreenshot of Executive Overview Dashboard