Amazon CloudWatch vs. ScienceLogic AI Platform

If you use any AWS services, CloudWatch is the natural choice to monitor & troubleshoot your workload. Thankfully, for most AWS services, CloudWatch is either built-in or very easy to set up. However, being proficient in browsing & tracking the log events would take some training & practice. Having some experienced people on the team would help immensely, especially in spreading the skill to the rest of the team.

Incentivized

Appropriate if you are setting up a monitoring suite in new Infrastructure Environment. Definitely NOT suited for Migration Projects. ScienceLogic SL1 cannot cater to a lot of monitoring requirements which already would have been configured in old monitoring suite. Plus, limited support for customizations and having to go to "Feature Requests" route makes in extremely complicated.

Incentivized

• It provides lot many out of the box dashboard to observe the health and usage of your cloud deployments. Few examples are CPU usage, Disk read/write, Network in/out etc.
• It is possible to stream CloudWatch log data to Amazon Elasticsearch to process them almost real time.
• If you have setup your code pipeline and wants to see the status, CloudWatch really helps. It can trigger lambda function when certain cloudWatch event happens and lambda can store the data to S3 or Athena which Quicksight can represent.

Incentivized

• 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

• Memory metrics on EC2 are not available on CloudWatch. Depending on workloads if we need visibility on memory metrics we use Solarwinds Orion with the agent installed. For scalable workloads, this involves customization of images being used.
• Visualization out of the box. But this can easily be addressed with other solutions such as Grafana.
• By design, this is only used for AWS workloads so depending on your environment cannot be used as an all in one solution for your monitoring.

Incentivized

• Creating powerpacks from scratch for new devices may be straightforward but will rarely be easy. Rewarding when completed, but not easy.
• Developer documentation needs a rethink. While the information may be there (it isn't always) it is not easy to find. This is not helped by using different terms for the same things.
• A developer console/dashboard for monitoring data collection from powerpacks instances without having to switch webpages or have to monitor multiple webpages.

Incentivized

We migrated away from our 20-year-old homegrown solution and have no back-tracking capability. ScienceLogic is demonstrating new capabilities that we would not have been able to do on our own using our legacy system.
We understand the capabilities of competitors based on our bake-off selection where ScienceLogic won on capabilities and future near-term potential (expandability, platform growth). We know that those competitors are not really close to where we have been able to push ScienceLogic (as a partner).

Incentivized

Although the tool itself is easy to integrate and is readily available for use, it has its limitations. The key limitations of cloudwatch are with respect to cost incurred on log retention and log querying. While for key use cases this is sufficient, for more advanced use cases, Amazon CloudWatch doesn't work out. Also, obviously it is tightly coupled with AWS, which makes you look away if you need a single tool for all monitoring

Incentivized

We use ScienceLogic SL1 in our organization to serve effective monitoring solutions to our external customers. Our customers depend upon us for critical events/alerts related to their IT infrastructure gears and using SL1, we're able to provide them with a proactive monitoring solution that resolves an issue before an impact is noticed by the customer. There are very few monitoring solutions that can cater to a variety of Cloud platforms like Public Cloud (AWS, Azure) and private cloud simultaneously and SL1 addresses this business problem very well

Incentivized

Science Logic SL1 provides the option of Distributed deployment where multiple instances of each appliance can be deployed to manage the load and availability. SL1 provides a High Availability feature for Database Servers and Data Collection. If one of the Data Collectors in the collector group fails, it will automatically redistribute the devices from the failed Data Collector among the other Data Collectors in the Collector Group. The high availability feature for the Database server ensures that SL1 performs failover automatically to another server without causing the outage to the application.

Incentivized

The performance is entirely dependent on the complexity of the environment/network being used to host the platform. Outside of those factors, the platform runs very efficiently and quickly out of the box. We have integrations with other platforms and neither seem to take a hit from our moderate API usage. Any issues with performance would be experienced by choices made in infrastructure or complexity of things built by the customer to display in the GUI (overly complicated and cluttered dashboards for example)

Incentivized

Support is effective, and we were able to get any problems that we couldn't get solved through community discussion forums solved for us by the AWS support team. For example, we were assisted in one instance where we were not sure about the best metrics to use in order to optimize an auto-scaling group on EC2. The support team was able to look at our metrics and give a useful recommendation on which metrics to use.

Incentivized

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

When I joined our company, I did not know about the in person training at firts. Logging onto the SL University, I realised that there were different sessions being held at different times throughout the year. The training itself was good, but being in a different time zone, made it difficult to attend, but the sessions that I attended was great!

Incentivized

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

Along with the purchase of the solution, we purchased a statement of work with their Professional Services organization to meet our outcomes and fill our critical gaps. The PS team was outstanding, very professional and allowed us to screen share while they built our integrations. In many cases they would teach us how they did certain things within the platform.

Incentivized

We use Cloudwatch for simpler monitoring, but these metrics and logs often feed into bigger ecosystems across our organization. The metrics and logs in Cloudwatch allow our developers quick and easy access to the data they need whilst easily integrating the same data into more prominent platforms for wider analysis, including Service desk support, SecOps, and ITOps monitoring within the organization.

Incentivized

Both of the tools we used before were with agents, so there was a need to configure the agent initially, before it could be used. Sometimes, there were issues with the service or the configuration of the agent, and deep troubleshooting was necessary to find the mistake in configuration before the communication with the endpoint was restored. Once the tools were running, they enabled very smooth reporting of what is running and what not, and the performance impact was lover than with WINRM monitoring.

Incentivized

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

• Positive for alarms and alert notifications once configured/customized.
• Has upfront learning curve, and cost can increase as does the alarm activity and monitoring details you may require.
• Cost-effective for any size organization keeping with AWS and utilizing its native tools is a savings in long-term ROI.

Incentivized

• Identified Major Incidents at least 50% of times when it happens
• Prevented Major Incidents due to alerting for basic IT Infra level issues at least more than 60% scenarios
• Provides ability to monitor large variety of IT Infra level devices and services