Datadog vs. ScienceLogic AI Platform

Datadog works really well with complex microservices architecture like any E-commerce platform which will be having multiple services but they all are interdependent to others so in this scenario Datadog will be best to monitor these as it will show the transactions also between those microservices. If you are using multiple services in your architecture whether it will be cloud services or on prem services Datadog will be the best choice to monitor all those service with in Datadog so that you can see everything in a single place. But if you are having small architecture and few services in that then in that scenario you can use Datadog but it will be little costly as compared to other but obviously the features are very well.

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

• Create Dashboards as per application, environments, and Custom metrics in one panel.
• Log aggregation, one-stop Application monitoring tools for the whole infrastructure.
• Playbooks, SLA definition, success and error quotas, request visualizations.
• DB monitoring, Serverless stack monitoring.
• Alerting of Production incidents so we can quickly resolve the issues on time.

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

• Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
• I would appreciate more supportive examples for how to filter and view metrics in the explorer
• I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame

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

Definitely will not revisit after our issues and, in my opinion, poor support.

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

There is some room for improvement, but the Datadog team sends out updates frequently, and the UI is user-friendly for engineers, with no significant loading issues or region-specific problems. That was one of the key reasons we preferred Datadog; our company has employees worldwide, and it wasn't difficult to transition to the tool.

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

The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.

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

Documentation was difficult to work through, rollout was catastrophic (completely outage)

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

Datadog is a more complex but complete solution than any of the other Log Aggregation, monitoring, or general observabilty tools that we have trialed. I found it easier to setup following useful and up-to-date documentation provided directly by Datadog instead of scattered around many blogs or articles. I would love to have my own Grafana + Prometheus expert to setup all the peices we need but you're paying for expertise there instead of an experience with Datadog.

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

• Saved us (time & money) from developing our own monitoring utilities that would pale in comparison
• Alerts allow us to remedy issues before our customers even know about them
• Tracking resource usage over time allows us to better plan for future needs, before it becomes a pain-point.

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