LogicMonitor’s SaaS-based platform, LM Envision, enables observability across on-prem and multi-cloud environments. It provides IT and business teams operational visibility and predictability across their technologies and applications.
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Logstash
Score 8.0 out of 10
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Pricing
LogicMonitor
Logstash
Editions & Modules
Enterprise
Contact sales team
Website Monitoring
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Offerings
Pricing Offerings
LogicMonitor
Logstash
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Our platform is broken down into Pro and Enterprise Pricing. Pro includes monitoring for all of your cloud, hybrid, and on-premises infrastructure. Our Enterprise package includes all of this, plus our AIOps and Machine Learning functionality that provides dynamic thresholds, root cause analysis, anomaly detection and more!
LogicMonitor only charges by the device. What is considered a device? A device is anything with an IP address that you want to monitor, including a physical device or a cloud resource. This means multiple data sources under the same IP address can be monitored for the same price. Unlike some monitoring platforms. we don’t charge per node, interface, or metric.
Well Suited to: - MSPs with multiple clients - Network segmentation where a single device can't communicate to everything and collection devices are needed in each environment Not well suited for: - Management functions that would need to change settings on a device - Self contained environments with no outside network access
Perfect for projects where Elasticsearch makes sense: if you decide to employ ES in a project, then you will almost inevitably use LogStash, and you should anyways. Such projects would include: 1. Data Science (reading, recording or measure web-based Analytics, Metrics) 2. Web Scraping (which was one of our earlier projects involving LogStash) 3. Syslog-ng Management: While I did point out that it can be a bit of an electric boo-ga-loo in finding an errant configuration item, it is still worth it to implement Syslog-ng management via LogStash: being able to fine-tune your log messages and then pipe them to other sources, depending on the data being read in, is incredibly powerful, and I would say is exemplar of what modern Computer Science looks like: Less Specialization in mathematics, and more specialization in storing and recording data (i.e. Less Engineering, and more Design).
LogicMonitor is very customizable. We can build whatever modules we need, because it uses standard protocols like HTTPS, SNMP and WMI to gather data and metrics.
We like that LogicMonitor is an agentless solution for our use case. Not all customers will allow an agent-based approach to 3rd party tools.
LogicMonitor has thousands of out of the box modules, which work on their own and also act as good baselines for the ones that we will end up customizing more. We are rarely starting at zero when we decide to do something new with LogicMonitor.
LogicMonitor has great documentation, and support has been helpful in the instances where we've needed them.
Logstash design is definitely perfect for the use case of ELK. Logstash has "drivers" using which it can inject from virtually any source. This takes the headache from source to implement those "drivers" to store data to ES.
Logstash is fast, very fast. As per my observance, you don't need more than 1 or 2 servers for even big size projects.
Data in different shape, size, and formats? No worries, Logstash can handle it. It lets you write simple rules to programmatically take decisions real-time on data.
You can change your data on the fly! This is the CORE power of Logstash. The concept is similar to Kafka streams, the difference being the source and destination are application and ES respectively.
Since it's a Java product, JVM tuning must be done for handling high-load.
The persistent queue feature is nice, but I feel like most companies would want to use Kafka as a general storage location for persistent messages for all consumers to use. Using some pipeline of "Kafka input -> filter plugins -> Kafka output" seems like a good solution for data enrichment without needing to maintain a custom Kafka consumer to accomplish a similar feature.
I would like to see more documentation around creating a distributed Logstash cluster because I imagine for high ingestion use cases, that would be necessary.
This product has met virtually all of our needs. It was easy to implement and has been simple to support. Customization has been intuitive with many options available. They keep adding features and expanding available options. The future of LogicMonitor looks even better than it is today which is very promising. The management and support teams at LogicMonitor are always helpful
Set up is super easy. Just stand up a small Linux or Windows server to act as a collector. There are no agents to install on monitored devices and all you need is SNMP or WMI access. When creating dashboards, all you have to do is find the widget on the device you want to show up and choose the menu option to add it.
The sales team support we received was top notch. They worked hand in hand to make sure the product met all expectations. So far we have not really had to work with support that much; we have worked with setup team after purchase to deploy product fully. No issues so far and we are four weeks in.
We found the LogicMonitor documentation and online guides to be up to date and easy to follow. During our pre-sale proof of concept phase, we learned the basics of creating import CSV files and had the bulk of our devices added the first day. After the purchase, we used the professional services to get training on the entire system and help customizing everything to meet our needs. We also made use of the available certification online training courses for our power users to get them comfortable with the system.
During the evaluation process we looked a number of other solutions, a detailed technically analysis was carried out to map functionlity, deployment and scalabilty across the solutions. The primary areas that LogicMonitor succeeded are around the simplicity of deployment, scalability and to a lesser extent cost. Of the alternative products, Datadog is a better solution if your focus is on APM. However it will be harder to manage in a scaling MSP environment.
MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational
Pricing seems to be getting more and more aggressive, I worry that it's going to turn into ServiceNow or SAP and everything minor feature will be an extreme cost that prices out us and our customers
Haven't really used it but our initial onboarding PS was disappointing. Felt like we were being told what we needed to cover as opposed to what we wanted to cover. In addition, we were pushed into using the PS in tight time frames and we were not ready to do so.
Positive: Learning curve was relatively easy for our team. We were up and running within a sprint.
Positive: Managing Logstash has generally been easy. We configure it, and usually, don't have to worry about misbehavior.
Negative: Updating/Rehydrating Logstash servers have been little challenging. We sometimes even loose data while Logstash is down. It requires more in-depth research and experiments to figure the fine-grained details.
Negative: This is now one more application/skill/server to manage. Like any other servers, it requires proper grooming or else you will get in trouble. This is also a single point of failure which can have the ability to make other servers useless if it is not running.
