Logz.io in Boston offers their enterprise-grade log analytics application, oriented towards providing data security and eliminating the need for capacity management.
$0
1 day of log retention.
Pricing
Logstash
Logz.io
Editions & Modules
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Log Management - Community
$0
1 day of log retention.
Log Management - Pro
$.92
per ingested GB. 7 days retention.
Distributed Tracing - Pro
$5
Per million spans.
Infrastructure Monitoring - Pro
$12
per month per 1000 time-series metrics.
Log Management - Enterprise
Custom
Cloud SIEM - Enterprise
from $1.49
per ingested GB. Price includes Logz.io Log Management
Logstash is a must in an ELK stack, which I am sure is going to be the #1 case. At any point when you have several sources, Logstash can be the common point to aggregate, and categorize those data. Then send this new data to its destination. Very handy. It is free and open source. It may not be appropriate to analyze data-sets dependent on each other but from a different data source. Reason being Logstash works on data at hand, and not wait for other data to arrive. It would be unwise for Logstashh to handle complicated, long-running transformations because this is injected and ejected. The faster you do it, the safer.
It is appropriate for companies that focus on developing extremely simple applications. The great visibility it provides makes it ideal to avoid problems that may affect the entire business or company thanks to the fact that it is capable of emitting dozens of alerts in a short time. Sometimes the search behavior becomes slow and inefficient, which can be uncomfortable.
Alerting - Logz.io allows you to set up numerous alerts and define the specific conditions to trigger these alerts, such as the number of occurrences over a specific period of time and severity.
Notifications - The supported integrations with Slack and OpsGenie make it easy to set up alerts to specific groups or users, like those in a particular Slack room or OpsGenie group. This is good to reduce noise and limit initial notifications to those who really need to get it.
Memory: Logstash is a HOG, if you are deploying it on commodity (i.e. cheap and old) hardware: You will need at least 2GB, just for Logstash. So don't expect to run your entire ELK stack on one AMD Athlon machine.
Overlap: Logstash fills in an area of the ELK stack that makes the most sense: as a log file transformer / shipper. However, if you start breaking that stack, with the addition of other components- you start seeing where features of Logstash may be implemented or solved in the additional components much easier (or better, or to a higher degree of resolution)
More Overlap: Since my team employs Syslog-ng extensively- Logstash can sometimes get in the way (and this may be a problem for DevOps stacks overall): You can configure Syslog to record certain information from a source, filter that data, and even export that data in a particular format. Logstash will pick that data up, and then parse it. However, if you don't keep your Syslog-ng configuration files, and your Logstash configuration files in sync, your results will not be what you expected, and this will translate into (sometimes) hours/days of work, hunting down a line item in a configuration file.
As I said earlier, for a production-grade OpenStack Telco cloud, Logstash brings high value in flexibility, compliance, and troubleshooting efficiency. However, this brings a higher infra & ops cost on resources, but that is not a problem in big datacenters because there is no resource crunch in terms of servers or CPU/RAM
Am really exited to use the reports generated especially AWS Cost and Usage Reports function tracks your AWS usage and provides estimated charges associated with your account so that we can reduce the data costs to a greater extend. This integration allows you to ship logs from your AWS Cost and Usage Reports to your Logz.io account.More amazing features awaits you in Logz.io account.
In the past, my team has been able to get in contact with Logz.io quickly and easily to address our questions about the product to see if it could fully meet all our needs. Some of the features we needed at the time were not available, but were on the Logz.io team's roadmap to implement in the future. I found their team to be friendly, professional, and helpful
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
Graylog and Microsoft System Center lacks infrastructure management and logging, proper analytics is done only on Logz.io. The customer support is also really great for Logz.io, we are really pleased with their support and timely action. The migration was also easy and took us hardly a day to set up and run the solutions.
It is very difficult to give any figures on ROI, as it depends on many factors, and in a Telcocloud environment, it is much complex to find out; however, I would give some points below on ROI
ROI based on flexibility is very high, as it reduces the time to find RCA
ROI based on integration is very high because it supports multi-vendor environments, avoiding vendor lock-in & works across multi-cloud setups
ROI on resource consumption is less because Logstash in 2-3 times more resource-intensive as compared to its lightweight alternatives resulting in latency
