LogDNA vs. Logstash

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
LogDNA
Score 7.9 out of 10
N/A
LogDNA headquartered in Mountain View offers their eponymous cloud log management or on-prem/self-hosted log management solution.N/A
Logstash
Score 7.6 out of 10
N/A
N/AN/A
Pricing
LogDNALogstash
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
LogDNALogstash
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Best Alternatives
LogDNALogstash
Small Businesses
SolarWinds Papertrail
SolarWinds Papertrail
Score 8.8 out of 10
SolarWinds Papertrail
SolarWinds Papertrail
Score 8.8 out of 10
Medium-sized Companies
SolarWinds Papertrail
SolarWinds Papertrail
Score 8.8 out of 10
SolarWinds Papertrail
SolarWinds Papertrail
Score 8.8 out of 10
Enterprises
Splunk Log Observer
Splunk Log Observer
Score 8.7 out of 10
Splunk Log Observer
Splunk Log Observer
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
LogDNALogstash
Likelihood to Recommend
10.0
(2 ratings)
10.0
(3 ratings)
Support Rating
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
LogDNALogstash
Likelihood to Recommend
LogDNA
Anytime we need to make any kind of log on any platform that we use, LogDNA is well suited. It is primarily used for logging errors on our server but also used for checking issues within our iOS apps when we need to identify a critical issue that is occurring. The search feature is very very good!
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Elastic
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).
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Pros
LogDNA
  • The UI is simple to use and hasn't ever been confusing for anyone to use on our team.
  • Backups are great since we do occasionally need to access logs from the past and it has helped us solve some problems.
  • Searching/filtering is great and I've always been able to get exactly what I need.
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Elastic
  • 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.
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Cons
LogDNA
  • There isn't really much I would change
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Elastic
  • 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.
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Support Rating
LogDNA
It is very very good. They are responsive and very helpful. They also take the time to walk you through the issues you are having. But honestly, it is so straightforward that you rarely need to get support from their team. They are very helpful when you do need their support though.
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Elastic
No answers on this topic
Alternatives Considered
LogDNA
LogDNA was already setup when I started and it is offered through Heroku so that's why it was chosen. It's also cheaper I believe. Compared to Logentries, it does the same thing.
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Elastic
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
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Return on Investment
LogDNA
  • It has helped a lot when trying to debug issues with customers or internal employees. It's a positive overall because it has saved us a lot of time debugging issues.
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Elastic
  • 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.
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