Alternatives to Logmatic logo

Alternatives to Logmatic

Splunk, Logentries, Kibana, Logstash, and Papertrail are the most popular alternatives and competitors to Logmatic.
67
77
+ 1
238

What is Logmatic and what are its top alternatives?

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.
Logmatic is a tool in the Log Management category of a tech stack.

Top Alternatives to Logmatic

  • Splunk

    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Logentries

    Logentries

    Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users. ...

  • Kibana

    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

  • Logstash

    Logstash

    Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana. ...

  • Papertrail

    Papertrail

    Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs. ...

  • ELK

    ELK

    It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server鈥憇ide data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch. ...

  • Graylog

    Graylog

    Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information. ...

  • Fluentd

    Fluentd

    Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure. ...

Logmatic alternatives & related posts

Splunk logo

Splunk

383
575
10
Search, monitor, analyze and visualize machine data
383
575
+ 1
10
PROS OF SPLUNK
  • 1
    Ability to style search results into reports
  • 1
    API for searching logs, running reports
  • 1
    Query any log as key-value pairs
  • 1
    Splunk language supports string, date manip, math, etc
  • 1
    Granular scheduling and time window support
  • 1
    Alert system based on custom query results
  • 1
    Query engine supports joining, aggregation, stats, etc
  • 1
    Custom log parsing as well as automatic parsing
  • 1
    Dashboarding on any log contents
  • 1
    Rich GUI for searching live logs
CONS OF SPLUNK
  • 1
    Splunk query language rich so lots to learn

related Splunk posts

Shared insights
on
KibanaKibanaSplunkSplunkGrafanaGrafana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

See more
Logentries logo

Logentries

281
150
105
Real-time log management and analytics built for the cloud
281
150
+ 1
105
PROS OF LOGENTRIES
  • 34
    Log search
  • 27
    Live logs
  • 19
    Easy setup
  • 14
    Heroku Add-on
  • 5
    Backup to S3
  • 2
    Easy setup, independent of existing logging setup
  • 2
    Free
  • 2
    Search/query with regex
  • 0
    E
CONS OF LOGENTRIES
    Be the first to leave a con

    related Logentries posts

    Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

    See more
    Kibana logo

    Kibana

    12.3K
    9.1K
    253
    Explore & Visualize Your Data
    12.3K
    9.1K
    + 1
    253
    PROS OF KIBANA
    • 86
      Easy to setup
    • 61
      Free
    • 44
      Can search text
    • 21
      Has pie chart
    • 13
      X-axis is not restricted to timestamp
    • 8
      Easy queries and is a good way to view logs
    • 6
      Supports Plugins
    • 3
      Dev Tools
    • 3
      More "user-friendly"
    • 3
      Can build dashboards
    • 2
      Easy to drill-down
    • 2
      Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
    • 1
      Up and running
    CONS OF KIBANA
    • 5
      Unintuituve
    • 3
      Elasticsearch is huge
    • 3
      Works on top of elastic only
    • 2
      Hardweight UI

    related Kibana posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD | 21 upvotes 路 4M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Patrick Sun
    Software Engineer at Stitch Fix | 11 upvotes 路 401.6K views

    Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

    Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

    See more
    Logstash logo

    Logstash

    7K
    5.1K
    98
    Collect, Parse, & Enrich Data
    7K
    5.1K
    + 1
    98
    PROS OF LOGSTASH
    • 65
      Free
    • 17
      Easy but powerful filtering
    • 12
      Scalable
    • 2
      Kibana provides machine learning based analytics to log
    • 1
      Great to meet GDPR goals
    • 1
      Well Documented
    CONS OF LOGSTASH
    • 2
      Memory-intensive
    • 1
      Documentation difficult to use

    related Logstash posts

    Tymoteusz Paul
    Devops guy at X20X Development LTD | 21 upvotes 路 4M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    Tanya Bragin
    Product Lead, Observability at Elastic | 10 upvotes 路 572.7K views

    ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

    See more
    Papertrail logo

    Papertrail

    591
    331
    273
    Hosted log management for servers, apps, and cloud services
    591
    331
    + 1
    273
    PROS OF PAPERTRAIL
    • 86
      Log search
    • 43
      Integrates with Heroku
    • 43
      Easy log aggregation across multiple machines
    • 37
      Simple interface
    • 26
      Backup to S3
    • 19
      Easy setup, independent of existing logging setup
    • 15
      Heroku add-on
    • 3
      Command line interface
    • 1
      Alerting
    CONS OF PAPERTRAIL
    • 1
      Expensive

    related Papertrail posts

    Logentries, LogDNA, Timber.io, Papertrail and Sumo Logic provide free pricing plan for #Heroku application. You can add these applications as add-ons very easily.

    See more
    ELK logo

    ELK

    563
    530
    8
    The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
    563
    530
    + 1
    8
    PROS OF ELK
    • 8
      Open source
    CONS OF ELK
    • 3
      Elastic Search is a resource hog
    • 3
      Logstash configuration is a pain

    related ELK posts

    Wallace Alves
    Cyber Security Analyst | 1 upvote 路 522.5K views

    Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

    See more
    Graylog logo

    Graylog

    436
    499
    55
    Open source log management that actually works
    436
    499
    + 1
    55
    PROS OF GRAYLOG
    • 13
      Open source
    • 11
      Powerfull
    • 7
      Well documented
    • 5
      Flexibel query and parsing language
    • 5
      User authentification
    • 5
      Alerts
    • 2
      User management
    • 2
      Alerts and dashboards
    • 2
      Easy query language and english parsing
    • 1
      Easy to install
    • 1
      Manage users and permissions
    • 1
      A large community
    CONS OF GRAYLOG
    • 1
      Does not handle frozen indices at all

    related Graylog posts

    Fluentd logo

    Fluentd

    418
    464
    22
    Unified logging layer
    418
    464
    + 1
    22
    PROS OF FLUENTD
    • 6
      Lightweight
    • 6
      Great for Kubernetes node container log forwarding
    • 6
      Open-source
    • 4
      Easy
    CONS OF FLUENTD
      Be the first to leave a con

      related Fluentd posts