Alternatives to logz.io logo

Alternatives to logz.io

Loggly, Logentries, Coralogix, Splunk, and Kibana are the most popular alternatives and competitors to logz.io.
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What is logz.io and what are its top alternatives?

It provides Elasticsearch, Logstash and Kibana on the cloud with alerts, unlimited scalability and free ELK apps. Index, search & visualize your data.
logz.io is a tool in the Log Management category of a tech stack.

Top Alternatives to logz.io

  • Loggly
    Loggly

    It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain. ...

  • 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. ...

  • Coralogix
    Coralogix

    Coralogix is a stateful streaming data platform that provides real-time insights and long-term trend analysis with no reliance on storage or indexing, solving the monitoring challenges of data growth in large-scale systems. ...

  • Splunk
    Splunk

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

  • 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. ...

  • Elastic Cloud
    Elastic Cloud

    A growing family of Elastic SaaS offerings that make it easy to deploy, operate, and scale Elastic products and solutions in the cloud. From an easy-to-use hosted and managed Elasticsearch experience to powerful, out-of-the-box search solutions. ...

  • 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‑side 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. ...

logz.io alternatives & related posts

Loggly logo

Loggly

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Unified log analysis & log monitoring
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PROS OF LOGGLY
  • 37
    Centralized log management
  • 25
    Easy to setup
  • 21
    Great filtering
  • 16
    Live logging
  • 15
    Json log support
  • 10
    Log Management
  • 10
    Alerting
  • 7
    Love the product
  • 7
    Great Dashboards
  • 4
    Heroku Add-on
  • 2
    Easy setup
  • 2
    No alerts in free plan
  • 2
    Backup to S3
  • 2
    Great UI
  • 2
    Good parsing
  • 2
    Powerful
  • 2
    Fast search
  • 2
    Easy to setup and use
CONS OF LOGGLY
  • 3
    Pricey after free plan

related Loggly posts

Logentries logo

Logentries

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Real-time log management and analytics built for the cloud
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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
    Coralogix logo

    Coralogix

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    32
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    Stateful streaming analytics for monitoring of large-scale systems
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    + 1
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    PROS OF CORALOGIX
      Be the first to leave a pro
      CONS OF CORALOGIX
        Be the first to leave a con

        related Coralogix posts

        Splunk logo

        Splunk

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        859
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        Search, monitor, analyze and visualize machine data
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        13
        PROS OF SPLUNK
        • 2
          Alert system based on custom query results
        • 2
          API for searching logs, running reports
        • 2
          Query engine supports joining, aggregation, stats, etc
        • 1
          Ability to style search results into 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
          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
        Kibana logo

        Kibana

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        Visualize your Elasticsearch data and navigate the Elastic Stack
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        PROS OF KIBANA
        • 88
          Easy to setup
        • 62
          Free
        • 45
          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

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        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 5.3M 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 · 524.7K 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
        Elastic Cloud logo

        Elastic Cloud

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        Deploy, operate, and scale Elastic products and solutions in the cloud
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        PROS OF ELASTIC CLOUD
          Be the first to leave a pro
          CONS OF ELASTIC CLOUD
            Be the first to leave a con

            related Elastic Cloud posts

            Gunnar Siréus
            System Developer at Siréus Consulting AB · | 2 upvotes · 8.5K views
            Shared insights
            on
            Elastic CloudElastic CloudJelasticJelastic

            I wonder what the best option for hosting my home page https://www.sireus.se is? On Jelastic or Elastic Cloud? Which host is the most suitable and cost-effective?

            See more
            ELK logo

            ELK

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            The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
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            PROS OF ELK
            • 13
              Open source
            • 3
              Good for startups with monetary limitations
            • 3
              Can run locally
            • 1
              Easy to setup
            • 1
              External Network Goes Down You Aren't Without Logging
            • 0
              Json log supprt
            • 0
              Live logging
            CONS OF ELK
            • 4
              Elastic Search is a resource hog
            • 3
              Logstash configuration is a pain
            • 1
              Bad for startups with personal limitations

            related ELK posts

            Wallace Alves
            Cyber Security Analyst · | 1 upvote · 655.7K views

            Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

            See more
            Graylog logo

            Graylog

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            Open source log management that actually works
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            PROS OF GRAYLOG
            • 17
              Open source
            • 12
              Powerfull
            • 7
              Well documented
            • 5
              User authentification
            • 5
              Flexibel query and parsing language
            • 5
              Alerts
            • 2
              Alerts and dashboards
            • 2
              User management
            • 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