Alternatives to SumoLogic logo

Alternatives to SumoLogic

Kibana, Grafana, OpenSSL, Logstash, and Prometheus are the most popular alternatives and competitors to SumoLogic.
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What is SumoLogic and what are its top alternatives?

The Sumo Logic platform helps you make data-driven decisions and reduce the time to investigate security and operational issues so you can free up resources for more important activities.
SumoLogic is a tool in the Monitoring Tools category of a tech stack.

Top Alternatives to SumoLogic

  • 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. ...

  • Grafana

    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • OpenSSL

    OpenSSL

    It is a robust, commercial-grade, and full-featured toolkit for the Transport Layer Security (TLS) and Secure Sockets Layer (SSL) protocols. It is also a general-purpose cryptography library. ...

  • 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. ...

  • Prometheus

    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • Let's Encrypt

    Let's Encrypt

    It is a free, automated, and open certificate authority brought to you by the non-profit Internet Security Research Group (ISRG). ...

  • Nagios

    Nagios

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...

  • 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. ...

SumoLogic alternatives & related posts

Kibana logo

Kibana

15.3K
12K
255
Visualize your Elasticsearch data and navigate the Elastic Stack
15.3K
12K
+ 1
255
PROS OF KIBANA
  • 88
    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
    More "user-friendly"
  • 3
    Can build dashboards
  • 3
    Dev Tools
  • 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 · | 23 upvotes · 4.8M 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 · 478.3K 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
Grafana logo

Grafana

11.4K
9K
399
Open source Graphite & InfluxDB Dashboard and Graph Editor
11.4K
9K
+ 1
399
PROS OF GRAFANA
  • 84
    Beautiful
  • 67
    Graphs are interactive
  • 56
    Free
  • 55
    Easy
  • 33
    Nicer than the Graphite web interface
  • 24
    Many integrations
  • 16
    Can build dashboards
  • 10
    Easy to specify time window
  • 9
    Dashboards contain number tiles
  • 8
    Can collaborate on dashboards
  • 5
    Open Source
  • 5
    Click and drag to zoom in
  • 5
    Integration with InfluxDB
  • 4
    Threshold limits in graphs
  • 4
    Authentification and users management
  • 3
    Simple and native support to Prometheus
  • 3
    It is open to cloud watch and many database
  • 2
    Great community support
  • 2
    Alerts
  • 2
    You can visualize real time data to put alerts
  • 2
    You can use this for development to check memcache
  • 0
    Grapsh as code
  • 0
    Plugin visualizationa
CONS OF GRAFANA
    Be the first to leave a con

    related Grafana posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3M views

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

    See more
    Matt Menzenski
    Senior Software Engineering Manager at PayIt · | 13 upvotes · 110.6K views

    Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

    See more
    OpenSSL logo

    OpenSSL

    9.8K
    4.2K
    0
    Full-featured toolkit for the Transport Layer Security and Secure Sockets Layer protocols
    9.8K
    4.2K
    + 1
    0
    PROS OF OPENSSL
      Be the first to leave a pro
      CONS OF OPENSSL
        Be the first to leave a con

        related OpenSSL posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH · | 28 upvotes · 3.5M views

        Our whole DevOps stack consists of the following tools:

        • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
        • Respectively Git as revision control system
        • SourceTree as Git GUI
        • Visual Studio Code as IDE
        • CircleCI for continuous integration (automatize development process)
        • Prettier / TSLint / ESLint as code linter
        • SonarQube as quality gate
        • Docker as container management (incl. Docker Compose for multi-container application management)
        • VirtualBox for operating system simulation tests
        • Kubernetes as cluster management for docker containers
        • Heroku for deploying in test environments
        • nginx as web server (preferably used as facade server in production environment)
        • SSLMate (using OpenSSL) for certificate management
        • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
        • PostgreSQL as preferred database system
        • Redis as preferred in-memory database/store (great for caching)

        The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

        • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
        • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
        • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
        • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
        • Scalability: All-in-one framework for distributed systems.
        • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
        See more
        Logstash logo

        Logstash

        8.4K
        6.4K
        102
        Collect, Parse, & Enrich Data
        8.4K
        6.4K
        + 1
        102
        PROS OF LOGSTASH
        • 68
          Free
        • 18
          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
        • 3
          Memory-intensive
        • 1
          Documentation difficult to use

        related Logstash posts

        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 4.8M 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 · 630.7K 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
        Prometheus logo

        Prometheus

        2.6K
        3K
        237
        An open-source service monitoring system and time series database, developed by SoundCloud
        2.6K
        3K
        + 1
        237
        PROS OF PROMETHEUS
        • 46
          Powerful easy to use monitoring
        • 38
          Flexible query language
        • 32
          Dimensional data model
        • 27
          Alerts
        • 23
          Active and responsive community
        • 21
          Extensive integrations
        • 19
          Easy to setup
        • 12
          Beautiful Model and Query language
        • 7
          Easy to extend
        • 6
          Nice
        • 3
          Written in Go
        • 2
          Good for experimentation
        • 1
          Easy for monitoring
        CONS OF PROMETHEUS
        • 12
          Just for metrics
        • 6
          Bad UI
        • 6
          Needs monitoring to access metrics endpoints
        • 4
          Not easy to configure and use
        • 3
          Supports only active agents
        • 2
          Written in Go
        • 2
          Requires multiple applications and tools
        • 2
          TLS is quite difficult to understand

        related Prometheus posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3M views

        Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

        By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

        To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

        https://eng.uber.com/m3/

        (GitHub : https://github.com/m3db/m3)

        See more
        Matt Menzenski
        Senior Software Engineering Manager at PayIt · | 13 upvotes · 110.6K views

        Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

        See more
        Let's Encrypt logo

        Let's Encrypt

        1.6K
        942
        94
        A free, automated, and open Certificate Authority (CA)
        1.6K
        942
        + 1
        94
        PROS OF LET'S ENCRYPT
        • 46
          Open Source SSL
        • 30
          Simple setup
        • 9
          Free
        • 9
          Microservices
        • 0
          Easy ssl certificates
        CONS OF LET'S ENCRYPT
          Be the first to leave a con

          related Let's Encrypt posts

          Nagios logo

          Nagios

          782
          914
          102
          Complete monitoring and alerting for servers, switches, applications, and services
          782
          914
          + 1
          102
          PROS OF NAGIOS
          • 53
            It just works
          • 28
            The standard
          • 12
            Customizable
          • 8
            The Most flexible monitoring system
          • 1
            Huge stack of free checks/plugins to choose from
          CONS OF NAGIOS
            Be the first to leave a con

            related Nagios posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3M views

            Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

            By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

            To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

            https://eng.uber.com/m3/

            (GitHub : https://github.com/m3db/m3)

            See more
            ELK logo

            ELK

            688
            691
            20
            The acronym for three open source projects: Elasticsearch, Logstash, and Kibana
            688
            691
            + 1
            20
            PROS OF ELK
            • 13
              Open source
            • 3
              Good for startups with monetary limitations
            • 2
              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 · 600.7K views

            Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx

            See more