Alternatives to Amazon GuardDuty logo

Alternatives to Amazon GuardDuty

CloudFlare, Amazon Macie, Kibana, Grafana, and Prometheus are the most popular alternatives and competitors to Amazon GuardDuty.
48
39
+ 1
1

What is Amazon GuardDuty and what are its top alternatives?

It is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. It monitors for activity such as unusual API calls or potentially unauthorized deployments that indicate a possible account compromise. It also detects potentially compromised instances or reconnaissance by attackers.
Amazon GuardDuty is a tool in the Monitoring Tools category of a tech stack.

Top Alternatives to Amazon GuardDuty

  • CloudFlare

    CloudFlare

    Cloudflare speeds up and protects millions of websites, APIs, SaaS services, and other properties connected to the Internet. ...

  • Amazon Macie

    Amazon Macie

    Amazon Macie is a security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Amazon Macie recognizes sensitive data such as personally identifiable information (PII) or intellectual property, and provides you with dashboards and alerts that give visibility into how this data is being accessed or moved. ...

  • 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. ...

  • 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. ...

  • Nagios

    Nagios

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

  • Zabbix

    Zabbix

    Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics. ...

  • Graphite

    Graphite

    Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand ...

Amazon GuardDuty alternatives & related posts

CloudFlare logo

CloudFlare

66.7K
14.6K
1.7K
The Web Performance & Security Company.
66.7K
14.6K
+ 1
1.7K
PROS OF CLOUDFLARE
  • 421
    Easy setup, great cdn
  • 274
    Free ssl
  • 196
    Easy setup
  • 184
    Security
  • 179
    Ssl
  • 94
    Great cdn
  • 76
    Optimizer
  • 69
    Simple
  • 43
    Great UI
  • 27
    Great js cdn
  • 11
    AutoMinify
  • 11
    DNS Analytics
  • 11
    Apps
  • 11
    HTTP/2 Support
  • 8
    Ipv6
  • 8
    Easy
  • 8
    Rocket Loader
  • 7
    IPv6 "One Click"
  • 6
    SSHFP
  • 6
    Nice DNS
  • 6
    Fantastic CDN service
  • 6
    Cheapest SSL
  • 6
    Amazing performance
  • 6
    API
  • 6
    Free GeoIP
  • 5
    SPDY
  • 5
    DNSSEC
  • 5
    Free and reliable, Faster then anyone else
  • 4
    Asynchronous resource loading
  • 4
    Ip
  • 3
    Performance
  • 3
    Ubuntu
  • 3
    Easy Use
  • 3
    Global Load Balancing
  • 1
    Mtn
  • 1
    Maker
  • 1
    Support for SSHFP records
  • 1
    CDN
CONS OF CLOUDFLARE
  • 1
    No support for SSHFP records

related CloudFlare posts

Johnny Bell

When I first built my portfolio I used GitHub for the source control and deployed directly to Netlify on a push to master. This was a perfect setup, I didn't need any knowledge about #DevOps or anything, it was all just done for me.

One of the issues I had with Netlify was I wanted to gzip my JavaScript files, I had this setup in my #Webpack file, however Netlify didn't offer an easy way to set this.

Over the weekend I decided I wanted to know more about how #DevOps worked so I decided to switch from Netlify to Amazon S3. Instead of creating any #Git Webhooks I decided to use Buddy for my pipeline and to run commands. Buddy is a fantastic tool, very easy to setup builds, copying the files to my Amazon S3 bucket, then running some #AWS console commands to set the content-encoding of the JavaScript files. - Buddy is also free if you only have a few pipelines, so I didn't need to pay anything 馃馃徎.

When I made these changes I also wanted to monitor my code, and make sure I was keeping up with the best practices so I implemented Code Climate to look over my code and tell me where there code smells, issues, and other issues I've been super happy with it so far, on the free tier so its also free.

I did plan on using Amazon CloudFront for my SSL and cacheing, however it was overly complex to setup and it costs money. So I decided to go with the free tier of CloudFlare and it is amazing, best choice I've made for caching / SSL in a long time.

See more
Johnny Bell

I recently moved my portfolio to Amazon S3 and I needed a new way to cache and SSL my site as Amazon S3 does not come with this right out of the box. I tried Amazon CloudFront as I was already on Amazon S3 I thought this would be super easy and straight forward to setup... It was not, I was unable to get this working even though I followed all the online steps and even reached out for help to Amazon.

I'd used CloudFlare in the past, and thought let me see if I can set up CloudFlare on an Amazon S3 bucket. The setup for this was so basic and easy... I had it setup with caching and SSL within 5 minutes, and it was 100% free.

See more
Amazon Macie logo

Amazon Macie

10
39
0
Automatically Discover, Classify, and Secure Content at Scale
10
39
+ 1
0
PROS OF AMAZON MACIE
    Be the first to leave a pro
    CONS OF AMAZON MACIE
      Be the first to leave a con

      related Amazon Macie posts

      Kibana logo

      Kibana

      14.8K
      11.5K
      255
      Visualize your Elasticsearch data and navigate the Elastic Stack
      14.8K
      11.5K
      + 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 路 4.6M 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 468.4K 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

      10.9K
      8.6K
      398
      Open source Graphite & InfluxDB Dashboard and Graph Editor
      10.9K
      8.6K
      + 1
      398
      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
        Integration with InfluxDB
      • 5
        Click and drag to zoom in
      • 5
        Open Source
      • 4
        Authentification and users management
      • 4
        Threshold limits in graphs
      • 3
        It is open to cloud watch and many database
      • 2
        You can visualize real time data to put alerts
      • 2
        Great community support
      • 2
        Alerts
      • 2
        Simple and native support to Prometheus
      • 2
        You can use this for development to check memcache
      • 0
        Plugin visualizationa
      • 0
        Grapsh as code
      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 路 2.9M 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鈥檚 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鈥檚 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 | 12 upvotes 路 82.2K 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
        Prometheus logo

        Prometheus

        2.5K
        3K
        236
        An open-source service monitoring system and time series database, developed by SoundCloud
        2.5K
        3K
        + 1
        236
        PROS OF PROMETHEUS
        • 46
          Powerful easy to use monitoring
        • 38
          Flexible query language
        • 32
          Dimensional data model
        • 27
          Alerts
        • 22
          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
        • 11
          Just for metrics
        • 6
          Bad UI
        • 6
          Needs monitoring to access metrics endpoints
        • 3
          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 路 2.9M 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鈥檚 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鈥檚 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 | 12 upvotes 路 82.2K 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
        Nagios logo

        Nagios

        775
        905
        102
        Complete monitoring and alerting for servers, switches, applications, and services
        775
        905
        + 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 路 2.9M 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鈥檚 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鈥檚 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
          Zabbix logo

          Zabbix

          529
          751
          58
          Track, record, alert and visualize performance and availability of IT resources
          529
          751
          + 1
          58
          PROS OF ZABBIX
          • 16
            Free
          • 7
            Alerts
          • 5
            Service/node/network discovery
          • 4
            Base metrics from the box
          • 4
            Templates
          • 3
            Multi-dashboards
          • 3
            SMS/Email/Messenger alerts
          • 2
            Support proxies (for monitoring remote branches)
          • 2
            Supports Graphs ans screens
          • 2
            Grafana plugin available
          • 1
            Perform website checking (response time, loading, ...)
          • 1
            Supports large variety of Operating Systems
          • 1
            Supports JMX (Java, Tomcat, Jboss, ...)
          • 1
            Open source
          • 1
            API available for creating own apps
          • 1
            Templates free available (Zabbix Share)
          • 1
            Works with multiple databases
          • 1
            Advanced integrations
          • 1
            Supports multiple protocols/agents
          • 1
            Complete Logs Report
          CONS OF ZABBIX
          • 5
            The UI is in PHP
          • 2
            Puppet module is sluggish

          related Zabbix posts

          Shared insights
          on
          DatadogDatadogZabbixZabbixCentreonCentreon

          My team is divided on using Centreon or Zabbix for enterprise monitoring and alert automation. Can someone let us know which one is better? There is one more tool called Datadog that we are using for cloud assets. Of course, Datadog presents us with huge bills. So we want to have a comparative study. Suggestions and advice are welcome. Thanks!

          See more
          Graphite logo

          Graphite

          362
          369
          39
          A highly scalable real-time graphing system
          362
          369
          + 1
          39
          PROS OF GRAPHITE
          • 16
            Render any graph
          • 9
            Great functions to apply on timeseries
          • 7
            Well supported integrations
          • 5
            Includes event tracking
          • 2
            Rolling aggregation makes storage managable
          CONS OF GRAPHITE
            Be the first to leave a con

            related Graphite posts

            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber | 14 upvotes 路 2.9M 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鈥檚 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鈥檚 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

            A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we鈥檙e looking to migrate all of these to our internal monitoring system soon).

            See more