Alternatives to Lumigo logo

Alternatives to Lumigo

Epsagon, Thundra, AWS X-Ray, Datadog, and Kibana are the most popular alternatives and competitors to Lumigo.
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What is Lumigo and what are its top alternatives?

With a single click and no manual code changes, Lumigo visualizes your entire serverless environment, allowing you to see the full story of every transaction or request from beginning to end.
Lumigo is a tool in the Cloud Monitoring category of a tech stack.

Top Alternatives to Lumigo

  • Epsagon
    Epsagon

    Epsagon enables teams to instantly visualize, understand and optimize their microservice architectures. With our unique lightweight auto-instrumentation, gaps in data and manual work associated with other APM solutions are eliminated, provi ...

  • Thundra
    Thundra

    By eliminating the need for multiple tools in pre-production. Thundra provides observability into the CI process, helps optimize build duration, enables more frequent deployments, higher development productivity, and lower CI costs. ...

  • AWS X-Ray
    AWS X-Ray

    It helps developers analyze and debug production, distributed applications, such as those built using a microservices architecture. With this, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. It provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. ...

  • Datadog
    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • 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. ...

  • Amazon CloudWatch
    Amazon CloudWatch

    It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment. ...

  • 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. ...

Lumigo alternatives & related posts

Epsagon logo

Epsagon

27
35
6
Instantly understand your microservices
27
35
+ 1
6
PROS OF EPSAGON
  • 3
    Great visualization
  • 3
    Easy and automated setup
CONS OF EPSAGON
    Be the first to leave a con

    related Epsagon posts

    Thundra logo

    Thundra

    14
    17
    6
    Developer platform that helps optimize build duration, enabling more frequent deployments and lower CI costs.
    14
    17
    + 1
    6
    PROS OF THUNDRA
    • 4
      Provides the most detailed monitoring of any other tool
    • 2
      Serverless monitoring
    CONS OF THUNDRA
      Be the first to leave a con

      related Thundra posts

      AWS X-Ray logo

      AWS X-Ray

      58
      97
      0
      An application performance management service
      58
      97
      + 1
      0
      PROS OF AWS X-RAY
        Be the first to leave a pro
        CONS OF AWS X-RAY
          Be the first to leave a con

          related AWS X-Ray posts

          Datadog logo

          Datadog

          7K
          6.1K
          822
          Unify logs, metrics, and traces from across your distributed infrastructure.
          7K
          6.1K
          + 1
          822
          PROS OF DATADOG
          • 134
            Monitoring for many apps (databases, web servers, etc)
          • 106
            Easy setup
          • 86
            Powerful ui
          • 82
            Powerful integrations
          • 69
            Great value
          • 53
            Great visualization
          • 45
            Events + metrics = clarity
          • 40
            Custom metrics
          • 40
            Notifications
          • 38
            Flexibility
          • 18
            Free & paid plans
          • 15
            Great customer support
          • 14
            Makes my life easier
          • 9
            Adapts automatically as i scale up
          • 8
            Easy setup and plugins
          • 7
            Super easy and powerful
          • 6
            In-context collaboration
          • 6
            AWS support
          • 5
            Rich in features
          • 4
            Docker support
          • 4
            Cost
          • 3
            Easy to Analyze
          • 3
            Full visibility of applications
          • 3
            Automation tools
          • 3
            Monitor almost everything
          • 3
            Cute logo
          • 3
            Simple, powerful, great for infra
          • 3
            Source control and bug tracking
          • 3
            Best than others
          • 3
            Expensive
          • 2
            Best in the field
          • 2
            Good for Startups
          • 2
            Free setup
          CONS OF DATADOG
          • 17
            Expensive
          • 4
            No errors exception tracking
          • 2
            External Network Goes Down You Wont Be Logging
          • 1
            Complicated

          related Datadog posts

          Robert Zuber

          Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

          We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

          See more

          We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

          We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

          We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

          You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

          See more
          Kibana logo

          Kibana

          16.6K
          13K
          256
          Visualize your Elasticsearch data and navigate the Elastic Stack
          16.6K
          13K
          + 1
          256
          PROS OF KIBANA
          • 88
            Easy to setup
          • 62
            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 · | 23 upvotes · 5.1M 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 · 505.1K 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

          12.8K
          10.1K
          402
          Open source Graphite & InfluxDB Dashboard and Graph Editor
          12.8K
          10.1K
          + 1
          402
          PROS OF GRAFANA
          • 84
            Beautiful
          • 67
            Graphs are interactive
          • 57
            Free
          • 56
            Easy
          • 33
            Nicer than the Graphite web interface
          • 24
            Many integrations
          • 17
            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
            Authentification and users management
          • 4
            Threshold limits in graphs
          • 3
            It is open to cloud watch and many database
          • 3
            Simple and native support to Prometheus
          • 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
          • 1
            No interactive query builder

          related Grafana posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M 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 · | 14 upvotes · 203.1K 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
          Amazon CloudWatch logo

          Amazon CloudWatch

          10.1K
          6.3K
          214
          Monitor AWS resources and custom metrics generated by your applications and services
          10.1K
          6.3K
          + 1
          214
          PROS OF AMAZON CLOUDWATCH
          • 76
            Monitor aws resources
          • 46
            Zero setup
          • 30
            Detailed Monitoring
          • 23
            Backed by Amazon
          • 19
            Auto Scaling groups
          • 11
            SNS and autoscaling integrations
          • 5
            Burstable instances metrics (t2 cpu credit balance)
          • 3
            HIPAA/PCI/SOC Compliance-friendly
          • 1
            Native tool for AWS so understand AWS out of the box
          CONS OF AMAZON CLOUDWATCH
          • 1
            Poor Search Capabilities

          related Amazon CloudWatch posts

          Maurice Ruff

          We build everything in AWS around microservices and are looking at Amazon CloudWatch, Datadog, and New Relic. Which one would work best for our situation?

          See more
          Prometheus logo

          Prometheus

          2.9K
          3.2K
          238
          An open-source service monitoring system and time series database, developed by SoundCloud
          2.9K
          3.2K
          + 1
          238
          PROS OF PROMETHEUS
          • 46
            Powerful easy to use monitoring
          • 38
            Flexible query language
          • 32
            Dimensional data model
          • 27
            Alerts
          • 23
            Active and responsive community
          • 22
            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
            TLS is quite difficult to understand
          • 2
            Requires multiple applications and tools
          • 1
            Single point of failure

          related Prometheus posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M 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 · | 14 upvotes · 203.1K 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