What is CloudBolt and what are its top alternatives?
Top Alternatives to CloudBolt
- Scalr
Scalr is a remote state & operations backend for Terraform with access controls, policy as code, and many quality of life features. ...
- Morpheus
Morpheus is a cloud application management and orchestration platform that works on any cloud or infrastructure, from AWS to bare metal. Enjoy complete cloud freedom with Morpheus. ...
- Terraform
With Terraform, you describe your complete infrastructure as code, even as it spans multiple service providers. Your servers may come from AWS, your DNS may come from CloudFlare, and your database may come from Heroku. Terraform will build all these resources across all these providers in parallel. ...
- RightScale
Automation is the core of RightScale, freeing you to run efficient, scalable, and highly-available applications. Our multi-cloud integration enables you to choose your own clouds, providing freedom to work with any vendor in a rapidly changing market. And rest assured knowing that you have visibility and control over all of your resources in one place. To take advantage of best practices, we encourage you to tap into cloud expertise provided by our service, support, and partner networks when building and managing your infrastructure. ...
- Ansible
Ansible is an IT automation tool. It can configure systems, deploy software, and orchestrate more advanced IT tasks such as continuous deployments or zero downtime rolling updates. Ansible’s goals are foremost those of simplicity and maximum ease of use. ...
- New Relic
The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too. ...
- 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 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. ...
CloudBolt alternatives & related posts
Scalr
- Image Builder5
- Open Source3
- Auto Scaling3
- Orchestration2
- Multi-Cloud Support2
- Cost Analytics2
- Chef Integration2
- Hybrid Cloud Management2
- User Friendly2
- Terraform CLI Integration1
- Open Policy Agent1
- Cost1
related Scalr posts
- Easy to deploy and use2
- Hybrid Cloud Management1
- Life cycle management1
- App provisioning1
- UI, API and CLI1
- Governance1
- SDN - ACI, NSX, Neutron1
- Config Management-Chef,Puppet,Salt,Ansible,AnsibleTower1
- Reporting1
- Analytics1
- Scheduling1
- Tagging, Env variables, cypher1
- Automation - Tasks and Workflows1
- Image builder1
- Infrastrcuture as Code1
- Platform as a Service1
- Infrastructure as Code, Platform as a Service1
related Morpheus posts
Terraform
- Infrastructure as code121
- Declarative syntax73
- Planning45
- Simple28
- Parallelism24
- Well-documented8
- Cloud agnostic8
- It's like coding your infrastructure in simple English6
- Immutable infrastructure6
- Platform agnostic5
- Extendable4
- Automation4
- Automates infrastructure deployments4
- Portability4
- Lightweight2
- Scales to hundreds of hosts2
- Doesn't have full support to GKE1
related Terraform posts
![Google Cloud IoT Core](https://img.stackshare.io/service/7702/cloud-iot-core.png)
![Terraform](https://img.stackshare.io/service/1276/default_2316907c4199f912e2ed79cbdb99025c9e5e2665.png)
![Python](https://img.stackshare.io/service/993/pUBY5pVj.png)
![Google Cloud Deployment Manager](https://img.stackshare.io/service/6810/deployment-manager-3.png)
![Google Cloud Build](https://img.stackshare.io/service/9309/PoHJY3K8_400x400.jpg)
Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
![Heroku](https://img.stackshare.io/service/133/3wgIDj3j.png)
![Kubernetes](https://img.stackshare.io/service/1885/21_d3cvM.png)
![Google Kubernetes Engine](https://img.stackshare.io/service/1587/s01TMTGn.png)
![Google Cloud SQL for PostgreSQL](https://img.stackshare.io/service/6671/sql.png)
![PostgreSQL](https://img.stackshare.io/service/1028/ASOhU5xJ.png)
We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).
We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .
Read the blog post to go more in depth.
related RightScale posts
Ansible
- Agentless284
- Great configuration210
- Simple199
- Powerful176
- Easy to learn155
- Flexible69
- Doesn't get in the way of getting s--- done55
- Makes sense35
- Super efficient and flexible30
- Powerful27
- Dynamic Inventory11
- Backed by Red Hat9
- Works with AWS7
- Cloud Oriented6
- Easy to maintain6
- Vagrant provisioner4
- Simple and powerful4
- Multi language4
- Simple4
- Because SSH4
- Procedural or declarative, or both4
- Easy4
- Consistency3
- Well-documented2
- Masterless2
- Debugging is simple2
- Merge hash to get final configuration similar to hiera2
- Fast as hell2
- Manage any OS1
- Work on windows, but difficult to manage1
- Certified Content1
- Dangerous8
- Hard to install5
- Doesn't Run on Windows3
- Bloated3
- Backward compatibility3
- No immutable infrastructure2
related Ansible posts
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.
Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.
New Relic
- Easy setup415
- Really powerful344
- Awesome visualization245
- Ease of use194
- Great ui151
- Free tier106
- Great tool for insights80
- Heroku Integration66
- Market leader55
- Peace of mind49
- Push notifications21
- Email notifications20
- Heroku Add-on17
- Error Detection and Alerting16
- Multiple language support13
- SQL Analysis11
- Server Resources Monitoring11
- Transaction Tracing9
- Apdex Scores8
- Azure Add-on8
- Analysis of CPU, Disk, Memory, and Network7
- Detailed reports7
- Performance of External Services6
- Error Analysis6
- Application Availability Monitoring and Alerting6
- Application Response Times6
- Most Time Consuming Transactions5
- JVM Performance Analyzer (Java)5
- Browser Transaction Tracing4
- Top Database Operations4
- Easy to use4
- Application Map3
- Weekly Performance Email3
- Pagoda Box integration3
- Custom Dashboards3
- Easy to setup2
- Background Jobs Transaction Analysis2
- App Speed Index2
- Super Expensive1
- Team Collaboration Tools1
- Metric Data Retention1
- Metric Data Resolution1
- Worst Transactions by User Dissatisfaction1
- Real User Monitoring Overview1
- Real User Monitoring Analysis and Breakdown1
- Time Comparisons1
- Access to Performance Data API1
- Incident Detection and Alerting1
- Best of the best, what more can you ask for1
- Best monitoring on the market1
- Rails integration1
- Free1
- Proce0
- Price0
- Exceptions0
- Cost0
- Pricing model doesn't suit microservices20
- UI isn't great10
- Expensive7
- Visualizations aren't very helpful7
- Hard to understand why things in your app are breaking5
related New Relic posts
Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.
Current Environment: .NET Core Web app hosted on Microsoft IIS
Future Environment: Web app will be hosted on Microsoft Azure
Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server
Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.
Please advise on the above. Thanks!
I need to choose a monitoring tool for my project, but currently, my application doesn't have much load or many users. My application is not generating GBs of data. We don't want to send the user information to New Relic because it's a 3rd party tool. And we can deploy Kibana locally on our server. What should I use, Kibana or New Relic?
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
related Kibana posts
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.
This is my stack in Application & Data
JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Slack
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
- No interactive query builder1
related Grafana posts
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.
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...
(GitHub : https://github.com/m3db/m3)