What is Equinix Metal and what are its top alternatives?
Top Alternatives to Equinix Metal
Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...
Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images. ...
Parcel is a web application bundler, differentiated by its developer experience. It offers blazing fast performance utilizing multicore processing, and requires zero configuration. ...
Flow is an online collaboration platform that makes it easy for people to create, organize, discuss, and accomplish tasks with anyone, anytime, anywhere. By merging a sleek, intuitive interface with powerful functionality, we're out to revolutionize the way the world's productive teams get things done. ...
- Amazon EC2
It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers. ...
- Microsoft Azure
Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment. ...
- Google Cloud Platform
It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. ...
We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel. ...
Equinix Metal alternatives & related posts
- Easy to scale and maintain 3rd party services86
- One API49
- Multiple integrations25
- Cleanest API19
- Mixpanel Integration8
- Segment SQL7
- Google Analytics Integration4
- Salesforce Integration2
- SQL Access2
- Clean Integration with Application2
- Own all your tracking data1
- Quick setup1
- Clearbit integration1
- Beautiful UI1
- Integrates with Apptimize1
- Woopra Integration1
- Not clear which events/options are integration-specific2
- Limitations with integration-specific configurations1
- Client-side events are separated from server-side1
related Segment posts
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.
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
- Cross platform builds27
- Vm creation automation9
- Bake in security4
- Good documentation1
- Easy to use1
related Packer posts
LaunchDarkly is almost a five year old company, and our methodology for deploying was state of the art... for 2014. We recently undertook a project to modernize the way we #deploy our software, moving from Ansible-based deploy scripts that executed on our local machines, to using Spinnaker (along with Terraform and Packer) as the basis of our deployment system. We've been using Armory's enterprise Spinnaker offering to make this project a reality.
- Zero configuration10
- Built-in dev server with livereload8
- Lack of documentation3
related Parcel posts
Using Webpack is one of the best decision ever. I have used to Grunt and gulp previously, but the experience is not the same, and despite I know there are other bundlers like Parcel, Webpack gives me the perfect balance between automatization and configuration. The ecosystem of tools and loaders is amazing, and with WebPack #merge, you can modularize your build and define standard pieces to assemble different build configurations. I don't like processes where you cannot see their guts, and you have to trust in magic a little bit too much for my taste. But also I don't want to reinvent the wheel and lose too much time configuring my build processes. And of course, I love #WebPackDevServer and hot reloading.
- Great for collaboration6
- Easy to use6
related Flow posts
- Quick and reliable cloud servers644
- Easy management392
- Low cost276
- Market leader88
- Backed by amazon80
- Free tier66
- Easy management, scalability57
- Easy to Start10
- Widely used8
- Node.js API7
- Industry Standard4
- Lots of configuration options3
- GPU instances2
- Amazing for individuals1
- Extremely simple to use1
- All the Open Source CLI tools you could want.1
- Simpler to understand and learn1
- Ui could use a lot of work13
- High learning curve when compared to PaaS6
- Extremely poor CPU performance3
related Amazon EC2 posts
To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.
Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.
We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.
Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.
Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.
#BigData #AWS #DataScience #DataEngineering
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.
- Scales well and quite easy113
- Can use .Net or open source tools95
- Startup friendly81
- Startup plans via BizSpark73
- High performance62
- Wide choice of services38
- Lots of integrations32
- Low cost32
- Twillio & Github are directly accessible19
- RESTful API13
- Startup support10
- Enterprise Grade10
- In person support7
- Virtual Machines6
- Free for students6
- Service Bus6
- Redis Cache5
- It rocks5
- SQL Databases4
- Infrastructure Services4
- Storage, Backup, and Recovery4
- BizSpark 60k Azure Benefit3
- Built on Node.js3
- Big Data3
- Preview Portal3
- Open cloud2
- Big Compute2
- Machine Learning2
- Stream Analytics2
- Data Factory2
- Event Hubs2
- Virtual Network2
- Traffic Manager2
- Media Services2
- BizTalk Services2
- Site Recovery2
- Active Directory2
- Multi-Factor Authentication2
- Visual Studio Online2
- Application Insights2
- Operational Insights2
- Key Vault2
- Infrastructure near your customers2
- Easy Deployment2
- Enterprise customer preferences1
- Best cloud platfrom1
- Easy and fast to start with1
- Remote Debugging1
- Confusing UI6
- Expensive plesk on Azure2
related Microsoft Azure posts
We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!
CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.
CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.
AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.
It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.
The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.
In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.
Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.
- 1 year free trial credit USD3003
- Good app Marketplace for Beginner and Advanced User2
- Premium tier IP address2
- Live chat support1
related Google Cloud Platform posts
I am currently working on a long term mobile app project. Current stack: Frontend: Dart/Flutter Backend: Go, AWS Resources (AWS Lambda, Amazon DynamoDB, etc.) Since there are only two developers and we have limited time and resources, we are looking for a BAAS like Firebase or AWS Amplify to handle auth and push notifications for now. We are prioritizing developing speed so we can iterate quickly. The only problem is that AWS amplify support for flutter is in developer preview and has limited capabilities (We have tested it out in our app). Firebase is the more mature option. It has great support for flutter and has more than we need for auth, notifications, etc. My question is that, if we choose firebase, we would be stuck with using two different cloud providers. Is this bad, or is this even a problem? I am willing to change anything on the backend architecture wise, so any suggestions would be greatly appreciated as I am somewhat unfamiliar with Google Cloud Platform. Thank you.
I have started using AWS Batch for some long ML inference jobs. So far it's working well and giving a decent performance. Since it is fully managed, it saves a lot of extra work as well. But Batch takes a good amount of time to create a new cluster and then load the job based on the priority of the queue. Going forward would love to put effort into something which is fast to start and give more flexibility as well. What other tools you would suggest for long-running backend jobs which can scale well. I am not looking for something fully managed so ignore the options similar to batch in Google Cloud Platform or Microsoft Azure, Looking for open-source alternatives here. Do you think Kubernetes, RabbitMQ/Kafka will be a good fit or just overkill for my problem. Usually w we get 1000s of requests in parallel and each job might take 20-30 mins in a 2 vCPU system.
- Great value for money559
- Simple dashboard364
- Good pricing362
- Nice ui250
- Easy configuration192
- Great documentation155
- Ssh access138
- Great community135
- IPv6 support12
- Private networking10
- 99.99% uptime SLA8
- Simple API7
- Great tutorials7
- 55 Second Provisioning6
- One Click Applications5
- Simple Control Panel3
- Word Press3
- 1Gb/sec Servers3
- Quick and no nonsense service2
- Hex Core machines with dedicated ECC Ram and RAID SSD s2
- Runs CoreOS2
- Good Tutorials2
- Ruby on Rails2
- KVM Virtualization1
- Amazing Hardware1
- Transfer Globally1
- FreeBSD Amp1
- My go to server provider1
- Ease and simplicity1
- Find it superfitting with my requirements (SSD, ssh.1
- Easy Setup1
- Static IP1
- It's the easiest to get started for small projects1
- Automatic Backup1
- Great support1
- Quick and easy to set up1
- Servers on demand - literally1
- Variety of services0
- Managed Kubernetes0
- No live support chat3
related DigitalOcean posts
Hello, I'm currently writing an e-commerce website with Laravel and Laravel Nova (as an admin panel). I want to start deploying the app and created a DigitalOcean account. After some searches about the deployment process, I saw that the setup via DigitalOcean (using Droplets) isn't very easy for beginners. Now I'm not sure how to deploy my app. I am in between Laravel Forge and DigitalOcean (?Apps Platform or Droplets?). I've read that Heroku and Laravel Vapor are a bit expensive. That's why I didn't consider them yet. I'd be happy to read your opinions on that topic!
Hi, I'm a beginner at using MySQL, I currently deployed my crud app on Heroku using the ClearDB add-on. I didn't see that coming, but the increased value of the primary key instead of being 1 is set to 10, and I cannot find a way to change it. Now I`m considering switching and deploying the full app and MySql to DigitalOcean any advice on that? Will I get the same issue? Thanks in advance!