Software Alternatives, Accelerators & Startups

Metaflow VS Deployment.io

Compare Metaflow VS Deployment.io and see what are their differences

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.

Deployment.io logo Deployment.io

Deployment.io makes it super easy for startups and agile engineering teams to automate application deployments on AWS cloud.
  • Metaflow Landing page
    Landing page //
    2023-03-03
  • Deployment.io deployment home
    deployment home //
    2024-03-23
  • Deployment.io deployment repositories
    deployment repositories //
    2024-03-23
  • Deployment.io deployment environments
    deployment environments //
    2024-03-23
  • Deployment.io deployment deployments
    deployment deployments //
    2024-03-23

Deployment simplifies continuous code integration and delivery automation for startups and agile engineering teams on the AWS cloud, eliminating the need for DevOps engineering. A developer can deploy static sites, web services, and environments without knowledge of AWS or DevOps. Deployment supports previews on pull requests and automatic deployments on code push without manual setup or scripting. It enables engineering teams to focus on tasks that add customer value instead of worrying about DevOps-related grunt work.

Metaflow

Pricing URL
-
$ Details
Platforms
-
Release Date
-

Deployment.io

$ Details
freemium
Platforms
AWS GitHub GitLab
Release Date
2024 February

Metaflow features and specs

No features have been listed yet.

Deployment.io features and specs

  • Automatic Deployments: Automated deployments to AWS cloud
  • Previews: Previews deployed to AWS on pull requests
  • Slack Alerts: Slack alerts for for any updates to deployments
  • Unlimited static sites: Deploy static sites with one click without any AWS setup
  • Unlimited web services: Deploy web services and backend APIs without any AWS setup
  • Unlimited environments: Create development, staging, and production environments on the fly on your AWS account
  • Unlimited repositories: Connect your GitHub and GitLab repositories for automated CI/CD

Metaflow videos

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Deployment.io videos

Deploying a Golang API on AWS using deployment.io

Category Popularity

0-100% (relative to Metaflow and Deployment.io)
Workflow Automation
100 100%
0% 0
DevOps Tools
39 39%
61% 61
CI/CD
0 0%
100% 100
Workflows
100 100%
0% 0

Questions and Answers

As answered by people managing Metaflow and Deployment.io.

What's the story behind your product?

Deployment.io's answer:

I led engineering teams at early-stage startups and realized that startups waste 70% of valuable engineering time on tedious, non-coding tasks that they can easily automate.

To solve this problem, we've built Deployment.io so engineering teams at startups can focus on writing more code that adds value and helps them achieve PMF faster.

Which are the primary technologies used for building your product?

Deployment.io's answer:

ReactJs using Typescript, GatsbyJs using Typescript, GoLang, and AWS

What makes your product unique?

Deployment.io's answer:

Deployment.io is built and designed for startups. Our customers can onboard in 5 minutes and start deploying apps to AWS without any DevOps or AWS knowledge. Other platforms are complex and require scripting or DevOps knowledge. They are built for bigger companies with a lot of resources.

Why should a person choose your product over its competitors?

Deployment.io's answer:

Startups and agile engineering teams should choose Deployment.io for the simplicity and ease of use. Our competitors are complex and are designed for bigger companies.

How would you describe your primary audience?

Deployment.io's answer:

For startups, speed and focus are crucial. Our primary audience is engineering teams at startups that want to focus on building code that adds value and not on DevOps related grunt work.

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Metaflow and Deployment.io

Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Deployment.io Reviews

  1. Super easy deployments to AWS

    Deploying web apps on AWS has never been this easy and it also takes care of scaling based on usage.

Social recommendations and mentions

Based on our record, Metaflow seems to be more popular. It has been mentiond 12 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Metaflow mentions (12)

  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 1 year ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 1 year ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: over 1 year ago
  • [OC] Gender diversity in Tech companies
    They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural... Source: over 1 year ago
  • Going to Production with Github Actions, Metaflow and AWS SageMaker
    Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project. - Source: dev.to / almost 2 years ago
View more

Deployment.io mentions (0)

We have not tracked any mentions of Deployment.io yet. Tracking of Deployment.io recommendations started around Mar 2024.

What are some alternatives?

When comparing Metaflow and Deployment.io, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Harness - Automated Tests For Your Web App

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development

DepHell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump ver...

GitHub Actions - Automate your workflow from idea to production