Software Alternatives, Accelerators & Startups

neptune.ai VS Dashbird

Compare neptune.ai VS Dashbird and see what are their differences

neptune.ai logo neptune.ai

Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Dashbird logo Dashbird

End-to-end observability & debugging platform for serverless applications.
  • neptune.ai Landing page
    Landing page //
    2023-08-24

Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.

Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code

Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

  • Dashbird Landing page
    Landing page //
    2023-08-27

Dashbird is an observability, debugging, and intelligence platform designed specifically to help serverless developers build, operate, improve, and scale their modern cloud applications on AWS environment fast, securely, and with ease. It’s free to use for up to 1M invocations and doesn’t require any code changes.

Dashbird fills the gaps left by CloudWatch and other traditional monitoring tools by offering enhanced out-of-the-box monitoring, operations, and actionable insights tools for architectural improvements, all in one place.

Full observability covered for AWS services: Lambda, API Gateway, DynamoDB, SQS, ECS, Step Functions, Kinesis, HTTP API Gateway, RDS, SNS, OpenSearch, ELB.

Dashbird’s approach is fairly simple, all the mission-critical data of your entire serverless system is placed in a single dashboard giving you a birds-eye-view of the entire system activity. Moreover, you get immediate alerts on any errors or warnings that may arise and get pointed to the exact point of failure in the system so it can be resolved fast.

The 3 core pillars of Dashbird are:

Real-time end-to-end serverless observability Automatic Failure Detection Continuous Well-Architected reports on your entire infrastructure

neptune.ai

Website
neptune.ai
$ Details
freemium
Platforms
Python
Release Date
2018 April
Startup details
Country
Poland
State
Mazowieckie
City
Warsaw
Founder(s)
Piotr Niedzwiedz
Employees
10 - 19

Dashbird

$ Details
-
Platforms
-
Release Date
-
Startup details
Country
Estonia
City
Tallinn
Employees
1 - 9

neptune.ai features and specs

No features have been listed yet.

Dashbird features and specs

  • Serverless observability: yes
  • Error and warning alerting: yes
  • Well-Architected Reports: yes
  • Quick log search: yes

neptune.ai videos

Machine Learning Experiment Management with Neptune.ai - How to start

Dashbird videos

Dashbird explained

Category Popularity

0-100% (relative to neptune.ai and Dashbird)
Data Science And Machine Learning
AWS Lambda
0 0%
100% 100
Data Science Notebooks
100 100%
0% 0
Monitoring Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare neptune.ai and Dashbird

neptune.ai Reviews

  1. Easy to use, not overdone, good for model management and collab

    Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group

Dashbird Reviews

We have no reviews of Dashbird yet.
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Social recommendations and mentions

Based on our record, Dashbird should be more popular than neptune.ai. It has been mentiond 59 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.

neptune.ai mentions (23)

  • A step-by-step guide to building an MLOps pipeline
    Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach. - Source: dev.to / 15 days ago
  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / 4 months ago
  • Show HN: A gallery of dev tool marketing examples
    Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / 9 months ago
  • How to structure/manage a machine learning experiment? (medical imaging)
    There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: 10 months ago
  • How to grow a developer blog to 3M annual visitors? with Jakub Czakon (Neptune.ai)
    Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 10 months ago
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Dashbird mentions (59)

  • Monitor Your AWS AppSync GraphQL APIs with Simplicity
    There's more to come at Dashbird, as we're already building more features to help you run the best possible AppSync endpoints. This includes a set of well-architected insights to guide you with best practices. - Source: dev.to / almost 2 years ago
  • An Introduction to Function as a Service (FaaS)
    Observability in serverless Tools like Datadog, Splunk, Thundra.io, New Relic, and Dashbird make monitoring and debugging serverless applications easy. They collect metrics, logs, and traces from AWS Cloudwatch and X-ray. - Source: dev.to / almost 2 years ago
  • Why and how to monitor Amazon API Gateway HTTP APIs
    With its latest release, Dashbird added support for APIG's HTTP APIs. All your HTTP APIs are automatically monitored after installing Dashbird into your AWS account. You need to deploy a CloudFormation template to set up Dashbird integration; it doesn't require any code changes! - Source: dev.to / almost 2 years ago
  • Serverless monitoring — the good, the bad and the ugly
    I decided to try out Dashbird because it’s free and seems promising. They’re not asking for a credit card either, making it a “why not try it out” situation. - Source: dev.to / about 2 years ago
  • We can do better failure detection in serverless applications
    With the emergence of managed and distributed services, the monitoring landscape will have to go through a significant change to keep up with modern cloud applications. Currently, devops overhead is one of the biggest obstacles for companies looking to use serverless in production and rely on it for mission-critical applications. Our team at Dashbird is hoping to solve that one problem at a time. - Source: dev.to / about 2 years ago
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What are some alternatives?

When comparing neptune.ai and Dashbird, you can also consider the following products

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

Lumigo - With one-click distributed tracing, Lumigo lets developers effortlessly find and fix issues in serverless and microservices environments.

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

Epsagon - Track costs and fix your serverless application.

Weights & Biases - Developer tools for deep learning research

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.