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Datadog VS H2O.ai

Compare Datadog VS H2O.ai and see what are their differences

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Datadog logo 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.

H2O.ai logo H2O.ai

Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.
  • Datadog Landing page
    Landing page //
    2023-10-05

Datadog is a monitoring and analytics platform for cloud-scale application infrastructure. Combining metrics from servers, databases, and applications, Datadog delivers sophisticated, actionable alerts, and provides real-time visibility of your entire infrastructure. Datadog includes 100+ vendor-supported, prebuilt integrations and monitors hundreds of thousands of hosts.

  • H2O.ai Landing page
    Landing page //
    2023-10-25

Datadog

Website
datadoghq.com
Pricing URL
Official Datadog Pricing
$ Details
freemium $15.0 / Monthly (per host)
Platforms
Browser REST API
Release Date
-
Edit details

H2O.ai

Website
h2o.ai
Pricing URL
-
$ Details
Platforms
-
Release Date
2012 January
Startup details
Country
United States
State
California
City
Mountain View
Founder(s)
Cliff Click
Employees
10 - 19
Edit details

Datadog features and specs

  • Comprehensive Monitoring
    Datadog offers a wide range of monitoring capabilities including infrastructure, application performance, log management, and user experience monitoring. This provides a unified view across the entire tech stack.
  • Integration Ecosystem
    With over 400 integrations available, Datadog can easily connect with virtually any service, application, and technology stack, making it highly versatile.
  • Scalability
    Datadog is designed to scale from small startups to large enterprises, providing functionalities that cater to varied sizes and complexities of operations.
  • Real-Time Data
    The platform provides real-time data and analytics, which is crucial for diagnosing and troubleshooting issues as they arise.
  • Alerting and Notifications
    Advanced alerting and notification features allow users to set up custom alerts based on metrics, enabling proactive problem resolution.
  • User-Friendly Interface
    The user interface is intuitive and easy to navigate, even for those who are not particularly technical, making it accessible to a broader range of users.
  • Security Features
    Datadog includes various security features such as compliance tracking, threat detection, and anomaly detection, enhancing overall security posture.

Possible disadvantages of Datadog

  • Cost
    Datadog can become quite expensive, especially as the volume of monitored data and the number of integrations increases. This can be a limiting factor for smaller businesses.
  • Complexity
    With its extensive feature set, Datadog can be overwhelming for new users, requiring a steep learning curve to master all functionalities.
  • Data Retention
    The default data retention period is often shorter than what some organizations require, leading to additional costs for longer retention.
  • Performance Overhead
    The extensive data collection and monitoring capabilities can add performance overhead to the monitored systems, potentially impacting their performance.
  • Customization Limitations
    While Datadog provides extensive dashboards and visualizations, some users find the customization options to be limited compared to other monitoring solutions.
  • Support
    Some users have reported that the customer support can be slow or insufficient at times, which could be a downside when facing critical issues.

H2O.ai features and specs

  • Open Source
    H2O.ai provides open-source machine learning and AI tools that allow developers and data scientists to access and modify the source code, enabling greater customization and transparency.
  • AutoML
    H2O.ai's AutoML functionality significantly reduces the time and effort required to build and deploy machine learning models by automating key parts of the data science workflow.
  • Scalability
    The platform is designed to handle large datasets efficiently, both on single machines and in distributed environments, making it suitable for enterprise-level applications.
  • Wide Range of Algorithms
    H2O.ai supports a diverse set of machine learning algorithms, including deep learning, gradient boosting, and generalized linear modeling, among others.
  • Integration
    It seamlessly integrates with popular data science tools and platforms, such as R, Python, and Spark, facilitating ease of use in existing workflows.
  • Enterprise Support
    H2O.ai offers enterprise-level support and additional features through its Driverless AI product, which can be attractive for businesses seeking professional services and scalability.

Possible disadvantages of H2O.ai

  • Learning Curve
    The platform can have a steep learning curve for beginners, particularly those who are not familiar with programming or data science concepts.
  • Cost
    While the open-source version is free, the enterprise version (Driverless AI) comes with a significant cost, which may be prohibitive for smaller organizations or individual practitioners.
  • Resource Intensive
    The platform can be resource-intensive, requiring substantial computational power and memory, potentially limiting its accessibility to those with high-end hardware or cloud resources.
  • Complexity
    Despite the AutoML features, advanced users may find certain functionalities and customizations complex, necessitating deep technical knowledge and experience.
  • Limited Visualization Tools
    Compared to some competitors, H2O.ai offers fewer built-in data visualization tools, which may necessitate the use of additional software to fully understand and interpret data.

Analysis of Datadog

Overall verdict

  • Datadog is generally considered a good choice for organizations needing a comprehensive monitoring solution that provides deep insights across various aspects of their technology stack. Its scalability and integration capabilities make it appealing for businesses of all sizes, especially those leveraging cloud services.

Why this product is good

  • Datadog is a powerful monitoring and analytics platform that provides comprehensive visibility into cloud-scale applications. It's known for its robust set of features, including infrastructure monitoring, application performance management, log management, and security monitoring. Datadog's ability to integrate with a vast array of services and technologies makes it a versatile tool for organizations looking to monitor complex systems. Furthermore, its real-time dashboards and alerting capabilities help teams quickly identify and address performance issues, improving reliability and efficiency.

Recommended for

  • Organizations using multiple cloud services and wanting unified monitoring.
  • IT teams looking for a detailed application performance management solution.
  • Businesses that require scalable monitoring for dynamic environments.
  • Companies seeking robust alerting and automation capabilities for infrastructure and application management.

Datadog videos

+ Add

Datadog Review & Walkthrough

More videos:

  • Review - DataDog: What it is and where its going
  • Review - Datadog: 2-Minute Tour

H2O.ai videos

+ Add

H2O Just Add Water was the weirdest show...

More videos:

  • Review - [Demo] Predicting Healthcare Outcomes with H2O.ai
  • Review - H2O wireless phone service full review 2019
  • Review - H2O Wireless:IS IT WORTH IT Review?
  • Review - H2O.ai VS. OBSERVE.ai: What The AI Race To Market Means
  • Review - H2O.ai Launches H2OGPT and LLM Studio: Build Your Own Enterprise Grade Chatbots

Category Popularity

0-100% (relative to Datadog and H2O.ai)

Datadog

H2O.ai

Monitoring Tools
100 100%
Monitoring Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Log Management
100 100%
Log Management
0% 0
AI
0 0%
AI
100% 100

Questions and Answers

As answered by people managing Datadog and H2O.ai.

What makes your product unique?

H2O.ai's answer:

At H2O.ai, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI.

User comments

Share your experience with using Datadog and H2O.ai. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Datadog and H2O.ai

Datadog Reviews

The 10 Best Nagios Alternatives in 2024 (Paid and Open-source)
10 Best Datadog Alternatives to Consider in 2023 Datadog is one of the most potent and versatile players on the market, but they have their fair share of downsides. The monitoring and observability space is quite competitive, so we will discuss 10 of the best Datadog alternatives and compare their pros and cons to determine which is better suited for your needs.
Source: betterstack.com
Top 10 Grafana Alternatives in 2024
While all Grafana alternatives do not offer pricing transparency, go for a flexible pricing structure that fits your budget. Tools like Datadog offer pricing based on data volume or monitoring scope, while Middleware offers a flexible pay-as-you-go pricing structure.
Source: middleware.io
Top 11 Grafana Alternatives & Competitors [2024]
Open Source vs. Proprietary: Determine whether an open-source solution like SigNoz or a proprietary one like Datadog better aligns with your requirements and budget. Open-source tools often offer more customization and community support, while proprietary tools may provide more comprehensive out-of-the-box features and dedicated customer service. At SigNoz, we offer both...
Source: signoz.io
10 Best Grafana Alternatives [2023 Comparison]
Datadog is a massive tool that offers a lot of features and solutions, including log management. But before we dive too deep, please note that Datadog is expensive. It absolutely is not for anyone other than large-budgeted corporations. Just take a look at what people are saying on X.
Source: sematext.com
5 Best DevSecOps Tools in 2023
There are many platforms that can be utilized for monitoring and alerting. Some examples are New Relic, Datadog, AWS CloudWatch, Sentry, Dynatrace, and others. Again, these providers each have pros and cons related to pricing, offering, ad vendor lock-in. So research the options to see what may possibly be best for a given situation.
Source: devopsauthority.tech

H2O.ai Reviews

Top 7 Predictive Analytics Tools
If a company is interested in an open-source predictive analytics tool with data mining features, put H2O at the top of the list. It offers fast performance, affordability, advanced capabilities, and extreme flexibility. The dashboard for H2O offers a veritable smorgasbord of actionable insights. However, this tool is more for the expert data science crowd than for citizen...
Source: www.datamation.com
The 16 Best Data Science and Machine Learning Platforms for 2021
Description: H2O.ai offers a number of AI and data science products, headlined by its commercial platform H2O Driverless AI. Driverless AI is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep...
Source: solutionsreview.com

Social recommendations and mentions

Based on our record, Datadog should be more popular than H2O.ai. It has been mentiond 5 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.

Datadog mentions (5)

  • Send the logs of your Shuttle-powered backend to Datadog
    Ideally, if we had access to the underlying infrastructure, we could probably install the Datadog Agent and configure it to send our logs directly to Datadog, or even use AWS Lambda functions or Azure Event Hub + Azure Functions in case we were facing some specific cloud scenarios. - Source: dev.to / over 1 year ago
  • I wanted a self hosted alternative to Atlassian status page so I build my own application !
    Currently supported : Datadog, Jenkins, DNS, HTTP. Source: over 2 years ago
  • Datadog on Kubernetes: Avoiding Common Pitfalls
    Datadog is a powerful monitoring and security platform that gives you visibility into end-to-end traces, application metrics, logs, and infrastructure. While Datadog has great documentation on their Kubernetes integration, we've observed that there's some missed nuance that leads to common pitfalls. - Source: dev.to / almost 4 years ago
  • Post-DockerCon spam
    .. Is to see you email address being silently distributed to every single company that I've watched a talk from. And now suddenly get several promotional spam emails per day from some 4-5 different domains like instana.com, datadoghq.com, snyk.io, cockroachlabs.com (some of them send even multiple emails per day!). Source: almost 4 years ago
  • Never write a UserService again
    We're commonly doing this with logging, using services such as Loggly or DataDog. We're using managed databases, be it on AWS, Heroku or database-vendor-specific solutions. We're storing binaries on S3. Externalising user authentication and authorization might be a good candidate as well. - Source: dev.to / about 4 years ago

H2O.ai mentions (3)

  • Major Technologies Worth Learning in 2025 for Data Professionals
    Artificial Intelligence (AI) is becoming a ubiquitous, and dare I say, indispensable part of data workflows. Tools like ChatGPT have made it easier to review data and write reports. But diving even deeper, tools like DataRobot, H2O.ai, and Google’s AutoML are also simplifying machine learning pipelines and automating repetitive tasks, enabling professionals to focus on high-value activities like model optimization... - Source: dev.to / 6 months ago
  • AI Democratization: Unlocking the Power of Artificial Intelligence for All
    Open-Source AI Frameworks: Open-source tools like TensorFlow, PyTorch, and H2O.ai allow developers to build and share AI models. These frameworks are freely available, fostering collaboration and innovation within the AI community. - Source: dev.to / 7 months ago
  • Nginx is now the most popular web server, overtaking Apache
    How about H2O? It's supposed to be significantly faster than Nginx: https://h2o.examp1e.net/. - Source: Hacker News / about 4 years ago

What are some alternatives?

When comparing Datadog and H2O.ai, you can also consider the following products

Zabbix - Track, record, alert and visualize performance and availability of IT resources

datarobot - Become an AI-Driven Enterprise with Automated Machine Learning

Dynatrace - Cloud-based quality testing, performance monitoring and analytics for mobile apps and websites. Get started with Keynote today!

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.

RapidMiner - RapidMiner is a software platform for data science teams that unites data prep, machine learning, and predictive model deployment.

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