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Amazon SageMaker VS Simple Analytics

Compare Amazon SageMaker VS Simple Analytics and see what are their differences

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Amazon SageMaker logo Amazon SageMaker

Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Simple Analytics logo Simple Analytics

The privacy-first Google Analytics alternative located in Europe.
  • Amazon SageMaker Landing page
    Landing page //
    2023-03-15
  • Simple Analytics Landing page
    Landing page //
    2022-09-05

Simple Analytics gives you insights into the performance of your website without ever collecting personal data, with a clean interface, and simple integration. GDPR, CCPA and, PECR compliant because we don't handle personal data and set no cookies.

Amazon SageMaker features and specs

  • Fully Managed Service
    Amazon SageMaker is a fully managed service that eliminates the heavy lifting involved with setting up and maintaining infrastructure for machine learning. This allows data scientists and developers to focus on building and deploying machine learning models without worrying about underlying servers or infrastructure.
  • Scalability
    Amazon SageMaker provides scalable resources that can automatically adjust to the needs of your workload, ensuring that you can handle anything from small-scale experimentation to large-scale production deployments.
  • Integrated Development Environment
    SageMaker includes a built-in Jupyter notebook interface, which makes it straightforward for data scientists to write code, visualize data, and run experiments interactively without leaving the platform.
  • Support for Popular Machine Learning Frameworks
    SageMaker supports popular frameworks such as TensorFlow, PyTorch, Apache MXNet, and more. It also provides pre-built algorithms that can be used out-of-the-box, offering flexibility in choosing the right tool for your ML tasks.
  • Automatic Model Tuning
    SageMaker includes hyperparameter tuning capabilities that automate the process of finding the best set of hyperparameters for your model, thus saving significant time and computational resources.
  • Advanced Security Features
    SageMaker integrates with AWS Identity and Access Management (IAM) for fine-grained access control, supports encryption of data at rest and in transit, and complies with various security standards, ensuring that your machine learning projects are secure.
  • Cost Management
    With SageMaker, you only pay for what you use. This pay-as-you-go pricing model allows for better cost management and optimization, making it a cost-effective solution for various machine learning workloads.

Possible disadvantages of Amazon SageMaker

  • Complexity for New Users
    The plethora of features and options available in SageMaker can be overwhelming for beginners who are new to machine learning or the AWS ecosystem. It might require a steep learning curve to become proficient in using the platform effectively.
  • Vendor Lock-In
    Using Amazon SageMaker ties you to the AWS ecosystem, which can be a disadvantage if you want flexibility in switching between different cloud providers. Migrating models and workflows from SageMaker to another platform could be challenging.
  • Cost Management Challenges
    While SageMaker offers a pay-as-you-go pricing model, the costs can quickly add up, especially for large-scale or long-running tasks. It may require diligent monitoring and optimization to avoid unexpectedly high bills.
  • Resource Limitations
    While SageMaker is highly scalable, there are certain resource limits (like instance types and quotas) that might be restrictive for very high-demand or specialized machine learning tasks. These limits could potentially hinder the flexibility you get from an on-premises or custom deployed solution.
  • Integration Complexity
    Integrating SageMaker with other tools and systems within your workflow might require additional development effort. Custom integrations can be complex and could involve additional overhead to set up and maintain.

Simple Analytics features and specs

  • Privacy-focused
    Simple Analytics does not collect personal data, ensuring compliance with privacy laws like GDPR and CCPA. This approach appeals to users concerned about data privacy.
  • Ease of Use
    The platform prides itself on a user-friendly interface, making analytics accessible for individuals with varying levels of technical expertise.
  • No Cookies
    By eliminating the need for cookies, Simple Analytics reduces the complexity of compliance and improves user trust.
  • Transparent Pricing
    Offers straightforward pricing without hidden fees, which benefits small to medium-sized businesses looking for cost-effective solutions.
  • Quick Setup
    Setting up Simple Analytics is a quick process, often taking just a few minutes, reducing the time and effort required to begin tracking site data.
  • Lightweight Script
    The tracking script is lightweight, ensuring that it does not significantly affect website loading times, thus maintaining a good user experience.

Possible disadvantages of Simple Analytics

  • Limited Features
    Compared to more comprehensive platforms like Google Analytics, Simple Analytics offers fewer features and customization options, which may not satisfy advanced users.
  • Basic Reporting
    The reporting capabilities are basic and may not provide in-depth insights that large enterprises or data-driven teams may require.
  • No Integration with Ad Services
    Simple Analytics lacks built-in integrations with advertising services like Google Ads, potentially complicating the tracking of campaign performance.
  • Smaller User Community
    Given its niche market focus, the platform has a smaller user community, which can make it harder to find peer support or community-driven solutions.
  • Less Mature Ecosystem
    Unlike older platforms, Simple Analytics may lack integrations with a wide range of third-party tools and services, limiting its flexibility.
  • Cost
    While the pricing is transparent, it can still be seen as relatively high for the features offered, especially when compared to free alternatives like Google Analytics.

Analysis of Simple Analytics

Overall verdict

  • Simple Analytics is a good choice for users who prioritize privacy and simplicity in their web analytics tools. It provides sufficient insights for basic website analytics needs without overwhelming users with too much data or complex features.

Why this product is good

  • Simple Analytics is often praised for its privacy-focused approach. It does not collect personal data, which appeals to users and businesses concerned about privacy and compliance with data protection regulations like GDPR. The platform offers an easy-to-understand interface with essential analytics metrics, making it accessible to users without a technical background. Additionally, Simple Analytics is lightweight, which means it doesn't slow down websites as much as other analytics tools might.

Recommended for

    Simple Analytics is recommended for small to medium-sized businesses, bloggers, and website owners who need straightforward analytics and value privacy. It’s particularly suitable for those looking to comply with privacy regulations without compromising on user data protection.

Amazon SageMaker videos

+ Add

Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks

More videos:

  • Review - An overview of Amazon SageMaker (November 2017)

Simple Analytics videos

+ Add

Fathom, simple analytics. A Google Analytics alternative | Privacy & Simplicity focused! 🎯

More videos:

  • Review - Seriously Simple Analytics Review
  • Review - Seriously Simple Analytics Review
  • Demo - Why we created Simple Analytics

Category Popularity

0-100% (relative to Amazon SageMaker and Simple Analytics)

Amazon SageMaker

Simple Analytics

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Analytics
0 0%
Analytics
100% 100
AI
100 100%
AI
0% 0
Web Analytics
0 0%
Web Analytics
100% 100

User comments

Share your experience with using Amazon SageMaker and Simple Analytics. 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 Amazon SageMaker and Simple Analytics

Amazon SageMaker Reviews

7 best Colab alternatives in 2023
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning. It allows users to write code, track experiments, visualize data, and perform debugging and monitoring all within a single, integrated visual interface, making the process of developing, testing, and deploying models much more manageable.
Source: deepnote.com

Simple Analytics Reviews

Top 10 AI Data Analysis Tools in 2024
Simple Analytics is a revolutionary web analytics platform that prioritizes user privacy and transparency above all else. Developed as an ethical alternative to data-hungry giants like Google Analytics, Simple Analytics offers a refreshingly lightweight and user-friendly solution for tracking website metrics without compromising on data protection. With its unwavering...
Source: powerdrill.ai
Privacy-oriented alternatives to Google Analytics
Simple Analytics was my original second contender for the analytics of this blog. The $19 a month starting plan with 100k pageviews is on the more expensive side, but their yearly deal gets you a better price than Fathom at just $9 a month.
Source: nts.strzibny.name
Lightweight alternatives to Google Analytics
One is the minimalist Simple Analytics product, which is a cloud-based tool created by solo developer Adriaan van Rossum; it has a clean-looking interface with only the few key metrics, similar to Plausible. Another is Fathom, which was open source initially, but the current version is proprietary (although the company hopes to start maintaining the open-source code base...
Source: lwn.net

Social recommendations and mentions

Based on our record, Amazon SageMaker should be more popular than Simple Analytics. It has been mentiond 44 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.

Amazon SageMaker mentions (44)

  • Dashboard for Researchers & Geneticists: Functional Requirements [System Design]
    Leverage Amazon SageMaker: For machine learning (ML) tasks, users can leverage Amazon SageMaker to analyze large datasets and build predictive models. - Source: dev.to / about 2 months ago
  • Address Common Machine Learning Challenges With Managed MLflow
    MLflow, an Apache 2.0-licensed open-source platform, addresses these issues by providing tools and APIs for tracking experiments, logging parameters, recording metrics and managing model versions. It also helps to address common machine learning challenges, including efficiently tracking, managing, deploying ML models and enhancing workflows across different ML tasks. Amazon SageMaker with MLflow offers secure... - Source: dev.to / 3 months ago
  • How I suffered my first burnout as software developer
    Our first task for the client was to evaluate various MLOps solutions available on the market. Over the summer of 2022, we conducted small proofs-of-concept with platforms like Amazon SageMaker, Iguazio (the developer of MLRun), and Valohai. However, because we weren’t collaborating directly with the teams we were supposed to support, these proofs-of-concept were limited. Instead of using real datasets or models... - Source: dev.to / 5 months ago
  • 👋🏻Goodbye Power BI! 📊 In 2025 Build AI/ML Dashboards Entirely Within Python 🤖
    Taipy’s ecosystem doesn’t stop at dashboards. With Taipy you can orchestrate data workflows and create advanced user interfaces. Besides, the platform supports every stage of building enterprise-grade applications. Additionally, Taipy’s integration with leading platforms such as Databricks, Snowflake, IBM WatsonX, and Amazon SageMaker ensures compatibility with your existing data infrastructure. - Source: dev.to / 6 months ago
  • Understanding the MLOps Lifecycle
    Based on your technological stack, various services are used to deploy machine learning models. Some popular services are AWS Sagemaker, Azure Machine Learning, Vertex AI, and many others. - Source: dev.to / 6 months ago
View more

Simple Analytics mentions (26)

  • This Next.js blog template is awesome.
    Multiple analytics options including Umami, Plausible, Simple Analytics, Posthog and Google Analytics. - Source: dev.to / 8 months ago
  • Awesome-no-code-tools
    Simple Analytics - Simple, clean, and friendly analytics. - Source: dev.to / 11 months ago
  • SaasRock v0.5.0 - Cookie consent and built-in Analytics
    SaasRock does not intend to invent the wheel, there are great analytics solutions out there, both free and powerful. But SaasRock’s main goal is to have everything you need when building SaaS applications, at least in a minimal way. - Source: dev.to / almost 3 years ago
  • Italian watchdog bans use of Google Analytics
    Regarding forbidden countries, it’s not forbidden in the Netherlands, yet. They will announce a verdict in a form of a report by the end of 2022 [1]. To give people an option and pink something else over Google Analytics, I have built an alternative, Simple Analytics [2]. It doesn’t use cookies or any form of tracking and you get still the useful data that 80% of the website owners need. [1]... - Source: Hacker News / almost 3 years ago
  • Italian watchdog bans use of Google Analytics
    It is. Most startups in the EU have to use more and more businesses in the EU. The selection is little, so way more changes to succeed if your EU based and serve both markets. I run Simple Analytics [1], which is a privacy-first analytics business from the Netherlands. I see a lot of business from the EU just because we are from the EU as well. [1] https://simpleanalytics.com/?ref=hn. - Source: Hacker News / almost 3 years ago
View more

What are some alternatives?

When comparing Amazon SageMaker and Simple Analytics, you can also consider the following products

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

Plausible.io - Plausible Analytics is a simple, open-source, lightweight (< 1 KB) and privacy-friendly web analytics alternative to Google Analytics. Made and hosted in the EU, powered by European-owned cloud infrastructure 🇪🇺

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.

Fathom Analytics - Simple, trustworthy website analytics (finally)

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.

Google Analytics - Improve your website to increase conversions, improve the user experience, and make more money using Google Analytics. Measure, understand and quantify engagement on your site with customized and in-depth reports.

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