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PlanetScale VS Activeloop

Compare PlanetScale VS Activeloop and see what are their differences

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PlanetScale logo PlanetScale

The last database you'll ever need. Go from idea to IPO.

Activeloop logo Activeloop

Data lake for machine and deep learning. The fastest dataset management tool for computer vision.
  • PlanetScale Landing page
    Landing page //
    2023-10-15
  • Activeloop Landing page
    Landing page //
    2021-09-20

About

Activeloop provides an optimized format for unstructured data, so users can stream their machine learning datasets while training ML models in PyTorch and TensorFlow. Activeloop acts as a data lake for deep learning on unstructured data and offers in-browser dataset visualization, querying, and version control. On top of those features, Activeloop integrates with experimentation and labeling tools to allow rapid iteration on computer vision datasets.

Activeloop supports the following use cases:

Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:

  1. AgriTech
  2. Audio processing
  3. Autonomous Vehicles & Robotics
  4. Biomedical and Healthcare ML
  5. Multimedia: Image enhancement, video enhancement, face detection, sports analytics, or machine learning for AR/VR
  6. Safety & Security: surveillance machine learning with biometrics, facial recognition, or crowd counting

PlanetScale

Website
planetscale.com
Pricing URL
Official PlanetScale Pricing
$ Details
freemium $29 / Monthly (250GB)
Platforms
-
Release Date
-
Edit details

Activeloop

Website
activeloop.ai
Pricing URL
Official Activeloop Pricing
$ Details
$450 / Monthly (Growth Plan for up to 10 users)
Platforms
AWS GCP Python
Release Date
2019 July
Edit details

PlanetScale features and specs

  • Scalability
    PlanetScale is designed for massive scale, leveraging the Vitess engine that powers YouTube. This makes it suitable for applications requiring high scalability for both read and write operations.
  • Global Distribution
    Offers multi-region deployment, ensuring low-latency access and higher availability, beneficial for globally distributed applications.
  • Serverless Approach
    The platform takes a serverless approach to database management, which means automatic scaling, less infrastructure to manage, and potential cost savings.
  • Branching and Sharding
    Supports database branching for isolated environments like development, testing, and production. It also supports sharding, which helps in distributing data across multiple nodes for better performance and reliability.
  • High Availability
    PlanetScale provides high availability with automated failover mechanisms, ensuring minimal downtime.
  • Strong Data Integrity
    Uses Vitess’s strong consistency models to ensure data integrity across distributed systems.
  • Developer Friendly
    Includes tools and features that make it easier for developers to manage, such as automatic migrations and simplified schema management.
  • Integration
    Can be easily integrated with various cloud service providers, making it flexible for different deployment environments.

Possible disadvantages of PlanetScale

  • Learning Curve
    The platform comes with a learning curve, especially for teams unfamiliar with Vitess or managing distributed databases.
  • Cost
    While it can offer cost savings in some areas, the pricing for large-scale deployments and multi-region setups can be relatively high.
  • Complexity of Advanced Features
    Advanced features like sharding and branching can add complexity to the database management operations.
  • Limited Ecosystem
    Compared to more established databases, the ecosystem and community around PlanetScale might be smaller, which can affect the availability of third-party tools and community support.
  • Vendor Lock-in
    Using a proprietary platform can lead to vendor lock-in, making it harder to switch to other database services if needed.
  • Early-stage Platform
    While promising, PlanetScale is relatively new compared to some other established database services, which means it may lack some maturity or have bugs that older platforms have ironed out.

Activeloop features and specs

No features have been listed yet.

Analysis of PlanetScale

Overall verdict

  • PlanetScale is a strong choice for developers and companies looking for a scalable, reliable, and developer-friendly database solution. Its foundations on proven technology and modern features make it a good option for various use cases.

Why this product is good

  • PlanetScale is known for its serverless database platform designed to be simple, scalable, and efficient. It is built on Vitess, which powers companies like YouTube and Slack, offering great performance at scale. PlanetScale provides features such as branching, sharding, and horizontal scaling without downtime, appealing to developers who need robust infrastructure. Additionally, it's designed to integrate seamlessly with developer workflows, providing tools like a CLI and a web console for easy database management.

Recommended for

  • Developers building cloud-native applications
  • Teams needing scalable databases with no downtime
  • Organizations requiring seamless integration with existing development workflows
  • Startups and tech companies looking for robust infrastructure

Analysis of Activeloop

Overall verdict

  • Activeloop is a solid choice for teams working with large-scale AI/ML datasets, particularly those involving unstructured data like images, video, and audio, offering a specialized data infrastructure (Deep Lake) that streamlines dataset versioning, storage, and streaming for machine learning workflows.

Why this product is good

  • Deep Lake format enables efficient storage and streaming of large unstructured datasets directly to ML training pipelines without full downloads
  • Built-in version control for datasets, similar to Git, making it easier to track changes and collaborate on data
  • Native integrations with popular ML frameworks like PyTorch and TensorFlow, plus support for vector search and LLM-based applications
  • Cloud-agnostic storage options allowing flexibility across AWS, GCP, and other providers
  • Strong focus on performance optimization for data loading, reducing bottlenecks in training large models
  • Growing ecosystem with support for multimodal data types, useful for computer vision and generative AI projects

Recommended for

  • ML engineers and data scientists working with large-scale image, video, or audio datasets
  • Teams building computer vision or multimodal AI applications
  • Organizations needing dataset version control integrated into their ML pipeline
  • Developers building retrieval-augmented generation (RAG) or LLM applications requiring vector storage
  • Startups and enterprises looking to optimize data loading performance for deep learning training
  • Teams seeking an alternative to traditional data lakes for AI-specific workloads

PlanetScale videos

+ Add

PlanetScale Beta - Release Radar

More videos:

  • Review - Using PlanetScale (MySQL) with Next.js and Vercel!
  • Review - PlanetScale and Prisma: building in the cloud - Nick Van Wiggeren | Prisma Day 2021

Activeloop videos

+ Add

Activeloop Product Demo Video

Category Popularity

0-100% (relative to PlanetScale and Activeloop)

PlanetScale

Activeloop

Databases
100 100%
Databases
0% 0
Machine Learning
0 0%
Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Science
0 0%
Data Science
100% 100

User comments

Share your experience with using PlanetScale and Activeloop. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, PlanetScale seems to be a lot more popular than Activeloop. While we know about 105 links to PlanetScale, we've tracked only 4 mentions of Activeloop. 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.

PlanetScale mentions (105)

  • Ask HN: Who is hiring? (June 2026)
    PlanetScale | https://planetscale.com/ | Software Engineer - PlanetScale Postgres | Remote (AMER, LATAM & EMEA) | Base range: $120,000 - $290,000 USD I'm the hiring manager for this position. Come build the best Postgres product on the planet with super talented folks, very high autonomy, tier-zero databases for some of the biggest and fastest growing data sets. Apply at... - Source: Hacker News / 3 months ago
  • PlanetScale announces Postgres is GA
    i'll take the opposite side. I was very impressed with their website. The very first line: > The world’s fastest and most scalable cloud databases the second line: > PlanetScale brings you the fastest databases available in the cloud. Both our Postgres and Vitess databases deliver exceptional speed and reliability, with Vitess adding ultra scalability through horizontal sharding. I know exactly what they do. Zero... - Source: Hacker News / 11 months ago
  • Serverless Backend: A New Era for Developers
    Database: It helps storing, managing and retriving data in a structured manner (e.g. NeonDB, PlanetScale, DynamoDB). - Source: dev.to / over 1 year ago
  • Ask HN: What's the best free database provider out there?
    Https://planetscale.com/ would be a good bet. - Source: Hacker News / over 1 year ago
  • List of 45 databases in the world
    PlanetScale — Serverless database platform built on MySQL and Vitess. - Source: dev.to / about 2 years ago
View more

Activeloop mentions (4)

  • [P] I built a Chatbot to talk with any Github Repo. 🪄
    This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input. Source: over 3 years ago
  • [D] NLP has HuggingFace, what does Computer Vision have?
    u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :). Source: over 4 years ago
  • [P] Database for AI: Visualize, version-control & explore image, video and audio datasets
    Hub, our open-source package, lets you stream datasets while training to PyTorch/TensorFlow. Check out how we achieved 95% GPU utilization while training on ImageNet at 50% less cost. We're building the Database for AI, with everything it should contain. If there's an adjacent feature that would make it more useful for your workflow, do let us know! Source: over 4 years ago
  • [P] Database for AI: Visualize, version-control & explore image, video and audio datasets
    I'm Davit from Activeloop (activeloop.ai). Source: over 4 years ago

What are some alternatives?

When comparing PlanetScale and Activeloop, you can also consider the following products

Supabase - An open source Firebase alternative

Iterative.ai - Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.

Datomic - The fully transactional, cloud-ready, distributed database

Pachyderm - Pachyderm is an open source analytics engine that uses Docker containers for distributed computations.

Vercel - Vercel is the platform for frontend developers, providing the speed and reliability innovators need to create at the moment of inspiration.

Scale - Get human tasks done with just one line of code.

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