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Cachely.dev VS Datapane

Compare Cachely.dev VS Datapane and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Cachely.dev logo Cachely.dev

Cachely is a managed implementation of self-hosted remote cache for monorepos. Speed up CI, prove how much time and cost you saved, get build optimization suggestions, safe from cache poisoning (CVE-2025-36852). Turborepo and Bazel on the roadmap.

Datapane logo Datapane

Datapane is an API-first platform for building reporting and BI tools using Python.
  • Cachely.dev Landing page
    Landing page //
    2026-08-01

Cachely is the managed self-hosted remote cache for Nx and Turborepo - the cache backend you'd otherwise build and run yourself, hosted for you on Cloudflare's edge (R2). It's a drop-in replacement for a DIY @nx/s3-cache / S3 bucket setup: point your build tool at Cachely with a token and two environment variables, and share build cache across CI and every developer's laptop.

Unlike a self-hosted cache, Cachely enforces read-only tokens at the API, so pull-request and fork builds can read but never write - closing the Nx cache-poisoning attack (CVE-2025-36852). It adds ROI reporting (the real build minutes and dollars the cache saved), per-tool insights, and build-optimization suggestions on top.

Pricing is a flat per-workspace subscription with no per-seat fees - add every developer, bot, and CI actor without watching the bill. Cachely never stores your source code; it caches only task outputs and their content hashes. Nx and Turborepo today; Bazel on the roadmap.

  • Datapane Landing page
    Landing page //
    2023-09-10

Cachely.dev features and specs

  • Simplified Caching Setup
    Cachely.dev likely offers an easy-to-integrate caching layer that reduces the complexity of manually configuring caching infrastructure, allowing developers to implement caching with minimal setup time.
  • Performance Improvement
    By providing a dedicated caching solution, Cachely.dev can help reduce latency and improve application response times, especially for frequently accessed data or API responses.
  • Developer-Focused Design
    The .dev domain and branding suggest the product is tailored specifically for developers, potentially offering clean APIs, SDKs, and documentation that fit into modern development workflows.
  • Scalability
    As a specialized caching service, it may be built to handle scaling automatically, removing the burden of managing cache infrastructure as traffic grows.
  • Reduced Backend Load
    Effective caching can significantly reduce the load on primary databases and backend services, potentially lowering infrastructure costs and improving overall system reliability.

Datapane features and specs

  • Easy Report Generation
    Datapane simplifies the process of creating and sharing interactive reports using Python, allowing users to convert Python scripts and Jupyter notebooks into dynamic reports easily.
  • Integration with Python
    Datapane integrates seamlessly with Python, which is beneficial for data scientists and analysts who already utilize Python in their data pipelines and analyses.
  • Interactive Elements
    Reports can include interactive elements such as plots, tables, and controls, providing a more engaging way to present complex data insights.
  • Deployment Options
    Datapane offers multiple deployment options, including a cloud service for easy sharing and collaboration, as well as the ability to host on-premises or on private infrastructure.
  • Privacy and Security
    Users concerned about data privacy and security can choose to deploy Datapane on their infrastructure, maintaining control over their data.

Possible disadvantages of Datapane

  • Learning Curve
    Users not familiar with Python or scripting may find it challenging to get started with Datapane, as it requires coding knowledge for report creation.
  • Limited to Python
    Organizations not using Python heavily in their workflows may find Datapane less adaptable, as it primarily targets Python users.
  • Cost Considerations
    Depending on the chosen deployment and scale, there might be cost implications, particularly for the cloud-hosted version of Datapane.
  • Feature Limitations
    Some advanced customization or feature requirements might exceed the capabilities of Datapane, necessitating the use of additional tools or services.

Cachely.dev videos

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Datapane videos

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Datapane Quick Overview

Category Popularity

0-100% (relative to Cachely.dev and Datapane)

Cachely.dev

Datapane

Productivity
29 29%
Productivity
71% 71
Business Intelligence
0 0%
Business Intelligence
100% 100
Developer Tools
25 25%
Developer Tools
75% 75
CI/CD
100 100%
CI/CD
0% 0

User comments

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

Based on our record, Datapane seems to be more popular. It has been mentiond 8 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.

Cachely.dev mentions (0)

We have not tracked any mentions of Cachely.dev yet. Tracking of Cachely.dev recommendations started around Jun 2026.

Datapane mentions (8)

  • How do you guys share R/Python based analyses to business stakeholders?
    PowerPoint will do. If there isn't too much data I will sometimes make a quick datapane html dashboard that I can also send their way. They like that, the plotly plots can be interactive so they can poke around. Nice quick solution that's easy to share. Source: almost 4 years ago
  • how do i convince data scientists to actually use my power bi dashboards?
    If you're going that route, check out Datapane - it's an open-source Python framework we're working on to create interactive reports from Plotly, Pandas, etc. Source: about 4 years ago
  • Ask HN: Who is hiring? (April 2022)
    Datapane | https://datapane.com | Remote (UK & Europe) Datapane is the frontend for the data science ecosystem. Our open-source library helps data scientists use the tools they love to create reports, dashboards, and apps for non-technical end-users. We are backed by some of the top investors in the world, and have grown to be the most popular way to create and share data science reports. We are proud to put the... - Source: Hacker News / over 4 years ago
  • Ask HN: Who is hiring? (January 2022)
    Datapane | https://datapane.com | Remote (Europe) Happy New Year! Datapane is the world's most popular way to create data science reports using Python. Our open-source framework is used by thousands of data scientists to create interactive reports, and our API-first platform serves over 50,000 people a month. We're a technical, remote team based in the UK and founded by YC alum and compsci PhDs. We're just closing... - Source: Hacker News / over 4 years ago
  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Datapane - API for building interactive reports in Python and deploying Python scripts and Jupyter Notebooks as self-service tools. - Source: dev.to / about 5 years ago
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What are some alternatives?

When comparing Cachely.dev and Datapane, you can also consider the following products

nxCloud - nxCloud is a commercial OwnCloud provider

ReportServer - In Reporting Services, URLs are used to access the Report Server Web service and the web portal. Before you can use either application, you must configure at least one URL each for the Web service and the web portal.

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