Pricing overview

GitHub ReadMe Stats is an open-source project designed to generate dynamic SVG images displaying GitHub statistics for inclusion in README files. The project operates under the MIT License, which permits free use, modification, and distribution. Consequently, there is no direct monetary cost associated with utilizing the core functionality of GitHub ReadMe Stats.

Users primarily interact with GitHub ReadMe Stats in two ways:

  1. Publicly Hosted Instance: The project maintainers provide a publicly accessible API endpoint, typically hosted on Vercel, which generates the SVG images on demand. Accessing this public endpoint is free for all users. However, usage is subject to rate limits imposed by the underlying GitHub API, which the service queries to fetch user data.
  2. Self-Hosting: Users can choose to deploy their own instance of GitHub ReadMe Stats on private or commercial cloud infrastructure. This approach offers greater control over performance, customization, and rate limits. While the software itself remains free, self-hosting incurs costs associated with the chosen cloud provider (e.g., server uptime, bandwidth, computational resources).

The absence of a direct licensing fee makes GitHub ReadMe Stats an accessible tool for individual developers, open-source projects, and organizations looking to enhance their GitHub profiles without incurring recurring software expenses. The primary financial consideration for most users centers on potential self-hosting infrastructure costs or adherence to the rate limits of the public service.

Plans and tiers

GitHub ReadMe Stats does not offer tiered pricing plans in the conventional sense, as it is a free and open-source project. Instead of commercial tiers, usage is differentiated by the method of deployment and the associated resource management. The two primary 'usage models' are:

  • Public API Access: This is the default and most common method of use. Developers embed image URLs directly into their GitHub READMEs. These URLs point to the publicly hosted instance of GitHub ReadMe Stats. This model is entirely free to use, and no registration or subscription is required. The service fetches data from GitHub's API on behalf of the user.
  • Self-Hosted Deployment: For users requiring higher performance, custom modifications, or wishing to bypass the rate limits of the public instance, self-hosting is an option. This involves deploying the GitHub ReadMe Stats codebase onto a server or serverless platform. While the software remains free, the user is responsible for all infrastructure costs.

The following table outlines the key characteristics of these two usage models:

Usage Model Price Key Considerations Best For
Public API Access Free
  • No setup required
  • Subject to GitHub API rate limits (60 requests/hour for unauthenticated, 5000 for authenticated requests through the service)
  • Shared resources, potential for slower response times during peak usage
  • Limited customization beyond URL parameters
  • Individual developers
  • Small open-source projects
  • Users with moderate traffic to their GitHub profiles
  • Quick and easy integration
Self-Hosted Deployment Variable (Infrastructure costs)
  • Requires server setup and maintenance
  • Full control over rate limits via personal GitHub token usage
  • Scalability depends on user's infrastructure choice
  • Enables deeper customization and potential for new features
  • Incurs costs for hosting, bandwidth, and compute resources (e.g., from AWS, Google Cloud, Azure, Vercel for private deployment)
  • Large organizations or projects
  • Users with high traffic or specific performance needs
  • Developers requiring custom features or themes
  • Those prioritizing data privacy and control

The choice between these models depends on the user's technical proficiency, traffic volume, and desire for customization and control. The project's official documentation provides detailed instructions for both public API usage and self-hosting.

Free tier and limits

The entire GitHub ReadMe Stats offering, when accessed via the public API, functions as a free tier. There are no paid upgrades or premium features. The core limitation stems from the underlying GitHub REST API rate limits. When the public GitHub ReadMe Stats instance fetches data, it operates within these constraints:

  • Unauthenticated Requests: GitHub typically limits unauthenticated requests to 60 requests per hour per IP address. If the public ReadMe Stats service makes too many unauthenticated requests from its shared IP range, it can hit this limit, potentially causing stats not to load.
  • Authenticated Requests: The public instance likely uses some form of authentication (e.g., a shared GitHub token) to increase its rate limit to 5,000 requests per hour. However, this is a shared resource across all users of the public API. High demand or abuse by a few users can consume this shared limit, affecting others.

For users who self-host GitHub ReadMe Stats, these limits can be managed more directly. By providing a personal GitHub token for authentication, a self-hosted instance can make up to 5,000 requests per hour, specific to that token. This offers a significantly higher dedicated limit compared to relying on the shared public instance.

The public instance also has a caching mechanism to mitigate rate limit issues. Statistics are cached for a period (e.g., 1 hour), meaning repeated requests for the same user's stats within that hour will serve cached data rather than making a new GitHub API call. This helps conserve the shared rate limit.

Real-world cost examples

Given GitHub ReadMe Stats's free and open-source nature, direct software costs are zero. Real-world costs primarily arise in self-hosting scenarios.

Example 1: Basic Public API Usage (No Cost)

  • Scenario: An individual developer wants to display their GitHub stats on their personal README. They use the default public API endpoints provided by the project.
  • Cost: $0.
  • Justification: The public service is free to use. The developer incurs no infrastructure or licensing fees. They are subject to the shared rate limits of the public instance.

Example 2: Self-Hosted on Vercel (Minimal Cost)

  • Scenario: A developer wants to self-host GitHub ReadMe Stats to ensure higher reliability and control over rate limits. They choose Vercel's hobby plan for deployment, as recommended in the project's documentation for ease of use.
  • Cost: Potentially $0 to a few dollars per month.
  • Justification: Vercel offers a generous Hobby tier that includes a significant free allowance for serverless functions, bandwidth, and builds. For a single-user or low-traffic self-hosted instance of GitHub ReadMe Stats, it's highly likely to remain within Vercel's free tier limits. If usage exceeds these limits (e.g., extremely high traffic to the README), Vercel's Pro plan starts at $20 per month, but this would be rare for a personal stats page.

Example 3: Self-Hosted on AWS Lambda (Variable Cost)

  • Scenario: A large open-source project or a company wants to self-host GitHub ReadMe Stats for multiple team members or a high-traffic project. They opt for AWS Lambda for serverless deployment, integrated with Amazon API Gateway.
  • Cost: $5 - $50+ per month, depending on invocation volume and data transfer.
  • Justification: AWS Lambda has a free tier that includes 1 million free requests and 400,000 GB-seconds of compute time per month. GitHub ReadMe Stats is a lightweight application, so a single instance would easily fit within this. However, if deployed for hundreds or thousands of users, or with continuous integration triggers, the costs would scale. API Gateway also has a free tier of 1 million requests, after which it costs approximately $3.50 per million requests. Data transfer out of AWS also incurs costs. For example, 10 million Lambda invocations could cost around $20, plus API Gateway and data transfer fees.

Example 4: Self-Hosted on Google Cloud Run (Variable Cost)

  • Scenario: A developer prefers Google Cloud Platform and deploys GitHub ReadMe Stats using Google Cloud Run, a serverless compute platform.
  • Cost: $2 - $30+ per month.
  • Justification: Google Cloud Run offers a free tier that includes 2 million requests, 360,000 GB-seconds, and 180,000 CPU-seconds per month. Similar to AWS Lambda, a single or low-traffic instance would likely stay within the free tier. Scaling up for high traffic would incur costs for additional requests, compute time, and outbound data transfer. For instance, 10 million requests could cost approximately $25, depending on the compute resources allocated per request and network egress.

How the pricing compares

GitHub ReadMe Stats stands out in its category due to its completely free and open-source licensing model. When comparing its pricing to alternatives, the primary distinction is the absence of subscription fees or commercial tiers.

  • Shields.io: Shields.io, a popular service for creating badges for various project metrics, is also free to use. It operates similarly by providing publicly accessible endpoints to generate SVG badges. Like GitHub ReadMe Stats, Shields.io relies on underlying third-party APIs (like GitHub's) and is subject to their rate limits. Both services offer core functionality without direct cost, making them comparable in their 'free' pricing model for public access.
  • GitHub Profile Trophy: GitHub Profile Trophy is another open-source project that generates SVG images, specifically focusing on displaying GitHub achievements and trophies. It shares the same open-source, free-to-use model as GitHub ReadMe Stats. Users can utilize a public instance or self-host without licensing fees. Its cost considerations are identical: free for public access, variable for self-hosting infrastructure.
  • GitHub Streak Stats: GitHub Streak Stats, which visualizes a user's GitHub contribution streak, also follows the open-source paradigm. It offers a public Vercel-hosted instance for free use and supports self-hosting. Functionally and financially, it aligns closely with GitHub ReadMe Stats.

In summary, the landscape of GitHub profile enhancement tools, particularly those generating dynamic SVG images based on GitHub data, is heavily dominated by free and open-source projects. GitHub ReadMe Stats's pricing model is consistent with these alternatives. The primary differentiator among these tools is not cost, but rather the specific types of statistics they display, their customization options, and their community support. For all these tools, any 'cost' is almost exclusively tied to the infrastructure expenses if a user chooses to self-host, rather than a direct payment for the software itself.