Zenndra API: A third-party solution for Medium data access

This review evaluates Zenndra, a third-party REST API designed to provide programmatic access to Medium data, contrasting it with DIY scraping and Medium's limited official API.

TL;DR

Best for: Developers and businesses requiring programmatic read access to public Medium content, such as user profiles, articles, tags, or search results, where Medium's official API is insufficient.

Skip if: Your sole need is to publish content to Medium programmatically, or if you prefer to build and maintain a custom scraping solution despite the known challenges.

Bottom line: Zenndra positions itself as a comprehensive, stable alternative for Medium data extraction, addressing a clear gap left by the platform's own API limitations.

METHODOLOGY

This v0 review draws on the founder's published claims at the provided source URL; independent benchmarks are pending. Update cadence: re-tested when claims diverge from observed behavior.

This review covers Zenndra API, observed as of May 29, 2026, based on information from a dev.to blog post titled "Medium API in 2026: Scraping vs Official API vs Zenndra (Honest Comparison)" by devto (likely Zenndra itself). The review covers the founder's claims regarding Zenndra's features, its claimed performance, and the stated limitations of alternative approaches (DIY scraping and Medium's official API). We analyze the technical details presented, including example curl commands and claimed response structures.

What is NOT covered in this v0 review includes independent performance benchmarks, long-term workflow integration, real-world reliability under load, data freshness, or comprehensive edge case testing. Our assessment relies solely on the information presented in the self-published comparison.

WHAT IT DOES

Zenndra is presented as a third-party REST API designed to overcome the limitations of accessing Medium data programmatically. The service aims to provide a stable, structured interface for data that is otherwise difficult to obtain.

Comprehensive data access

The core offering of Zenndra is its breadth of data access. The source claims 42 endpoints covering various aspects of Medium, including articles, users, publications, tags, feeds, and search functionalities. This contrasts sharply with Medium's official API, which is primarily restricted to authenticated write operations (creating posts, getting your own profile, publishing to owned publications) and cannot fetch arbitrary user articles, search content, or access follower graphs.

Stable JSON responses

Unlike DIY scraping, which the source describes as yielding "a mess of React-rendered markup with no stable selectors," Zenndra promises clean, stable JSON responses. This is a critical claim for developers seeking reliable data integration without the constant maintenance burden associated with parsing frequently changing HTML structures. The provided curl examples illustrate structured JSON outputs for user profiles, including fields like username, name, followers, and bio.

Claimed performance

The source highlights a claimed response time of "200 OK in ~142ms" for fetching a user's profile via Zenndra's API. This metric, if accurate and consistent, suggests a performant solution for data retrieval, which is important for applications requiring timely access to Medium content. The API uses a Bearer token for authorization, a standard practice for REST APIs.

WHAT'S INTERESTING / WHAT'S NOT

What's interesting about Zenndra is its direct approach to solving a well-documented problem: the lack of a robust, public-facing API for Medium. The claim of 42 specific endpoints is significant, indicating a broad coverage of Medium's data landscape, from user profiles and articles to trending feeds and search. This level of granularity, combined with the promise of stable JSON output, directly addresses the pain points of developers who have previously resorted to fragile scraping solutions or found Medium's official API inadequate. The stated response time of ~142ms for a user profile fetch, while a founder's claim, points to an ambition for low-latency data delivery.

What's not particularly novel is the critique of DIY scraping and Medium's official API. The challenges of maintaining scrapers against dynamic frontends and the severe limitations of Medium's own API are widely known within the developer community. The article, being a self-published comparison by Zenndra, naturally frames these alternatives in the worst possible light to highlight Zenndra's value proposition. While the problem statement is accurate, the "honest comparison" is inherently biased towards the product being promoted. A critical missing piece from the founder's pitch is any detail on pricing, rate limits, data freshness guarantees, or how Zenndra handles potential legal or ethical implications of scraping Medium at scale.

PRICING

Pricing information for Zenndra is not available in the source material. The review is based on data accessed on May 29, 2026.

VERDICT

Zenndra positions itself as the most viable option for developers needing extensive, programmatic read access to Medium data in 2026. While DIY scraping is technically free, its high maintenance cost due to Medium's constantly changing frontend makes it impractical for most production use cases. Medium's official API is functionally useless for read-heavy applications, limited almost exclusively to authenticated publishing. Zenndra's claimed 42 endpoints and stable JSON output directly address these shortcomings, offering a structured and potentially reliable data source. If your project requires fetching arbitrary Medium content, Zenndra appears to be the most direct solution available, assuming its claims of stability and performance hold up under independent verification.

WHAT WE'D TEST NEXT

For a v2 review, our immediate focus would be on independently verifying Zenndra's core claims. We would benchmark the actual response times across a variety of endpoints, not just user profiles, and assess consistency under different load conditions. We would also test the stability of the JSON schema over time, specifically monitoring for breaking changes or unexpected data formats. A critical area of investigation would be the freshness of the data provided by Zenndra, comparing it against live Medium content to understand any potential lag. Finally, we would evaluate the cost-effectiveness of Zenndra once pricing details become available, comparing it against the operational costs of maintaining a robust in-house scraping solution for specific data needs.