X Platform API Adjustments Effective April 20, Own Reads Reduced to $0.001 Each
X has announced a new round of API pricing adjustments, effective April 20, with the most significant change being the billing for "Owned Reads" at $0.001 per call, equivalent to 1 dollar for 1000 calls. This tier covers developers reading their own account posts, mentions, likes, favorites, followers, and 12 GET endpoints.
The write endpoints have also been adjusted, with the main POST /2/tweets interface increasing from $0.01 to $0.015 per tweet, and posts with URLs priced at $0.20 each, while replies remain at the old price of $0.01; self-service tier accounts will also disable follow/unfollow, like/unlike, and quote retweet operations. Tesla and xAI CEO Elon Musk responded that developers can access through the open-source agent gateway OpenClaw, expressing a desire to maintain low prices but cannot provide resources for free.
Source: Public Information
ABAB AI Insight
This price adjustment significantly lowers the cost of accessing owned data while tightening automated growth actions, creating a differentiated incentive structure. The reduction in Owned Reads allows lightweight agents to build applications around user lists, favorites, and follower relationships at a low cost, accelerating the development of decentralized tools; the price increase for URL posts and the disabling of self-service interactions specifically target the suppression of large-scale automated traffic and fake interactions, reducing the risk of low-cost abuse of platform data.
This adjustment corresponds to the platform's recalibration of data flow and growth mechanisms in the AI agent era. Historically, social API pricing has oscillated between openness and control, and X refines cost distribution through a pay-per-use model, lowering the barrier for individual developers to stimulate ecological innovation while pushing high-frequency growth actions to a higher payment tier, avoiding the consumption of free or low-cost resources by capital-intensive automation, thus forming a sustainable capital recovery path.
From a global financial and power structure perspective, this dynamic strengthens the platform's pricing power over user-generated content and interaction data. After AI tools lower construction costs, the barrier to data access directly impacts value capture: low-cost owned reads promote distributed application innovation and user-side productivity enhancement, while high-cost writes constrain large-scale manipulation, long-term driving wealth redistribution from pure traffic harvesting to real interactions and tool ecosystems, rather than mere fluctuations in platform revenue.