# DeepSeek Is Free Now but What’s the Catch? **URL:** \ **Category:** Content Creators\ **Tags:** kai, machine-learning, deep-seek, tech\ **Created:** [24 October 2026 15:30 UTC](https://www.artofsm.art/t/deepseek-is-free-now-but-what-s-the-catch/26801 "2026-10-24T15:30:05Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [24 October 2026 15:30 UTC](https://www.artofsm.art/t/deepseek-is-free-now-but-what-s-the-catch/26801/1 "2026-10-24T15:30:05Z")
[![](https://www.artofsm.art/uploads/default/original/3X/4/a/4a9d51d5a4ed476d7b130d8c820102cc8399c5ca.jpeg "DeepSeek Is Free Now but What’s the Catch?") ](https://www.youtube.com/watch?v=zIWxzkO1nak) The video explains that DeepSeek’s seemingly low-cost, high-volume AI service relies heavily on an efficient KV cache system that drastically reduces GPU computation by serving most tokens from precomputed data, debunking misconceptions about massive profits from token processing alone. It also highlights that while DeepSeek has open-sourced parts of their technology, critical components remain proprietary, making self-hosting complex and emphasizing the importance of understanding cache management and infrastructure when evaluating AI service costs. ---
**Author:** ![artesia](https://www.artofsm.art/user_avatar/www.artofsm.art/artesia/32/36_2.png) [@artesia](https://www.artofsm.art/u/artesia)\ **Post date:** [24 October 2026 15:50 UTC](https://www.artofsm.art/t/deepseek-is-free-now-but-what-s-the-catch/26801/3 "2026-10-24T15:50:31Z")
The video begins with the creator reflecting on the high costs of AI API usage, a common frustration among developers and founders. The recent release from DeepSeek sparked widespread excitement and misconceptions online, with many believing the company was generating massive profits due to low operational costs. However, the creator points out that this narrative is incomplete and misleading. DeepSeek’s update, V4.1/1017, is a large but efficient model that uses a mixture of experts approach, activating only a fraction of its parameters per token. Alongside this, DeepSeek launched a search engine API with a free tier ending soon, open-sourced their inference engine, and faced a federal ban on civilian computers, all while reportedly closing a massive funding round. The core misunderstanding revolves around DeepSeek’s claim of serving 155 trillion tokens daily at $38 per GPU per hour, which initially suggested enormous profits given GPU rental costs. However, the creator breaks down the math, revealing that serving such a volume would require tens of thousands of GPUs, far exceeding DeepSeek’s actual reported revenue and GPU usage. The key to this discrepancy lies in the KV cache system, which stores precomputed token data to avoid redundant processing. This cache dramatically reduces the computational load by allowing GPUs to process only new tokens, while most tokens are served from the cache at a fraction of the cost. DeepSeek’s KV cache hit rate has improved from 56.3% in early 2025 to 96% recently, meaning GPUs now compute far fewer tokens from scratch. This efficiency is central to their cost structure, with cached tokens costing significantly less than newly computed ones. The company’s infrastructure includes massive SSD storage to hold this cache, enabling long context retention and efficient multi-turn conversations. The revenue breakdown shows that output tokens, though few, generate nearly as much income as the vast majority of cached tokens combined, highlighting the economic importance of the cache system. Despite open-sourcing their inference engine and model weights, DeepSeek has withheld critical components like the distributed KV cache system and load balancing software, which are tightly integrated with their internal infrastructure. This means the released code supports only offline, single-turn inference and cannot replicate the full efficiency of their live service. The open-source release serves more as a distribution strategy to encourage adoption of their models rather than a full transparency move. Other providers offer similar models and pricing, but differences in cache retention and infrastructure lead to significant cost variations for users. The video concludes with practical advice for AI users: the real cost of using models like DeepSeek’s depends heavily on cache hit rates and how well providers manage cache persistence across machines. Self-hosting is complex and expensive due to hardware and software requirements, especially for maintaining the cache system. Users should scrutinize providers’ cache policies and infrastructure rather than just price per token. The creator emphasizes that understanding these nuances transforms the perception of AI billing from confusion to clarity, enabling smarter optimization of costs in AI development and usage.