On May 31, 2026, the promotional API discount for DeepSeek V4 Pro expires. But DeepSeek has announced it will not restore the original price. The 75% discount is hardening into the permanent list price. If your service's monthly API bill was 1 million won (about $720), the same usage will now cost 250,000 won (about $180). That number is not hypothetical. It shows up on the June invoice.
On Hacker News, the developer community, the announcement drew 244 points and 145 comments. What's striking is that many of those comments weren't simply celebrating the discount — they were from people who had already started redesigning their service architectures around this price. The baseline for AI API costs has, in fact, dropped yet again.
The Promotion Ended — and the Price Didn't Go Back Up
DeepSeek V4 Pro's API discount was announced from the start as a temporary measure, with the expiration clearly stated: May 31, 2026, at 15:59 UTC. Most users expected prices to snap back after that point.
In mid-May, DeepSeek announced a different decision through its official documentation and social channels: even after the promotion ends, the quarter-level pricing will be officially confirmed as permanent policy pricing.
Put that number next to the other major models and its position becomes clear. As of May 2026, OpenAI's GPT-4o costs $5 per million input tokens and $15 per million output tokens. Anthropic's Claude Sonnet line runs around $3 for input and $15 for output. Google's Gemini 1.5 Pro offers a free tier up to a certain usage level, with paid pricing beyond it. In a direct comparison, DeepSeek V4 Pro's permanently fixed price is dramatically lower than any of them.
On performance, DeepSeek V4 Pro posts numbers that compete with the top models from OpenAI and Anthropic on public benchmarks for coding, math, and complex reasoning. The price has come down at the same time the benchmarks sit near the top of the rankings.
Behind DeepSeek's ability to sustain this price is a difference in architectural design. When DeepSeek R1 was released in early 2025, claims circulated that its training cost was a few dozen times lower than OpenAI's. The exact figures were never independently verified. What has stayed consistent, though, is a strategy of building price competitiveness on a low-cost inference structure. With Google, Amazon, and Meta all steadily cutting prices on their own AI services, DeepSeek's decision can be read as a move to lock in the floor price first in the competitive landscape.
Pull in API users with a low price, and once those users start designing their services around your platform, the structure makes it hard for them to leave even if prices rise later. That is why this price lock is more than a simple promotion extension.
The Concerns Raised in the Face of a Low Price
There are reasons this change is hard to welcome unreservedly.
DeepSeek is headquartered in China. There is not enough transparency about which servers the data sent through its API passes through, or how it is processed. Some public institutions in Europe and some companies in the US have already restricted DeepSeek internally or issued guidance discouraging its use. How this API can be applied under Korea's Personal Information Protection Act and the data-handling rules governing the finance and healthcare sectors is a question that requires separate legal review.
The pattern of capturing a market on price and then changing the terms later has played out repeatedly in the cloud industry. AWS attracted startups early on with low prices and free tiers, then adjusted pricing in stages once dependence on the platform had deepened. When a service deeply tied to a cloud provider faces a price hike, switching costs make it hard to move. There is no guarantee, at this point, that DeepSeek won't follow the same path.
Service availability also needs verification. During DeepSeek's period of explosive growth in 2025, there were episodes where the API became unstable under surging traffic. Adopting it without directly testing real-world latency and availability in a production environment is its own separate risk.
These concerns don't apply with equal weight in every situation. The size of the risk depends on the nature of your service's data and its regulatory environment. Skipping the evaluation just because concerns exist, and adopting without verification just because the price is low, each create costs in their own way.
Now Is the Time to Pull Out Your Invoices
The people for whom this price change actually matters are those already using AI APIs in real work. If you're a solo entrepreneur, a solo product manager, or a middle manager driving AI adoption, there are things to check now.
Start by figuring out which APIs you're using, and at what volume. More often than you'd expect, these costs are lumped together as a "ChatGPT subscription" or "AI tool expenses." Without knowing, line by line, which model you're using for which purpose, it's hard to explore alternatives. Pull out the last three months of invoices and break them down item by item — that's the starting point. If, for example, you're handling customer email summaries and social media content drafts with the same model, you can first calculate what changes when repetitive, less complex work like draft writing is split off to a cheaper model.
Not every task needs the highest-performing model. Teams have reported cutting costs by 30–60% by routing repetitive work — simple classification, keyword extraction, draft summaries — to cheaper models, and reserving expensive models for complex reasoning or final customer-facing output. With costs down, now is also the moment to redesign that structure from scratch.
To decide whether to adopt DeepSeek, first sort out the nature of the data you process. Internal workflow automation, content generation based on public information, and processing that involves no personal data carry relatively low risk. If you handle customer personal data directly, are contractually required to guarantee where data is processed, or operate in a heavily regulated industry like finance or healthcare, legal review has to come first.
When smartphones first went mainstream, the teams that explored the market and designed services during the first two or three years after the app stores opened held the advantage in the competition that followed. Similar results repeated when cloud services became an option for startups. The people who pulled ahead in those moments had something in common: accumulated experience concretely experimenting with how a new environment connected to the way they worked. Simply knowing how to use a technology, and judging which technology to connect at which moment and how, are entirely different capabilities.
The fact that AI API costs have hardened at a quarter of what they were is a signal that the window for making that judgment has opened again. If you're working right now without a grasp of your own tool cost structure, that blind spot is quietly draining money from your invoice every month.
