On May 31, 2026, DeepSeek's promotional API discount for V4 Pro expires. But DeepSeek has announced that it won't be rolling prices back. The 75% discount is being frozen in place as the permanent, official price. For a service that had been paying ₩1 million a month in API charges, the same usage will now cost ₩250,000. This isn't a hypothetical. It shows up on the first June invoice.
On Hacker News, the developer community, the announcement pulled 244 points and 145 comments. What's striking is that much of the discussion wasn't simply celebrating the discount—it was developers beginning to redesign their own service architectures around the new price. In practice, the baseline cost of AI APIs has dropped another notch.
The promotion ended, but the price didn't go up
DeepSeek's API price cut for V4 Pro was billed from the start as a temporary measure. The expiration was spelled out precisely: 15:59 UTC on May 31, 2026. Most users assumed prices would snap back to normal after that point.
In mid-May, DeepSeek announced something different through its official documentation and social channels: even after the promotion ended, the quarter-price level would be locked in as the permanent list price.
Line that number up against the other major models and its position becomes clear. As of May 2026, OpenAI's GPT-4o runs $5 per million input tokens and $15 per million output tokens. Anthropic's Claude Sonnet family sits around $3 input and $15 output. Google's Gemini 1.5 Pro offers a free tier up to a certain volume and charges beyond it. In a head-to-head comparison, DeepSeek V4 Pro's permanent fixed price is markedly lower than any of them.
On performance, DeepSeek V4 Pro is posting numbers that compete with the top-tier models from OpenAI and Anthropic on public benchmarks for coding, math, and complex reasoning. The price has come down at the same moment the performance benchmarks sit near the top.
Behind DeepSeek's ability to hold this price is a difference in architectural design. When DeepSeek R1 was released in early 2025, the company claimed its training costs were a fraction—tens of times lower—than OpenAI's. The exact figures were never independently verified. But the strategy of building price competitiveness on a low inference-cost structure has been consistent throughout. With Google, Amazon, and Meta all continually lowering the prices of their own AI services, DeepSeek's move can be read as a bid to lock in the floor price first.
By drawing in API users on the strength of a low price—and once those users start designing their services around the platform—DeepSeek creates a structure where customers find it hard to leave even if prices rise later. That's why fixing the price is more than just extending a promotion.
The concerns that come with a low price
There are reasons this change is hard to embrace without reservation.
DeepSeek is a company headquartered in China. There isn't enough transparency about which servers the data passing through its API travels through, or how that data is handled. Some public institutions in Europe and some companies in the United States 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 financial and medical sectors is a matter that requires separate legal review.
The pattern of lowering prices to capture a market and then changing the terms afterward has repeated throughout the cloud industry. AWS courted startups in its early days with low pricing and free tiers, then adjusted prices in stages once dependence on the platform had grown. When a service deeply reliant on the cloud faces a price increase, switching costs make it hard to move. At this point, there's no guarantee that DeepSeek won't follow the same path.
Service availability is another thing to check. During DeepSeek's period of explosive growth in 2025, there were cases where the API became unstable under traffic surges. Adopting it without directly testing real latency and availability in a production environment is a risk in its own right.
These concerns don't carry equal weight in every situation. The size of the risk shifts with the nature of a service's data and its regulatory environment. Skipping a review simply because concerns exist, and adopting without verification simply because the price is low, each generate costs in their own different ways.
It's time to pull out the invoice
The people for whom this price change actually matters are those already wiring AI APIs into their day-to-day work. If you're a solo operator in Korea, a solo PM, or a middle manager driving an AI rollout, there are things to check right now.
Start by figuring out which APIs you're using and at what volume. More often than you'd think, this gets lumped together as a "ChatGPT subscription" or "AI tool costs." Without an itemized view of which model is doing which job, it's hard to explore alternatives. Pulling out the last three months of invoices and breaking them down line by line is the starting point. If, say, you're handling both customer-email summaries and social-media draft writing with the same model, you can first calculate the cost impact of splitting the repetitive, less complex work—like drafting—onto a cheaper model.
Not every task needs the highest-performing model. Teams have reported cutting costs 30–60% by routing repetitive tasks—simple classification, keyword extraction, draft summaries—to cheaper models, and reserving the expensive ones for complex reasoning or customer-facing final output. Now that costs have come down is also the moment to redesign this structure from scratch.
To decide whether to adopt DeepSeek, you first have to sort out the nature of the data you handle. Internal workflow automation, content generation based on public information, processing tasks that contain no personal data—these carry relatively low risk. If you deal directly with customers' personal data, are contractually obligated to guarantee where data is processed, or operate in a heavily regulated sector like finance or medicine, legal review has to come first.
When smartphones first went mainstream, the teams that explored the market and designed services in the first two or three years after the app store opened took a favorable position in the competition that followed. The same outcome repeated when cloud services became an option for startups. The people who got ahead in these moments had something in common: they had accumulated hands-on experience experimenting with exactly how a new environment connected to their own way of working. Knowing how to use a technology and judging which technology to connect, when, and how are entirely different capabilities.
The fact that AI API costs have settled at a quarter of what they were is a signal that the window to make that judgment has opened again. If you're working without a grasp of your own tooling cost structure right now, that not-knowing is quietly slipping out of your invoice every month.
