AI Token Inflation: Can Owning Your Own Infrastructure Protect Your Business?

Partly. Owning AI infrastructure such as an NVIDIA DGX Station fixes your hardware cost and protects you from vendor price changes, model retirements and spending caps. It doesn't protect you from electricity, staffing or the quality gap between open-weight and frontier models. The record also shows that "token inflation" isn't one-directional. Per-token prices for a given level of capability have mostly fallen since 2023. What has grown is the volatility: scheduled price doublings, new tokenizers that produce more tokens for the same text, usage-based plans replacing flat fees, and agent workloads that consume far more tokens than chat. Google's own pricing page shows Gemini 4 Argon going from US$2/US$10 per million input/output tokens to US$4/US$20 on 1 Dec 2026, and Gemini 3.8 Flash and 3.6 Flash doubling on 1 Jan 2027.

Have AI prices been going up or down?

Both, depending on what you measure. This timeline uses only dated, published changes:

Sources: OpenAI announcements (14 Mar 2023, 6 Aug 2024), Simon Willison (10 Jun 2025), TechRepublic (Jan 2025), Google Developers Blog (17 Jun 2025), TechCrunch (28 Jul 2025), Anthropic (24 Nov 2025) and current pricing pages, claude.com/pricing, RCPmag (9 Dec 2025), and the Google Gemini API pricing page (as listed on 5 Nov 2026). Prices are list prices in US$ per million input/output tokens or per user per month.

Why can your AI bill rise even when token prices fall?

Several forces push spending up independently of the headline rate:

• Scheduled increases. An application built on Gemini 4 Argon that processes 500 million input and 100 million output tokens a month costs US$2,000 a month at today's rate and US$4,000 from 1 Dec 2026. That is an extra US$24,000 a year, about S$30,658 at USD/SGD 1.2774, for the same work.
• Tokenizer changes. Anthropic's pricing documentation says Claude 4.7 and later models use a newer tokenizer that produces approximately 30% more tokens for the same text. An unchanged per-token price can still mean a higher cost per document.
• Reasoning tokens. OpenAI, Anthropic and Google all bill thinking tokens as output tokens, and output is the expensive side. Anthropic charges for the full thinking process even when only a summary is shown.
• Agents. An agent that calls tools in a loop resends the growing context on every step. We cover this in The Hidden Cost of AI Agents.
• Plan changes. Flat-fee plans are moving to metered usage, as with Claude Enterprise's switch to per-seat plus usage at API rates and the monthly API credit pools on some Claude plans.

The risk isn't simply that AI gets more expensive. It is that you can't predict next year's bill, and you don't control the terms.

What else can change without your agreement?

Price is only one lever a provider controls. Models retire on the vendor's schedule. OpenAI's deprecation page lists GPT-4o and GPT-4.1 for API shutdown on 14 Jan 2027, and Anthropic retired Claude 3.5 Sonnet about 11 weeks after deprecating it (13 Aug to 28 Oct 2025). Workflows tuned to a specific model have to be re-tested on its successor. Spending caps can pause your service: Anthropic's API tiers cap monthly spend, and when a cap is reached, usage pauses until the start of the next month. Outages happen too. OpenAI's 10 Jun 2025 disruption lasted more than ten hours, and Anthropic's 2 Mar 2026 incident ran for at least seven and a half. Our post When a Global Cloud Service Goes Down, Does Your Office Stop Working? covers the operational side.

What does owning the infrastructure actually protect?

A DGX Station GB300 such as the ASUS ExpertCenter Pro ET900N G3 or Supermicro ARS-511GD-NB-LCC (price on request at SourceIT) converts a variable token bill into a fixed hardware cost. Once bought, the open-weight model you run doesn't retire unless you choose to replace it, a vendor can't double its price, and no spending cap applies. Measured throughput is high enough for serious shared use. StorageReview recorded 9,280 output tokens per second with gpt-oss-120b at 128 concurrent requests on the ASUS ET900N G3.

What ownership does not protect:

• Electricity. Singapore's regulated tariff moves every quarter. SP Group's rate fell from 31.91¢ to 28.59¢ per kWh (before GST) between Q3 and Q4 2026, a 10.4% cut. Future quarters can go either way. At the Q4 rate with GST, a DGX Station drawing its full 1.6 kW around the clock would cost about S$4,367 a year, which is an upper bound.
• Staff. Someone has to patch, monitor and update the system. See You Bought a S$150,000 AI Supercomputer. Now Who's Going to Manage It?
• Quality. Open-weight models trail the frontier. On Artificial Analysis's Intelligence Index (v5), the best open-weights model scores 33 against 55 to 60 for leading closed models. If a task genuinely needs frontier quality, local hardware doesn't remove that dependency.
• Hardware prices. Your purchase price is locked in, but so is your technology. Rented GPU prices can fall too; AWS cut its H100 (P5) on-demand price by 44% in June 2025.

How can a business hedge without betting everything on one route?

1. Keep workloads portable. Serve local models through an OpenAI-compatible API such as vLLM or NVIDIA NIM, and put an internal gateway such as LiteLLM in front. Switching a workload between a commercial API and your own server then becomes a configuration change.
2. Put the predictable volume on owned capacity. Batch jobs, internal search and high-volume classification are steady and measurable. They suit fixed-cost hardware; see The 24/7 AI Factory.
3. Buy frontier quality where it pays. Use commercial APIs for the tasks where quality clearly matters, and track the cost per task, not just per token.
4. Read the pricing pages every quarter. Scheduled changes are announced in advance, as Google's Gemini changes show.

For the break-even maths, see How Many Tokens Must a DGX Station Generate Before It Pays for Itself? and Buy DGX Station or Keep Paying for Cloud GPUs? For the broader question of why IT prices move, Why IT Prices Change Overnight explains the hardware side.

Buying in Singapore

SourceIT lists the five DGX Station GB300 systems (ASUS ExpertCenter Pro ET900N G3, HP ZGX Fury G1n, MSI XpertStation WS300, GIGABYTE W775-V10-L01 and Supermicro ARS-511GD-NB-LCC), all price on request. For a smaller hedge, a GB10 system such as the NVIDIA DGX Spark 4TB is S$9,300 (listed 5 Nov 2026). Browse both in our NVIDIA DGX Spark & DGX Station collection. For a quotation, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Are AI API prices going up? Some are. Google's pricing page shows Gemini 4 Argon doubling from US$2/US$10 to US$4/US$20 per million input/output tokens on 1 Dec 2026, and Gemini 3.8 Flash and 3.6 Flash doubling on 1 Jan 2027. Other prices have fallen, such as OpenAI's 80% cut to o3 in June 2025.

Why would the same work cost more on a newer model? Partly because of tokenizers. Anthropic says Claude 4.7 and later produce about 30% more tokens for the same text. Reasoning tokens, billed as output, add to the bill as well.

Does owning a DGX Station freeze my AI costs? It freezes the hardware cost and removes vendor pricing and retirement risk for the models you run. Electricity, staff time and any commercial APIs you still use can all change.

Can local models fully replace commercial APIs? For many routine tasks, yes. For tasks that need frontier reasoning or the newest features, closed models still lead on public benchmarks, so most businesses keep some API use.

How do I avoid lock-in either way? Use OpenAI-compatible interfaces, route requests through an internal gateway, and keep prompts and evaluations portable, so a workload can move between providers or your own hardware without being rewritten.