To be included in AIPI, a SKU must meet all of the following:

The following pricing types are excluded from AIPI:

Vendors publish prices in different formats, units, and currencies. AIPI normalizes all prices to enable direct comparison.

Currency conversion: For CNY pricing, ATOM uses a fixed annual exchange rate (set January 1st, held for 12 months) rather than daily spot rates. This eliminates foreign exchange volatility from price trend analysis, isolating genuine AI pricing movements from currency fluctuations. Chinese vendors operate in a relatively stable pricing environment, and removing FX noise enables clearer analysis of strategic pricing decisions. All other currencies use daily spot rates as their pricing stability is well-established. The database stores both original and normalized prices for audit purposes.

AIPI indexes use a chained matched-model methodology to isolate actual vendor price movements from changes in the composition of tracked models. This approach is standard practice in financial index construction (comparable to CPI matched-model methodology) and prevents model additions or removals from creating false price signals.

The problem it solves: In a rapidly evolving market, vendors frequently add new models and retire old ones. A simple average would shift whenever the model mix changes, even if no vendor actually changed a price. For example, if three expensive models are delisted in a given week, a simple average would show a price decline that never occurred. The matched-model approach eliminates this composition bias entirely.

How it works:

Why unweighted: Within each matched set, all vendors and models are weighted equally. This prevents market dominance by high-volume providers from skewing the index and ensures pricing trends from emerging vendors are visible alongside established players. The unweighted approach provides a balanced representation of the full pricing spectrum available to buyers.

Each index is calculated separately for three pricing directions:

The AIPI Volatility Composite measures week-over-week pricing stability across AI inference markets. Unlike the price indexes that track absolute levels, this composite tracks the percentage of models that changed price from one week to the next.

Calculation methodology: For each pricing direction (Input, Cached Input, Output), the system identifies models present in both the current and prior week (the matched set), then counts how many of those models changed price. The volatility percentage is calculated as:

Key features:

Investment implications: Volatility levels above 2% historically coincide with major market events such as new flagship model launches, competitive repricing waves, or capacity constraints. The composite serves as an early warning system for pricing instability that may impact infrastructure budgets, vendor negotiations, or portfolio company cost structures. Institutional buyers use volatility trends to time vendor contract negotiations and assess competitive dynamics.

Benchmark context: Enterprise B2B API providers (Stripe, AWS, Twilio) typically maintain 99.5%+ weekly stability (under 0.5% volatility). AI inference markets show structurally higher volatility due to rapid innovation, competitive dynamics, and capacity optimization. The AIPI Volatility Composite provides a quantitative measure of this market characteristic, enabling investors and operators to monitor pricing stability trends over time.

Data freshness: Every SKU carries a Last Verified Date, the date when pricing was last confirmed against the vendor's published page. When a vendor updates pricing, ATOM's automated pipelines detect the change and update the database within 24 hours.

AIPI is deliberately optimized for investment-grade pricing intelligence. The following capabilities define what AIPI is designed to deliver:

AIPI indexes measure price only. The following dimensions are explicitly not covered, as they require different methodologies and data sources: