Also known as: Dynamic pricing, algorithmic pricing
Online retailers and platforms automatically adjust prices to demand, costs, capacity and competitors' prices.
Understand the use case
Airbnb and online fashion retailer Myntra report using machine learning pricing models in their own technical publications.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
System design and data flows
Demand, inventory, costs, capacity and competitors' prices arrive continuously.
Competitors' prices and demand signals originate externally and can be deliberately manipulated. Pricing based on individual profiles or behaviour processes personal data; consumer organisations found this to be rare.
A model estimates demand at different prices and selects the price that best improves the target metric.
The system processes demand, availability and other permitted factors to derive suggested prices. Business rules can set price limits and validity periods. Only transfer to the sales system turns a model calculation into an actual offered price.
The system sets or suggests the price.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Customers pay the current price, and sales feed back into the model.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
8 review first8 clarify8 baseline threats
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
None yet. Each change will appear here.
In this system design: 8 to review first, 8 to clarify, 8 baseline threats.
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
Baseline for every AI system
Third-party content: Present
AI model: Part of every AI system
Review first
Baseline for every AI system
Provider service: Depends on your system
To clarify
Training and feedback: Present
Review first
Result: Part of every AI system
Review first
Business application or equipment: Depends on your system
To clarify
person: Part of every AI system
Baseline for every AI system
Affected person: Depends on your system
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
Only predicts demand and does not set prices
Determines the order in which offers appear, rather than their prices
Competitors' prices and demand signals originate externally and can be deliberately manipulated. Pricing based on individual profiles or behaviour processes personal data; consumer organisations found this to be rare.
The model processes the inputs. Whether it runs internally or with a service provider must be established for the specific implementation.
The pricing model learns from the deployer's own booking and sales data. Sales at the prices set feed back into the model as new data.
Some systems set shop prices themselves; others only suggest them.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
Relate review candidates to actual data flows: what could happen, and who would be affected?
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
A total of 24 threats from the threat catalogue may be relevant to this use case.
The model learns it can charge particularly high prices in emergencies or when capacity is scarce. Revenue rises, but customers feel exploited and regulators take notice.
A competitor temporarily sets unusual prices or creates artificial demand. The model learns from this and adjusts its own prices in a way that harms the deployer.
Calculated prices go straight into the shop without review. An input error then creates prices far below cost or far above market levels that take effect immediately.
Targeted Poisoning / Label Poisoning
Falsified training labels can deliberately shift model outputs.
Listed because of this characteristic
Training data or an adopted base model can introduce hidden triggers for misbehaviour.
Listed because of this characteristic
Targeted queries can recover some personal or protected training data from the model.
Listed because of this characteristic
Sensitive Information Disclosure
Confidential content can appear in answers, supplied context or logs.
Listed because of this characteristic
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
Up to 8 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. One of these is particularly important here and is therefore listed above.
Each Yes adds further threats to your checklist, up to 8 in total. If you cannot yet answer a question, include its threats in your review.
How your system is built
How you operate the system
Some systems set shop prices themselves; others only suggest them.
If yes, add
These questions arise for almost every AI system.
If yes, add
If yes, add
If yes, add
If yes, add
If yes, add
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Controls for the remaining threats are available on their pages in the threat catalogue.
For every AI system Applies since 2 February 2025.
Depending on use Review trigger since 2 February 2025.
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Detailed obligations
Applies if the system deliberately sets prices to exploit individuals' distress or vulnerability in a way that risks significant harm.
Applies since 2 February 2025.
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Review these threats for every AI system, whatever its purpose.
If any of these features does apply to your system, add the listed threats.
Does the system generate its own text, such as answers, drafts or summaries?
If yes, add
Does the system generate or edit images, sounds, voices or videos?
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
If yes, add
Can users give the system natural language tasks that it then performs?
If yes, add
Does the system search its own collection of documents or records for relevant content?
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Can people outside your organisation use the system, including without login?
If yes, add
Does the system process several input types together, such as text with images or speech?
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Does the system execute programs, scripts or commands itself?
If yes, add
Ye et al., Customized Regression Model for Airbnb Dynamic Pricing, KDD 2018 According to the deployer
Airbnb's researchers state that the model described powers Price Tips and Smart Pricing in production.
Kedia, Jain and Sharma (Myntra Designs), Price Optimization in Fashion E-commerce (arXiv 2007.05216) According to the deployer
Myntra combines demand forecasts at each discount level with optimisation across its assortment and reports using the method, validated through A/B tests.
A study of 16 German online retailers and Amazon found substantial price changes over time but little personalisation for individual customers.
The Federal Cartel Office describes how competition law applies to algorithmic pricing.
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured