Pricen gives you several distinct approaches to pricing, and they all solve different problems. Dynamic Pricing lets you set prices from your own business rules: "match this competitor minus 5%", "keep a 30% margin over cost", "price at the market average", and the system applies those rules automatically across selected products.
Price Optimization takes a different route: instead of following rules you define, our AI studies your sales history and calculates the price that best meets a goal you choose, such as maximum profit.
The simplest way to tell them apart: Dynamic Pricing is for merchants who think in rules ("I want to be 3% below competitor Y"), while Price Optimization is for merchants who think in objectives ("I want maximum profit"). With Dynamic Pricing you stay in full control of the pricing logic and the system handles execution at scale. With Price Optimization you set the objective and let the AI decide the price.
The key differences at a glance
Dimension | Dynamic Pricing | Price Optimization |
How prices are set | You define the business rules | The AI finds the mathematically best price |
Best for | You already know your pricing strategy and want to automate it | You want the system to discover the best price |
Competitor data | Central: used for matching and positioning | Can be used as safeguard values |
Role of AI | Optional: a pre-calculated optimal price can be used as one rule | The main engine: it models demand for each product |
Level of control | High: you define the exact logic | Lower: you set the goal and the safeguards, the AI sets the price |
Typical use | "Always be 3% below the cheapest competitor" | "Find the price that maximizes my profit" |
You don't have to choose just one
Many merchants use both. Dynamic Pricing works well for competitive categories where you're tracking rivals, while Price Optimization suits products that have no close competitors to match against such as private label products. Running each where it fits is a common and effective setup.
Which one fits your situation?
The table below maps common goals to the approach we'd recommend.
Your situation | Recommended approach |
You want to match or undercut competitors | Dynamic Pricing: use match competitor or set market value |
You want maximum profit without defining rules | Price Optimization: let the AI find the best price |
You have strict margin requirements and want cost-based pricing | Dynamic Pricing: use set base price with a margin formula |
You sell products with no close competitors | Price Optimization: competitor data isn't relevant |
You want AI-suggested prices with competitive guardrails | Dynamic Pricing: start with set AI optimal price, then add competitor rules |
You have a mix of competitive and proprietary products | Both: Dynamic Pricing for competitive items, Price Optimization for the rest |
You want fully hands-off, automated pricing | Either works. Dynamic Pricing gives more predictable, rule-based results; Price Optimization may shift as demand patterns change |
To learn how a Dynamic Pricing strategy is built and executed, see How Dynamic Pricing Works. For the equivalent on the AI side, see How Price Optimization Works.