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Pricing
2 posts about Pricing. Every summary links to the original.
Introducing Always-On pricing: automatic savings for Databricks Lakebase
Databricks introduces Always-On pricing for Lakebase, offering a lower rate for baseline compute while retaining elastic autoscaling. On any Lakebase Postgres Autoscaling project, operators turn off scale-to-zero and set an autoscaling range; the minimum capacity becomes the baseline, which receives the lower rate after 24 hours of continuous use. Capacity above the minimum continues to autoscale up to the configured maximum and is billed at standard Autoscaling rates, while users can re-enable scale-to-zero later. The pricing targets established workloads with a consistent activity floor; intermittent or new workloads can retain scale-to-zero to avoid paying for idle hours and uncertain baseline estimates. The announcement says the baseline price is 25% lower, and an additional 50% promotional discount runs through January 31, 2027.
Kunal Kande, Mike JeromeRetail markdown optimization: from reactive markdowns to proactive
Retail markdown optimization addresses the gap between changing demand signals and delayed merchandising action. The post defines it as strategically reducing prices on slow-moving or end-of-life inventory, using demand forecasts, sell-through rates, weeks of supply, and price-elasticity models to choose which SKUs to mark down, when to start, how deep to discount, and where to act. It presents Databricks Genie as a natural-language interface across e-commerce, store, and wholesale data, with supplier, margin, and historical-pattern context; one example is Coop’s Microsoft Teams assistant, AskCap, which reported a 30% retention rate among internal users. Earlier detection of sell-through deceleration can give merchants more time to protect margin, adjust open-to-buy, and reallocate capital, while Genie supports decisions rather than making them.
Sarah Duffy
