Forecasts flash-sale and seasonal demand spikes per SKU, then triggers infrastructure auto-scaling and CDN pre-warming before the surge arrives — not while checkout is already timing out.
Cadence is in active design with early-access retail partners. The calculator below is modeled on a real deployment — a D2C fashion brand we scaled through a flash sale. The full forecasting platform is being built around our first partners.
Flash sales, viral moments, and seasonal peaks arrive faster than a manual capacity-planning process can react to.
A single hour of checkout downtime during a peak event can cost more than a full month of "normal" traffic.
Most teams solve this by permanently over-provisioning for the worst case — paying peak-capacity prices 365 days a year.
Merchandising forecasts demand; engineering plans capacity — usually with no shared model between the two teams.
Ingests historical sales, campaign calendars, and external signals to project demand spikes before they happen — not react after traffic already climbed.
Connects the demand forecast straight to your cloud auto-scaling groups and CDN edge config — capacity ramps ahead of the spike, not during it.
Surfaces pricing and inventory-allocation suggestions when demand outpaces supply, so merchandising and engineering work off the same signal.
Pushes hot product pages and assets to edge locations ahead of a predicted spike, cutting time-to-first-byte when it matters most.
Own uptime during Black Friday, flash sales, and viral moments, and are tired of manually pre-scaling based on guesswork.
Run frequent flash sales and promotional drops and need infrastructure that scales as fast as their marketing calendar.
Want a single forecast that both merchandising and infrastructure teams plan against, instead of two disconnected guesses.
Modeled on a real deployment — see below for the source case study.
Illustrative estimate using a blended $10/concurrent-user/hour rate, derived from our flash sale case study ($1.8M processed across 45,000 concurrent users in under 4 hours). Your actual revenue-per-user will vary by average order value and conversion rate — this is a planning estimate, not a guarantee.
Case study: a D2C fashion brand expected 6x normal traffic for a flash sale. We deployed a serverless, edge-cached architecture that handled 45,000 concurrent users with zero major incidents, processing roughly $1.8M in transactions in under 4 hours.
Cadence's scaling model is built on a real deployment, not a lab benchmark — the same serverless, edge-cached architecture pattern that processed $1.8M with zero major incidents.
Auto-scaling, CDN edge configuration, and event-driven architecture are what our DevOps practice builds every day — Cadence packages that expertise into a forecasting layer.
Most infra tools ignore demand forecasting; most forecasting tools ignore infrastructure. Cadence is built to connect both conversations.
Become an early access partner and let's scale your next event together.